IDENTIFYING AN ORIGIN OF ABNORMAL DRIVING BEHAVIOR FOR IMPROVED VEHICLE OPERATION

The disclosure includes embodiments for identifying an origin of abnormal driving behavior for improved vehicle operation. A method includes identifying an abnormal driving behavior of a driver of a vehicle at a time T. The method includes identifying a set of events that occurred within a predetermined time Δt before time T. The method includes executing, by a processor, a cause-and-effect analysis on the set of events to determine one or more events from the set of events that caused the abnormal driving behavior. The method includes executing a strategy to reduce the abnormal driving behavior so that vehicle operation is improved.

BACKGROUND

The specification relates to identifying an origin of abnormal driving behavior for improved vehicle operation.

Drivers of vehicles sometimes drive abnormally for various reasons. For example, the driver may be experiencing from one or more of the following conditions which contributes to their driving their vehicle abnormally: fatigue; poor vision; driving in inclement weather; driving at night; distraction; alcohol consumption; drug use; inexperience; diminished capacity; medical treatment; medical condition; any condition that is related to, or a derivative of, the conditions previously listed; etc.

Sometimes a vehicle is driven abnormally because it needs repair or maintenance. For example, the vehicle may have one or more of the following conditions: a flat tire; incorrect air pressure in a tire; a broken or uncalibrated sensor; low fluid; a fluid leak; a fault with the vehicle breaking system; a fault with the vehicle fuel injection system; a fault with the vehicle power steering system; a fault with a vehicle control system; a fault with a vehicle computer or sensor; a fault with the vehicle electrical system; a loose belt or chain; damage to a body panel or some other portion of the vehicle; an onboard vehicle computer that needs a software update; or any other condition or combination of conditions which adversely affects the operation of the vehicle and is capable of correction by repair of one or more vehicle parts, replacement of one or more vehicle parts, or maintenance to one or more vehicle parts.

Modern vehicles broadcast V2X messages that include digital data describing their locations, speeds, headings, past actions, and future actions, etc. Vehicles that broadcast V2X messages are referred to as “V2X transmitters.” Vehicles that receive the V2X messages are referred to as “V2X receivers.” The digital data that is included in the V2X messages can be used for various purposes including, for example, the proper operation of Advanced Driver Assistance Systems (ADAS systems) or autonomous driving systems which are included in the V2X receivers.

Modern vehicles include ADAS systems or automated driving systems. An automated driving system is a collection of ADAS systems which provides sufficient driver assistance that a vehicle is autonomous. ADAS systems and automated driving systems are referred to as “vehicle control systems.” Other types of vehicle control systems are possible. A vehicle control system includes code and routines, and optionally hardware, that are operable to control the operation of some or all of the systems of a vehicle.

A particular vehicle that includes these vehicle applications is referred to herein as an “ego vehicle” and other vehicles in the vicinity of the ego vehicle are referred to as “remote connected vehicles.”

SUMMARY

A problem is that some of the vehicles that operate on the roadways display abnormal driving behavior. Existing solutions to this problem focus on detecting abnormal driving behavior and initiating management systems that attempt to minimize the risk caused by the abnormal driving behavior by addressing the harm or risk caused by the abnormal driving behavior; this focus on addressing the harm or risk caused by the abnormal driving behavior is the critical focus of these existing solutions. For example, if a vehicle is swerving outside of their lane of travel, the existing solutions temporarily take control of the steering of the vehicle and recenter the vehicle inside its own lane of travel. Some existing solutions do not take control of the vehicle which is being driven abnormally, and instead provide a notification of the abnormal driving to the driver of the vehicle which may or may not be accompanied by a suggestion for how to correct the problem. Some existing solutions use cellular communications to notify other vehicles about the abnormal driving behavior.

The problem with these existing solutions is that the abnormal driving behavior inevitably returns and introduces avoidable risk into the operating environment of vehicles. This is a potentially fatal mistake. For example, consider the example given above. It is good that the existing solutions are able to detect that a vehicle is swerving outside of its lane of travel and takes steps to center the vehicle in its own lane of travel. However, for some period of time the vehicle is still driving outside of its lane of travel, and this is extremely dangerous. What is needed is an approach that makes it less likely that the vehicle will be driven abnormally in the first place.

Described herein are embodiments of an origin system. The origin system is different from the existing solutions for various reasons. For example, the origin system does not focus on addressing the harm or risk caused by the abnormal driving behavior is the critical focus of these existing solutions; instead, the origin system focuses on determining the origin, or root cause, of a driver's abnormal driving behavior and then takes steps to ensure that this root cause is eliminated from the driver's driving experience in the future. In this way, the origin system beneficially makes it less likely that vehicles are driven abnormally repeatedly in the same way for the same reasons. A non-limiting example is now provided.

For example, a driver of a vehicle swerves outside of their lane of travel whenever they receive a phone call on their cellular phone. The origin system described herein determines that the driver is swerving outside of their lane of travel and that the origin of this abnormal driving behavior is that the driver swerves outside of their lane of travel whenever they receive a phone call while operating their vehicle. The origin system therefore takes steps to ensure that the driver is not able to receive phone calls in the future. For example, the driver's cellular phone has a client stored therein which operates in cooperation with the origin system. The origin system communicates with this client to cause the cellular phone to implement a strategy such as one or more of the following: turn off its communication radios whenever the vehicle is traveling; place the phone in “do not disturb” mode so that the driver is not made aware of the phone call which is then redirected to voicemail; place the phone is “silent” mode so that the phone does not provide the driver with a notification of the phone call; redirect all received phone calls to voicemail and provide the driver with a silent notification of the phone call; and any other strategy which achieves the result of the driver being unable to receive the phone call while they are driving the vehicle. The origin system then updates the profile data of the driver with profile update data that describes, among other things, the strategy which is implemented to correct/remove the abnormal driving behavior. The profile data is stored in a database or some other data structure so that the strategy is implemented by the origin system of this vehicle, and/or other vehicles having an origin system, in the future. In this way the origin system beneficially determines the origin of a driver's abnormal behavior and implements a strategy which is operable to remove the origin from the current and future driving experiences of the driver so that the abnormal driving behavior does not occur in the future responsive to the same origin occurring.

In some embodiments, the profile data of the driver includes digital that describes one or more of the following: a unique identifier of the driver; a list of the geographic locations that they have driven at in the past; a list of their past abnormal driving behaviors; a list of the origins of their past abnormal driving behaviors; a list of the strategies which were implemented to remove these abnormal driving behaviors; and a list of the strategies for correcting abnormal driving behavior which has been approved by the driver. An example of the profile data according to some embodiments includes the profile data183depicted inFIG. 1.

The profile update data includes digital data that describes one or more of the following: (1) the origin of the abnormal driving behavior, (2) the abnormal driving behavior itself, and (3) the strategy which is implemented to remove the origin of the abnormal driving behavior. An example of the profile update data according to some embodiments includes the profile update data172depicted inFIG. 1.

In some embodiments, the strategies implemented by the origin system are required to be pre-approved by a human prior to their implementation by the origin system. For example, in some embodiments the strategies are pre-approved by the driver of the vehicle prior to their implementation (e.g., so that their implementation does not frighten, anger, confuse, surprise, or otherwise affect the driver in an unexpected way). In some embodiments, the strategies are pre-approved by an engineer that designed the origin system. In some embodiments, the strategies are pre-approved by the both the driver and the engineer.

In some embodiments, the strategies are selected from a dataset of approved strategies that are designed by the engineer to counteract a known set of abnormal driving behaviors. In this way the strategies are known to be effective at eliminating the abnormal driving behavior. In some embodiments, the strategies are determined based at least in part on the execution of one or more digital twin simulations. Digital twin simulations are described in more detail below. Digital twin data includes any digital data, software, and/or other information that is necessary to execute the digital twin simulations. An example of the digital twin data according to some embodiments includes the digital twin data162depicted inFIG. 1.

In one embodiment, the origin system identifies abnormal driving behavior as one or more of the following: behavior which is within a set of known abnormal driving behavior (e.g., based on real-world or simulated observations of behavior that are result in negative outcomes or impact); behavior which satisfies a threshold for matching an object prior of abnormal driving behavior; and behavior which satisfies a threshold for abnormality. Threshold data includes digital data that describes any of the threshold described herein. An example of the threshold data according to some embodiments includes the threshold data196depicted inFIG. 1. Reference data includes digital data that describes one or more of the known abnormal driving behaviors and the object priors described herein. An example of the reference data according to some embodiments includes the reference data188depicted inFIG. 1.

In some embodiments, the strategies are implemented by a vehicle control system and/or a client which is stored on a processor-based computing device of the driver such as a smartphone, cellular phone, smartwatch, smart glasses, or some other processor-based computing device.

One general aspect includes a method for identifying an origin of abnormal driving behavior for improved vehicle operation. The method also includes identifying an abnormal driving behavior of a driver of a vehicle at a time t; identifying a set of events that occurred within a predetermined time Δt before time t; executing, by a processor, a cause-and-effect analysis on the set of events to determine one or more events from the set of events that caused the abnormal driving behavior; and executing a strategy to reduce the abnormal driving behavior so that vehicle operation is improved. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods.

Implementations may include one or more of the following features. The method where the strategy is selected from a set of pre-approved strategies based on a type of the abnormal driving behavior so that the strategy is customized for the type. The set of pre-approved strategies is pre-approved by the driver of the vehicle. The pre-approval occurs prior to the driver operating the vehicle. The pre-approval is stored in a trip profile which the driver creates each time they operate the vehicle. The pre-approval is stored in a user profile associated with the driver and the user profile is accessible via a plurality of vehicles so that the user profile of the driver is usable by the driver when operating any of the plurality of vehicles. The set of pre-approved strategies is pre-approved by an engineer of the vehicle. The strategy is selected based on the execution of a set of digital twin simulations. The abnormal driving behavior is identified based at least in part on the execution of a set of digital twin simulations. The method is executed by onboard vehicle computers of one or more vehicles that are members of a vehicular micro cloud. The vehicular micro cloud does not include use of cellular communications. The method is executed at least in part by a hardware server. The strategy is executed by a vehicle control system of the vehicle. The strategy is executed by a plurality of vehicle control systems of a plurality of vehicles that are members of a vehicular micro cloud. The vehicular micro cloud is formed responsive to identifying the abnormal driving behavior by the method. Implementations of the described techniques may include hardware, a method or process, or computer software on a computer-accessible medium.

One general aspect includes a system including a non-transitory memory; a vehicle control system; and a processor communicatively coupled to the non-transitory memory and the vehicle control system, where the non-transitory memory stores computer readable code that is operable, when executed by the processor, to cause the processor to execute steps including: identifying an abnormal driving behavior of a driver of a vehicle at a time t; identifying a set of events that occurred within a predetermined time Δt before time t; executing, by a processor, a cause-and-effect analysis on the set of events to determine one or more events from the set of events that caused the abnormal driving behavior; and executing a strategy, by the vehicle control system, to reduce the abnormal driving behavior so that vehicle operation is improved. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods.

Implementations may include one or more of the following features. The system where the strategy is selected from a set of pre-approved strategies based on a type of the abnormal driving behavior so that the strategy is customized for the type. The set of pre-approved strategies is pre-approved by the driver of the vehicle. The pre-approval occurs while to the vehicle is not moving but the driver is operating the vehicle. Implementations of the described techniques may include hardware, a method or process, or computer software on a computer-accessible medium.

One general aspect includes a computer program product including computer code stored on a non-transitory memory that is operable, when executed by an onboard vehicle computer, to cause the onboard vehicle computer to execute processes including: identifying an abnormal driving behavior of a driver of a vehicle at a time T; identifying a set of events that occurred within a predetermined time Δt before time T; executing, by a processor, a cause-and-effect analysis on the set of events to determine one or more events from the set of events that caused the abnormal driving behavior; and executing a strategy to reduce the abnormal driving behavior so that vehicle operation is improved.

DETAILED DESCRIPTION

Described herein are embodiments of an origin system. The functionality of the origin system is now introduced according to some embodiments.

In some embodiments, the origin system includes code and routines that are operable, when executed by a processor, to cause the processor to execute one or more of the following steps: analyzing sensor data to determine that abnormal driving behavior of a vehicle operated by a driver is occurring; determining an origin of the abnormal driving behavior which is determined to be a root cause of the abnormal driving behavior; determining a strategy to implement to remove the origin of the abnormal driving behavior from future driving experiences of the driver (e.g., whether they are operating this vehicle or a different vehicle); implementing the strategy to the current and future driving experiences of the driver so that the origin of the abnormal driving behavior does not occur or is minimized sufficient so that the abnormal driving behavior does not reoccur responsive to this same origin; observing whether the strategy is implemented and satisfies a threshold for removing the abnormal driving behavior; and, responsive to determining that the threshold for removing the abnormal driving behavior is satisfied, updating a profile of the driver to include profile update data that describes one or more of (1) the origin of the abnormal driving behavior, (2) the abnormal driving behavior itself, and (3) the strategy which is implemented to remove the origin of the abnormal driving behavior.

Sensor Data

Vehicles include onboard sensors that constantly record sensor data describing their external environment. In some embodiments, the sensor data is time stamped so that individual sensor measurements recorded by the onboard sensors include a time stamp describing the time when the sensor measurement was recorded. Time data includes digital data that describes the time stamps for the sensor measurements that are described by the sensor data. Vehicles transmit V2X messages to one another. Examples of the time data according to some embodiments include the time data154,155depicted inFIG. 1.

The sensor data includes digital data describing the sensor measurements recorded by the onboard sensors (e.g., the sensor set). These V2X messages include V2X data in their payload. The V2X data includes, among other things, the sensor data they record. Vehicles that receive these V2X messages use this V2X data to improve their awareness of their environment. For vehicles that include Advanced Driver Assistance Systems (ADAS systems) or autonomous driving systems, the V2X data is inputted to these systems so that they can better understand their driving environment when providing their functionality.

An example of one specific type of sensor data includes GPS data. “GPS” refers to “geographic positioning system.” The GPS data includes digital data that describes the geographic location of an object such as a vehicle or a smartphone.

An example of the V2X data according to some embodiments includes the V2X data133depicted inFIG. 1. An example of the sensor data according to some embodiments includes the ego sensor data195and the remote sensor data193depicted inFIG. 1. The ego sensor data195includes sensor data recorded by the sensor set126of the ego vehicle123. The remote sensor data193includes sensor data recorded by the sensor set126of the remote connected vehicle124. For example, with reference toFIG. 1, the remote sensor data193is received by the communication unit145of the ego vehicle123via a V2X transmission that includes V2X data133including the remote sensor data193as its payload; the origin system199then parses the remote sensor data193from the V2X data133and stores the V2X data133and the remote sensor data193in the memory127of the ego vehicle123. The remote sensor data193serves as a source of data, in addition to the ego sensor data195and the reference data188, for identifying the occurrence of abnormal driving behavior by the driver109of the ego vehicle123. In some embodiments, the V2X data133is received by the ego vehicle123because the ego vehicle123and the remote connected vehicle124are members of the same vehicular micro cloud194. Vehicular micro clouds are described in more detail below according to some embodiments.

A vehicle control system is an onboard system of a vehicle that controls the operation of a functionality of the vehicle. ADAS systems and autonomous driving systems are examples of vehicle control systems. Examples of the vehicle control system according to some embodiments includes the vehicle control system153depicted inFIG. 1.

Example General Method

In some embodiments, the origin system includes code and routines that are operable, when executed by a processor, to cause the processor to execute one or more steps of an example general method described herein. The origin system may be an element of one or more of an ego vehicle, a remote connected vehicle, a cloud server, or an edge server installed in a roadway device such as a roadside unit (RSU). As described, the origin system is an element of the ego vehicle, but this description is not intended to be limiting.

In some embodiments, these steps are executed by a processor or onboard vehicle computer of an ego vehicle. The ego vehicle is a connected vehicle. A connected vehicle is a vehicle that includes a communication unit. An example of a communication unit includes the communication unit145depicted inFIG. 1. The remote connected vehicle is also a connected vehicle, and so, it includes a communication unit.

As used herein, the term “wireless message” refers to a V2X message transmitted by a communication unit of a connected vehicle such as a remote connected vehicle or the ego vehicle.

An example of the example general method is now described. In some embodiments, one or more steps of the example general method are skipped. The steps of the example general method may be executed in any order, and not necessarily the order presented. In some embodiments, a plurality of vehicles on a roadway include instances of the origin system and the origin systems of these vehicles also execute some or all of the steps described below. For example, one or more of these steps are executed by the members of a vehicular micro cloud in some embodiments. Vehicular micro clouds are not a requirement of the origin system.

The steps of the example general method are now described according to some embodiments.

Step 1: A driver creates a profile for themselves. The driver includes a human driver of the ego vehicle. An example of a driver according to some embodiments includes the driver109depicted inFIG. 1.

The profile data includes digital data that describes the profile for a driver. The profile describes, among other things, a set of strategies for correcting abnormal driving behavior which have been approved by the driver. For example, the origin system does not implement a strategy to correct the origin of abnormal driving behavior unless the strategy has been pre-approved by the driver, and the profile data includes digital data that describes the strategies which have been pre-approved by the driver. Note that the strategy is selected by the origin system to correct the origin of the abnormal driving behavior, and not the abnormal driving behavior itself.

In some embodiments, these pre-approvals are on a use-by-use or trip-by-trip basis so that they expire after each use of the ego vehicle by the driver or each trip by the driver, respectively.

In some embodiments, the pre-approvals do not expire. In some embodiments, the origin system is configured so that a driver's pre-approvals for strategies follow them from vehicle-to-vehicle. For example, if a driver is operating their personal ego vehicle in California, then the pre-approved strategies apply for each use of their personal ego vehicle. If this same driver travels to Germany and rents a remote connected vehicle which also has an instance of the origin system, the pre-approvals will be valid when the driver uses this rented remote connected vehicle so long as they log on to their account for the origin system via an interface of the rented remote connected vehicle (e.g., the touchscreen display of the infotainment system, a microphone, any other interface).

The profile data is stored in a non-transitory memory. In some embodiments, a vehicle (e.g., ego vehicle123) which includes an instance of the origin system includes a non-transitory memory (e.g., memory127) that stores profile data for one or more drivers that operate the vehicle.

In some embodiments, the profile data for a fleet of vehicles (e.g., the ego vehicle and one or more remote connected vehicles) is stored and/or backed up by a cloud server which manages the profile data for the fleet of vehicles. For example, the profile data is stored in a data structure of a cloud server (and/or the edge server) which is accessible by the fleet of vehicles having their own instances of the origin system.

A data structure includes a non-transitory memory that organizes a set of data such as the system data. An example of the data structure according to some embodiments includes the data structure131depicted inFIG. 1. The system data includes some or all of the digital data described herein. An example of the system data according to some embodiments includes the system data129depicted inFIG. 1.

The cloud server includes a hardware server. An example of the cloud server includes the cloud server103depicted inFIG. 1.

In some embodiments, the data structure is an element of an edge server. An example of the edge server according to some embodiments includes the edge server198depicted inFIG. 1. An edge server includes a hardware server. In some embodiments, the edge server is an element of a roadside device such as a roadside unit.

An example of the profile data according to some embodiments includes the profile data183depicted inFIG. 1.

In some embodiments, the profile data describes a set of strategies that are pre-approved by an engineer of the ego vehicle itself as effective at correcting particular abnormal driving behaviors in particular contexts. In some embodiments, the origin system does not implement a strategy unless it is approved by the both the engineer and the driver that is operating the ego vehicle at the time when the strategy would be implemented. In this way, the origin system ensures that only effective strategies are implemented and that their implementation does not surprise or anger the driver of the ego vehicle.

In some embodiments, approved strategy data includes digital data that describes the pre-approved strategies. An example of the approved strategy data according to some embodiments includes the approved strategy data182depicted inFIG. 1.

The following steps assume that the driver of the ego vehicle has inputted their profile data so that the origin system considers the profile data when implementing strategies to correct the origin of abnormal driving behavior.

Step 2: The origin system causes the sensor set of the ego vehicle to record ego sensor data. The ego sensor data includes digital data that describes the sensor measurements of the sensors that are included in the sensor set of the ego vehicle. In some embodiments, the individual sensor measurements are time stamped so an instance of ego sensor data describes both a sensor measurement and when this measurement was recorded. In some embodiments, the ego sensor data includes time data that describes the timestamps for the sensor measurements.

In some embodiments, the sensor measurements described by the ego sensor data describe one or more of the following: the ego vehicle over time including its location in a roadway environment over time; the location of the ego vehicle relative to other objects within the roadway environment over time; the driver's operation of the ego vehicle over time, the presence of other objects over time within the roadway environment that includes the ego vehicle; the location of these objects in the roadway over time relative to other objects (e.g., the location of these other objects relative to one another and relative to the ego vehicle); and the behavior of these other objects over time (e.g., a remote connected vehicle driving abnormally).

An example of the ego sensor data according to some embodiments includes the ego sensor data195depicted inFIG. 1. An example of the time data associated with the ego sensor data195according to some embodiments includes the time data155depicted inFIG. 1.

The sensors included in the sensor set, and the type of measurements they can record, are described in more detail below.

Step 3: (Optional) A set of one or more remote connected vehicles in sensor range of the ego vehicle include their own instance of the origin system. The origin system of these remote connected vehicles causes the sensor sets of these remote connected vehicles to record sensor measurements of their roadway environment. These sensor measurements include sensor measurements of the ego vehicle and the behavior of the ego vehicle over time.

The sensor measurements recorded by an individual remote connected vehicle from the set of remote connected vehicles is described by remote sensor data. The remote sensor data includes digital data that describes the sensor measurements of the sensors that are included in the sensor set of the remote connected vehicle. In some embodiments, the individual sensor measurements are time stamped so an instance of remote sensor data describes both a sensor measurement and when this measurement was recorded. In some embodiments, the remote sensor data includes time data that describes the timestamps for the sensor measurements.

In some embodiments, the sensor measurements described by the remote sensor data describe one or more of the following: the remote connected vehicle over time including its location in a roadway environment over time; the location of the remote connected vehicle relative to other objects within the roadway environment over time; a driver's operation of the remote connected vehicle over time, the presence of other objects (including the presence of the ego vehicle) over time within the roadway environment that includes the remote connected vehicle; the location of these objects (including the location of the ego vehicle) in the roadway over time relative to other objects (e.g., the location of the ego vehicle relative to the remote connected vehicle as measured from the perspective of the remote connected vehicle); and the behavior of these other objects (including the behavior of the ego vehicle) over time (e.g., abnormal driving behavior of the ego vehicle as recorded by the sensors of the remote connected vehicle as well as an event which preceded the abnormal driving behavior).

An example of the remote sensor data according to some embodiments includes the ego sensor data195depicted inFIG. 1. An example of the time data associated with the remote sensor data193according to some embodiments includes the time data154depicted inFIG. 1.

The sensors included in the sensor sets of the remote connected vehicles are similar to those included in the ego vehicle.

In some embodiments, the ego vehicle and the set of remote connected vehicles described in step 3 are all members of a vehicular micro cloud. In some embodiments, an origin system of a vehicle that includes an origin system (e.g., the ego vehicle) initiates the creation of the vehicular micro cloud responsive to observing one of the vehicles in the roadway environment (e.g., the ego vehicle itself) being driven abnormally based on analysis of its own sensor data (e.g., the ego sensor data). Vehicular micro clouds are described in more detail herein. For example, a description of vehicular micro clouds is provided following the description of the example general method.

Step 4: (Optional) The origin systems of the set of remote connected vehicles described in step 3 build V2X messages including V2X data. V2X data includes digital data that is the payload for a V2X message. An example of the V2X data according to some embodiments includes the V2X data133depicted inFIG. 1. In some embodiments, the origin systems of the set of remote connected vehicles described in step 3 build V2X data133including their remote sensor data193and time data154; these origin systems build V2X messages including the V2X data133as their payloads and cause the communication units of these remote connected vehicles to transmit V2X messages including the V2X messages. Each instance of V2X data133for each remote connected vehicle includes a plurality of instances of remote sensor data193and corresponding time data154for the sensor measurements described by this remote sensor data193. Each of the remote connected vehicles builds its own V2X message including its own V2X data. Each origin system of each remote connected vehicle causes the communication unit of each of the remote connected vehicles to broadcast its own V2X message.

Step 5: (Optional) The V2X messages broadcast at step 4 are received by the communication unit of the ego vehicle. The origin system of the ego vehicle parses the V2X data133from the V2X messages received by the communication unit of the ego vehicle and stores the V2X data133in the memory of the ego vehicle. The origin system of the ego vehicle parses the remote sensor data193and the time data154from these instances of V2X data133and stores the remote sensor data193and the time data154in the memory. In this way the origin system of the ego vehicle receives the remote sensor data193and the time data154from a set of remote connected vehicles. The origin system of the ego vehicle therefore has access to a rich data set including its own ego sensor data195and the remote sensor data193of a set of remote connected vehicles124to consider in the subsequent steps of this example general method.

Step 6: The origin system of the ego vehicle analyzes the available sensor data. This sensor data includes the ego sensor data and, optionally, one or more sets of remote sensor data received at step 5. The ego sensor data and the remote sensor data are referred to separately or collectively as the “sensor data.” The origin system of the ego vehicle analyzes the sensor data relative to the reference data to identify if the ego vehicle is behaving abnormally. Reference data includes digital data that describes one or more of the known abnormal driving behaviors and the object priors described herein. An example of the reference data according to some embodiments includes the reference data188depicted inFIG. 1.

If abnormal behavior is identified by the origin system at step 6, then the example general method proceeds to step 7. If abnormal behavior is not identified by the origin system at step 6, then the example general method restarts at step 1 and proceeds from there until abnormal behavior is identified at step 6. The steps of this example general method subsequent to step 6 assume that the origin system identifies abnormal behavior at step 6.

In some embodiments, this step 6 corresponds to step305in the method300depicted inFIG. 3according to some embodiments.

Step 7: (Optional) In some embodiments, the origin system of the ego vehicle takes step to form a vehicular micro cloud responsive to identifying the abnormal driving behavior at step 6. The ability of the origin system to form a vehicular micro cloud is beneficial, for example, since it provides the origin system of the ego vehicle with access to greater computational resources of a plurality of connected vehicles in order to provide services such as one or more of the following: identifying the origin of the abnormal behavior; selecting a strategy to reduce, minimize, or remove the origin of the abnormal behavior; and implementing the strategy to reduce, minimize, or remove the origin of the abnormal behavior. There are numerous other ways that the formation of a vehicular micro cloud benefits the functionality of the origin system to identify the origin of a driver's abnormal driving behavior and the implementation of a strategy to address the origin of the driver's abnormal driving behavior. Vehicular micro clouds are described in more detail herein. For example, a description of vehicular micro clouds follows the description of this example general method.

Step 8: The origin system analyzes the sensor data and the reference data to determine the time “T” when the ego vehicle began to behave abnormally. For example, the origin system analyzes the sensor data and determines that: (1) a particular instance of sensor data (e.g., sensor data195or remote sensor data193) describes a measurement that, when compared to the reference data188by the origin system, indicates that the ego vehicle is acting abnormally; (2) the particular instance of sensor data is associated with, or includes within in it, time data describing the time “T” when the ego sensor data was recorded; (3) at a set of time prior to “T” the sensor data, when compared to the reference data188by the origin system, indicates that the ego vehicle is not behaving abnormally; and (4) that the ego vehicle began to behave abnormally at time “T” since it is not behaving abnormally based on the sensor data associated with the time data describing the set of time prior to “T.” In this way the origin system is able to analyze the sensor data in view of the reference data to identify the time “T” when the ego vehicle began to behave abnormally. In some embodiments, this time “T” is the same time referred to below in the description of the happens-before relationship analysis. In some embodiments, the set of time prior to “T” is referred to in the description of the happens-before relationship analysis as “Δt.”

In some embodiments, the sensor data associated with time “T” describes a measurement that, when compared to the reference data by the origin system, indicates one or more of driving events, driving actions, and a particular driving behavior which is abnormal. The driving events, driving actions, and particular driving behavior for time “T” are referred to again below with the description of the happens-before relationship analysis.

Similarly, the set of time “Δt” is associated with a set of sensor measurements that, when compared to the reference data by the origin system, indicates one or more of driving events, driving actions, and a particular driving behaviors as indicated by the reference data (e.g., object priors which indicate different events, actions, or patterns of actions, i.e., behaviors). These driving events, driving actions, and particular driving behavior for the set of time “Δt” are referred to again below with the description of the happens-before relationship analysis.

Step 9: The origin system initiates an analysis of the sensor data to determine the origin of the abnormal driving behavior. The analysis data includes digital data that describes this analysis. An example of the analysis data according to some embodiments includes the analysis data181depicted inFIG. 1. The origin data includes digital data that describes the origin identified by this analysis. An example of the origin data according to some embodiments includes the origin data184depicted inFIG. 1.

In some embodiments, the analysis executed by the origin system at step 9 includes one or more of the following: (1) a happens-before relationship analysis; and (2) a cause-and-effect analysis. Each of these individual processes for analysis are described in more detail below under appropriate headers. These descriptions of these processes for analysis are part of the description of the example general method and also applicable in general for other methods executed by the origin system in some embodiments.

Happens-Before Relationship Analysis

In some embodiments, a happens-before relationship analysis includes one or more of the following steps: (1) retrieving the sensor data describing the sensor measurements for time “T”; (2) determining one or more of the driving events, driving actions, and particular driving behaviors [including the abnormal driving behavior] that occurred at time “T” by comparing the sensor data for time “T” to the reference data; (3) retrieving the sensor data describing the sensor measurements for the set of time set of time “Δt”; (4) determining one or more of the driving events, driving actions, and particular driving behaviors that occurred at the set of time “Δt” by comparing the sensor data for the set of time “Δt” to the reference data; (5) chronologically ordering the driving events, driving actions, and a particular driving behaviors for time “T” and the set of time “Δt”; and (6) constructing a happens-before diagram that describes the chronological order of the driving events, driving actions, and particular driving behaviors for time “T” and the set of time “Δt”. Examples of happens-before diagrams according to some embodiments are depicted at the bottom ofFIG. 4and the bottom ofFIG. 6.

In some embodiments, the set of time “Δt” used in the happens-before relationship analysis is not the same set of time “Δt” used to identify the time “T.” Instead, in some embodiments the set of time “Δt” is a predetermined time interval before time “T” that corresponds to the time when the abnormal driving behavior began. In some embodiments, this predetermined time interval is a first set of time programmed into the origin system by an engineer of the origin system. For example, the first set of time “Δt” is 0.1 seconds before T. The origin system analyzes the events that occurred during this first set of time “Δt” to determine the events that occurred during the first set of time “Δt.” These events are described by factor data within the analysis data181. The origin system optionally forms a second set of time “Δt” by increasing the first set of time “Δt” by some interval such as 0.1 seconds so that the second set of time “Δt” is 0.2 seconds before time “T.” The time intervals described here are non-limiting examples; other time intervals are possible. The origin system analyzes the events that occurred during this second set of time “Δt” to determine the events that occurred during the second set of time “Δt.” These events are described by factor data within the analysis data181. This process of increasing the time interval (i.e., the set of time “Δt”) is repeated until a threshold is satisfied.

In some embodiments, the threshold is one which is configured to ensure that the time interval is large enough to include the event or set of events that caused the abnormal driving behavior, i.e., the origin of the abnormal driving behavior.

In some embodiments, the threshold includes one or more of the following: an amount of time that must be covered by the set of time “Δt”; an amount of time that must be covered by the set of time “Δt” where the amount of time is variable based on the type of abnormal driving behavior that occurred at time “T”; a number of events that are described by the factor data included in the analysis data181; an amount of time that must be covered by the set of time “Δt” where the amount of time is determined based on analysis of historical data that describes the amount of time that is appropriate for the type of abnormal driving behavior that occurred at time “T”; a number of events described by the factor data included in the analysis data where the number of events is determined based on analysis historical data that describes the amount of time that is appropriate for the type of abnormal driving behavior that occurred at time “T”; an amount of time that must be covered by the set of time “Δt” where the amount of time is determined based on digital twin simulations of events that simulated the type of abnormal driving behavior that occurred at time “T”; a number of events described by the factor data included in the analysis data where the number of events is determined based on digital twin simulations of events that simulated the type of abnormal driving behavior that occurred at time “T”; an amount of time that must be covered by the set of time “Δt” where the amount of time is determined based on deep learning algorithm analysis of historical data that describes the amount of time that is appropriate for the type of abnormal driving behavior that occurred at time “T”; and a number of events described by the factor data included in the analysis data where the number of events is determined based on deep learning algorithm analysis of historical data that describes the amount of time that is appropriate for the type of abnormal driving behavior that occurred at time “T.”

In some embodiments, the factor data includes digital data that describes one or more driving events, driving actions, and particular driving behaviors [including the abnormal driving behavior] that occurred at particular timestamps described by the time data.

In some embodiments, the historical data includes digital data stored on the memory of the ego vehicle and describing past abnormal driving behaviors by type and the intervals of time prior to time “T” for these abnormal driving behaviors when the event that was the origin of the abnormal driving behavior was found by the origin system. In other words, the historical data describes the lessons learned by the origin system based on its past operation such that the historical data beneficially enables the origin system to improve its accuracy and speed of analysis over time.

The origin system includes code and routines that are operable, when executed by a processor of the ego vehicle, to cause the processor to execute the happens-before relationship analysis.

The happens-before diagram and the happens-before relationship analysis are described by the analysis data.

In some embodiments, a cause-effect analysis includes one or more of the following steps: (1) analyzing the happens-before relationship diagram described by the analysis data by grouping the factor data chronologically in groups of one-by-one (e.g., one factor by one factor), two-by-two (e.g., two factors by two factors), and N-by-N (e.g., N factors by N factors, where “N” is any positive whole number); (2) analyzing the groups of factors in chronological order relative to one another to determine which factors precipitated one another (e.g., determining the cause-and-effect relationships among the factors relative to one another chronologically), including, or in some embodiments with exclusive emphasis on, the factors that precipitated the abnormal driving behavior at time “T”; (3) constructing one or more cause-and-effect diagrams based at least in part on the on the analysis of step 2; (4) analyzing the cause-and-effect diagrams to determine the origin of the abnormal driving behavior diagram; and (5) outputting origin data describing the origin of the abnormal driving behavior. Examples of cause-and-effect diagrams according to some embodiments are depicted inFIGS. 5 and 7.

The origin data includes digital data describing the origin of the abnormal driving behavior. An example of the origin data according to some embodiments includes the origin data184depicted inFIG. 1.

Step 10: Selecting, form the set of pre-approved strategies described by the approved strategy data, a selected strategy to implement by either the ego vehicle or the vehicular micro cloud. The output of this step is the selected strategy data. The selected strategy data includes digital data that describes the strategy selected from the set of pre-approved strategies to minimize, reduce, or eliminate the origin of an abnormal driving behavior identified by the origin system. An example of the selected strategy data according to some embodiments includes the selected strategy data186depicted inFIG. 1.

Step 11: The origin system takes steps to implement the selected strategy for one or more of the current and future driving experiences so that the abnormal driving behavior does not occur for the same reason in the future. In some embodiments, implementing the selected strategy includes causing an electronic display of the ego vehicle to display a graphical user interface (GUI) or some other data that informs the driver that the selected strategy will be implemented. GUI data includes digital data that is outputted by the origin system and causes the electronic display to display the GUI informing the driver about the selected strategy and its implementation. An example of the GUI data according to some embodiments includes the GUI data187depicted inFIG. 1.

Step 12: The origin system updates the profile for the driver to include the profile update data172which is outputted from the above steps. In this way the selected strategy is implemented by the origin system of this ego vehicle and any other vehicle that the driver operates that includes an instance of the origin system therein.

In some embodiments, the origin system of the ego vehicle uses digital twin simulations to provide its functionality. For example, the origin system uses digital twin simulations to identify which strategies will correct which abnormal driving behaviors or which factors are likely to precipitate which types of abnormal driving behavior. In this way the digital twin simulations execute various simulations which attempt to help the origin system to provide its functionality. Digital twin data includes any digital data that is necessary to execute the digital twin simulations and output the origin data. The digital twin data also describes the output of these digital twin simulations (e.g., the digital twin data includes a recommendation for which pre-approved strategy to select to address the origin of the abnormal driving behavior identified by the origin system). An example of the digital twin data according to some embodiments includes the digital twin data162depicted inFIG. 1. Digital twin simulations are described in more detail below.

In some embodiments, the origin system makes similar determinations based on one or more of pattern recognition, time series analysis, and deep learning analysis. The origin system includes code and routines and any digital data necessary to execute the pattern recognition analysis, time series analysis, and/or deep learning analysis.

The pattern recognition data includes any digital data that is necessary for the origin system to perform the pattern recognition analysis using one or more of the following as inputs to a pattern recognition algorithm included in the origin system: the reference data; the sensor data; the profile data; the factors data; the historical data; and the approved strategies data. The pattern recognition data may include object priors or any other digital data that is necessary for this analysis. In some embodiments, the pattern recognition data is an element of the system data129and stored in the data structure131.

The time series analysis data includes any digital data that is necessary for the origin system to perform the time series analysis using one or more of the following as inputs to a time series analysis algorithm included in the origin system: the reference data; the sensor data; the profile data; the factors data; the historical data; and the approved strategies data. In some embodiments, the time series analysis data is an element of the system data129and stored in the data structure131.

The deep learning analysis data includes any digital data that is necessary for the origin system to perform the deep learning analysis using one or more of the following as inputs to a deep learning algorithm included in the origin system: the reference data; the sensor data; the profile data; the factors data; the historical data; and the approved strategies data. In some embodiments, the deep learning analysis data is an element of the system data129and stored in the data structure131.

Vehicular Micro Clouds

Vehicular micro clouds are an optional feature of some of the embodiments described herein. Some of the embodiments described herein include vehicular micro clouds. For example, some or all of the vehicles which are registered with the origin system are connected vehicles (e.g., vehicles that include a processor, a communication unit, and an instance of the origin system) and members of a vehicular micro cloud. In some embodiments, the vehicular micro cloud hosts the origin system in a distributed fashion using the computing resources of the vehicles that are members of the vehicular micro cloud so that a cloud server and/or an edge server is not strictly necessary to provide the service of the origin system to the users of the origin system. Some of the embodiments described herein do not include vehicular micro cloud.

In some embodiments, a server such as a cloud server and/or an edge server is also an element of the vehicle micro cloud.

In some embodiments, a vehicular micro cloud includes as a group of connected vehicles where vehicles perform task(s) cooperatively/collaboratively. Vehicular micro clouds can be divided into two categories based on their mobility: (1) stationary; and (2) mobile.

In the stationary cloud, a certain geographical region is designated as the vehicular micro cloud region, and vehicles entering that region contribute their resources for vehicular cloud services. A stationary vehicular micro cloud is sometimes referred to as a “static” vehicular micro cloud.

In the mobile vehicular cloud, on the other hand, the vehicular micro cloud moves as the micro cloud members move. A mobile vehicular micro cloud is sometimes referred to as a “dynamic” vehicular micro cloud.

In some embodiments, as an optional operating environment, the origin system is hosted by a plurality of members of a vehicular micro cloud. These members are also registered with the origin system. The origin system causes the vehicles, which each include an instance of the origin system or at least a subset of the code and routines of the origin system, to execute steps to form the vehicular micro cloud.

Member data includes digital data that describes information about a vehicular micro cloud and its members. For example, the member data is digital data that describes the identity of the members of the vehicular micro cloud and their specific computing resources; all members of the vehicular micro cloud make their computing resources available to one another for their collective benefit. An example of the member data according to some embodiments includes the member data171depicted inFIG. 1. In some embodiments, the origin system199cause the communication unit to transmit a wireless message to candidates for membership in the vehicular micro cloud that causes these candidates to join the vehicular micro cloud. The list of candidates is determined by the origin system based on the technical data which is transmitted by the candidates to one another via BSMs; in some embodiments, these BSMs also include sensor data recorded by the vehicles that transmit the BSMs. Vehicular micro clouds are described in more detail below according to some embodiments.

Vehicular micro clouds provide vehicular micro cloud tasks. A vehicular micro cloud task includes any task executed by a vehicular micro cloud or a group of vehicular micro clouds. As used herein, the terms “task” and “vehicular micro cloud task” refer to the same thing. A “sub-task” as used herein is a portion of a task or vehicular micro cloud task. An example of a task includes, for example, determining and executing vehicle driving maneuvers that eliminates an origin of an abnormal driving behavior identified by the origin system.

In some embodiments, the vehicular micro cloud tasks provided by the vehicular micro cloud includes some or all of the tasks which are necessary to provide the functionality of the origin system described herein. In some embodiments, a vehicular micro cloud includes a group of connected vehicles that communicate with one another via V2X messages to provide the service of the origin system to the ego vehicle and/or the members of the vehicular micro cloud.

The vehicular micro cloud includes multiple members. A member of the vehicular micro cloud includes a connected vehicle that sends and receives V2X messages via the network (e.g., the network105depicted inFIG. 1). In some embodiments, the network is a serverless ad-hock vehicular network. In some embodiments, the members of the network are nodes of the serverless ad-hoc vehicular network.

In some embodiments, a serverless ad-hoc vehicular network is “serverless” because the serverless ad-hoc vehicular network does not include a server. In some embodiments, a serverless ad-hoc vehicular network is “ad-hoc” because the serverless ad-hoc vehicular network is formed its members when it is determined by one or more of the members to be needed or necessary. In some embodiments, a serverless ad-hoc vehicular network is “vehicular” because the serverless ad-hoc vehicular network only includes connected vehicles as its endpoints. In some embodiments, the term “network” refers to a V2V network.

In some embodiments, the vehicular micro cloud only includes vehicles. For example, the serverless ad-hoc network does not include the following: an infrastructure device, a base station, a roadway device, an edge server, an edge node, and a cloud server. An infrastructure device includes any hardware infrastructure device in a roadway environment such as a traffic signal, traffic light, traffic sign, or any other hardware device that has or does not have the ability to wirelessly communicate with a wireless network.

In some embodiments, the serverless ad-hoc vehicular network includes a set of sensor rich vehicles. A sensor rich vehicle is a connected vehicle that includes a rich sensor set.

In some embodiments, an operating environment that includes the serverless ad-hoc vehicular network also includes a legacy vehicle. A legacy vehicle is a connected vehicle that includes a legacy sensor set. The overall sensing ability of the rich sensor set is greater than the overall sensing ability of the legacy sensor set. For example, a roadway environment includes a set of sensor rich vehicles and a legacy vehicle; the rich sensor set is operable to generate sensor measurements that more accurately describe the geographic locations of objects in the roadway environment when compared to the sensor measurements generated by the legacy sensor set.

In some embodiments, the legacy vehicle is an element of the serverless ad-hoc vehicular network. In some embodiments, the legacy vehicle is not an element of the serverless ad-hoc vehicular network but is able to provide shared rides to users because the driver of the legacy vehicle has a smart device (e.g., an electronic processor-based computing device such as a smartphone, smartwatch, tablet computer, laptop, smart glasses, etc.) which they use to receive information that enables them to participate as registered vehicles that provide shared rides to the users of the Service provided by the origin system.

In some embodiments, the serverless ad-hoc vehicular network is a vehicular micro cloud. It is not a requirement of the embodiments described herein that the serverless ad-hoc vehicular network is a vehicular micro cloud. Accordingly, in some embodiments the serverless ad-hoc vehicular network is not a vehicular micro cloud.

In some embodiments, the serverless ad-hoc vehicular network includes a similar structure that is operable to provide some or all of the functionality as a vehicular micro cloud. Accordingly, a vehicular micro cloud is now described according to some embodiments to provide an understanding of the structure and functionality of the serverless ad-hoc vehicular network according to some embodiments. When describing the vehicular micro cloud, the term “vehicular micro cloud” can be replaced by the term “vehicular micro cloud” since a vehicular micro cloud is an example of a vehicular micro cloud in some embodiments.

Distributed data storage and computing by a group of connected vehicles (i.e., a “vehicular micro cloud”) is a promising solution to cope with an increasing network traffic generated for and by connected vehicles. Vehicles collaboratively store (or cache) data sets in their onboard data storage devices and compute and share these data sets over vehicle-to-vehicle (V2V) networks as requested by other vehicles. Using vehicular micro clouds removes the need for connected vehicles to access remote cloud servers or edge servers by vehicle-to-network (V2N) communications (e.g., by cellular networks) whenever they need to get access to unused computing resources such as shared data (e.g., some or all of the system data129described herein), shared computational power, shared bandwidth, shared memory, and cloudification services.

Some of the embodiments described herein are motivated by the emerging concept of “vehicle cloudification.” Vehicle cloudification means that vehicles equipped with on-board computer unit(s) and wireless communication functionalities form a cluster, called a vehicular micro cloud, and collaborate with other micro cloud members over V2V networks or V2X networks to perform computation, data storage, and data communication tasks in an efficient way. These types of tasks are referred to herein as “vehicular micro cloud tasks” if plural, or a “vehicular micro cloud task” if singular.

In some embodiments, a vehicular micro cloud task includes any computational, data storage, or data communication task collaboratively performed by a plurality of the members of a vehicular micro cloud. In some embodiments, the set of tasks described above with regards to the example general method include one or more vehicular micro cloud tasks as described herein.

In some embodiments, a computational task includes a processor executing code and routines to output a result. The result includes digital data that describes the output of executing the code and routines. For example, a computational task includes a processor executing code and routines to solve a problem (e.g., identifying the origin of an abnormal driving behavior exhibited by the ego vehicle), and the result includes digital data that describes the solution to the problem (e.g., selecting and/or implementing the selected strategy described by the selected strategy data). In some embodiments, the computational task is broken down into sub-tasks whose completion is equivalent to completion of the computational task. In this way, the processors of a plurality of micro cloud members are assigned different sub-tasks configured to complete the computational task; the micro cloud members take steps to complete the sub-tasks in parallel and share the result of the completion of the sub-task with one another via V2X wireless communication. In this way, the plurality of micro cloud members work together collaboratively to complete the computational task. The processors include, for example, the onboard units or electronic control units (ECUs) of a plurality of connected vehicles that are micro cloud members.

In some embodiments, a data storage task includes a processor storing digital data in a memory of a connected vehicle. For example, a digital data file which is too big to be stored in the memory of any one vehicle is stored in the memory of multiple vehicles. In some embodiments, the data storage task is broken down into sub-tasks whose completion is equivalent to completion of the data storage task. In this way, the processors of a plurality of micro cloud members are assigned different sub-tasks configured to complete the data storage task; the micro cloud members take steps to complete the sub-tasks in parallel and share the result of the completion of the sub-task with one another via V2X wireless communication. In this way, the plurality of micro cloud members work together collaboratively to complete the data storage task. For example, a sub-task for a data storage task includes storing a portion of a digital data file in a memory of a micro cloud member; other micro cloud members are assigned sub-tasks to store the remaining portions of digital data file in their memories so that collectively the entire file is stored across the vehicular micro cloud or a sub-set of the vehicular micro cloud.

In some embodiments, a data communication task includes a processor using some or all of the network bandwidth available to the processor (e.g., via the communication unit of the connected vehicle) to transmit a portion a V2X wireless message to another endpoint. For example, a V2X wireless message includes a payload whose file size is too big to be transmitted using the bandwidth available to any one vehicle and so the payload is broken into segments and transmitted at the same time (or contemporaneously) via multiple wireless messages by multiple micro cloud members. In some embodiments, the data communication task is broken down into sub-tasks whose completion is equivalent to completion of the data storage task. In this way, the processors of a plurality of micro cloud members are assigned different sub-tasks configured to complete the data storage task; the micro cloud members take steps to complete the sub-tasks in parallel and share the result of the completion of the sub-task with one another via V2X wireless communication. In this way, the plurality of micro cloud members work together collaboratively to complete the data storage task. For example, a sub-task for a data communication task includes transmitting a portion of a payload for a V2X message to a designated endpoint; other micro cloud members are assigned sub-tasks to transmit the remaining portions of payload using their available bandwidth so that collectively the entire payload is transmitted.

In some embodiments, a vehicular micro cloud task is collaboratively performed by the plurality of members executing computing processes in parallel which are configured to complete the execution of the vehicular micro cloud task.

In some embodiments, a vehicular micro cloud includes a plurality of members that execute computing processes whose completion results in the execution of a vehicular micro cloud task. For example, the serverless ad-hoc vehicular network provides a vehicular micro cloud task to a legacy vehicle.

Vehicular micro clouds are beneficial, for example, because they help vehicles to perform computationally expensive tasks (e.g., determining the analysis data, executing the digital twin simulations, etc.) that they could not perform alone or store large data sets that they could not store alone. In some embodiments, the computational power of a solitary ego vehicle is sufficient to execute these tasks.

Vehicular micro clouds are described in the patent applications that are incorporated by reference in this paragraph. This patent application is related to the following patent applications, the entirety of each of which is incorporated herein by reference: U.S. patent application Ser. No. 15/358,567 filed on Nov. 22, 2016 and entitled “Storage Service for Mobile Nodes in a Roadway Area”; U.S. patent application Ser. No. 15/799,442 filed on Oct. 31, 2017 and entitled “Service Discovery and Provisioning for a Macro-Vehicular Cloud”; U.S. patent application Ser. No. 15/845,945 filed on Dec. 18, 2017 and entitled “Managed Selection of a Geographical Location for a Micro-Vehicular Cloud”; U.S. patent application Ser. No. 15/799,963 filed on Oct. 31, 2017 and entitled “Identifying a Geographic Location for a Stationary Micro-Vehicular Cloud”; U.S. patent application Ser. No. 16/443,087 filed on Jun. 17, 2019 and entitled “Cooperative Parking Space Search by a Vehicular Micro Cloud”; U.S. patent application Ser. No. 16/739,949 filed on Jan. 10, 2020 and entitled “Vehicular Micro Clouds for On-demand Vehicle Queue Analysis”; U.S. patent application Ser. No. 16/735,612 filed on Jan. 6, 2020 and entitled “Vehicular Micro Cloud Hubs”; U.S. patent application Ser. No. 16/387,518 filed on Apr. 17, 2019 and entitled “Reorganizing Autonomous Entities for Improved Vehicular Micro Cloud Operation”; U.S. patent application Ser. No. 16/273,134 filed on Feb. 11, 2019 and entitled “Anomaly Mapping by Vehicular Micro Clouds”; U.S. patent application Ser. No. 16/246,334 filed on Jan. 11, 2019 and entitled “On-demand Formation of Stationary Vehicular Micro Clouds”; and U.S. patent application Ser. No. 16/200,578 filed on Nov. 26, 2018 and entitled “Mobility-oriented Data Replication in a Vehicular Micro Cloud.”

In some embodiments, a typical use case of vehicular micro clouds is a data storage service, where vehicles in a micro cloud collaboratively keep data contents in their on-board data storage device. The vehicular micro cloud allows vehicles in and around the vehicular micro cloud to request the data contents from micro cloud member(s) over V2V communications, reducing the need to access remote cloud servers by vehicle-to-network (e.g., cellular) communications. For some use cases, micro cloud members may also update the cached data contents on the spot with minimal intervention by remote cloud/edge servers (e.g., updating a high-definition road map based on measurements from on-board sensors). This paragraph is not intended to limit the functionality of the embodiments described herein to data storage. As described herein, the embodiments are operable to provide other vehicular micro cloud tasks in addition to data storage tasks.

In some embodiments, the functionality provided by the origin system is a task provided by the vehicular micro cloud. For example, the origin system is an element of a hub of a vehicular micro cloud. The origin system receives a set of wireless messages and this triggers the origin system to form a vehicular micro cloud. The origin system processes V2X data for the benefit of one or more members of the vehicular micro cloud. For example, the ego vehicle includes computational power that exceeds that of another member, and the ego vehicle processes wireless messages for this member which would otherwise be unable to do so, or unable to do so in a timeframe that satisfies a threshold for latency. Hub vehicles are described in more detail below.

The endpoints that are part of the vehicular micro cloud may be referred to herein as “members,” “micro cloud members,” or “member vehicles.” Examples of members include one or more of the following: a connected vehicle; an edge server; a cloud server; any other connected device that has computing resources and has been invited to join the vehicular micro cloud by a handshake process. In some embodiments, the term “member vehicle” specifically refers to only connected vehicles that are members of the vehicular micro cloud whereas the terms “members” or “micro cloud members” is a broader term that may refer to one or more of the following: endpoints that are vehicles; and endpoints that are not vehicles such as roadside units.

In some embodiments, the communication unit of an ego vehicle includes a V2X radio. The V2X radio operates in compliance with a V2X protocol. In some embodiments, the V2X radio is a cellular-V2X radio (“C-V2X radio”). In some embodiments, the V2X radio broadcasts Basic Safety Messages (“BSM” or “safety message” if singular, “BSMs” or “safety messages” if plural). In some embodiments, the safety messages broadcast by the communication unit include some or all of the system data as its payload. In some embodiments, the system data is included in part 2 of the safety message as specified by the Dedicated Short-Range Communication (DSRC) protocol. In some embodiments, the payload includes digital data that describes, among other things, sensor data that describes a roadway environment that includes the members of the vehicular micro cloud.

As used herein, the term “vehicle” refers to a connected vehicle. For example, the ego vehicle and remote connected vehicle depicted inFIG. 1are connected vehicles.

A connected vehicle is a conveyance, such as an automobile, that includes a communication unit that enables the conveyance to send and receive wireless messages via one or more vehicular networks. The embodiments described herein are beneficial for both drivers of human-driven vehicles as well as the autonomous driving systems of autonomous vehicles. For example, the origin system improves the performance of a vehicle control system, which benefits the performance of the vehicle itself by enabling it to operate more safety or in a manner that is more satisfactory to a human driver of the ego vehicle.

In some embodiments, the origin system improves the performance of a network because it beneficially takes steps to enable the completion of vehicular micro cloud tasks.

In some embodiments, the origin system improves the performance of a connected vehicle because it beneficially enables the onboard vehicle computer of a vehicle to identify an origin of its abnormal driving behavior and implement a strategy so that the events which originated the abnormal driving behavior do not occur in the future or do not occur sufficient to precipitate the abnormal driving behavior.

In some embodiments, the origin system is software installed in an onboard unit (e.g., an electronic control unit (ECU)) of a vehicle having V2X communication capability. The vehicle is a connected vehicle and operates in a roadway environment with N number of remote connected vehicles that are also connected vehicles, where N is any positive whole number that is sufficient to satisfy a threshold for forming a vehicular micro cloud. The roadway environment may include one or more of the following example elements: an ego vehicle; N remote connected vehicles; an edge server; and a roadside unit. For the purpose of clarity, the N remote connected vehicles may be referred to herein as the “remote connected vehicle” or the “remote connected vehicles” and this will be understood to describe N remote connected vehicles.

In some embodiments, the origin system includes code and routines stored on and executed by a cloud server or an edge server.

An example of a roadway environment according to some embodiments includes the roadway environment140depicted inFIG. 1. As depicted, the roadway environment140includes objects. Examples of objects include one or of the following: other automobiles, road surfaces; signs, traffic signals, roadway paint, medians, turns, intersections, animals, pedestrians, debris, potholes, accumulated water, accumulated mud, gravel, roadway construction, cones, bus stops, poles, entrance ramps, exit ramps, breakdown lanes, merging lanes, other lanes, railroad tracks, railroad crossings, and any other tangible object that is present in a roadway environment140or otherwise observable or measurable by a camera or some other sensor included in the sensor set.

The ego vehicle and the remote connected vehicles may be human-driven vehicles, autonomous vehicles, or a combination of human-driven vehicles and autonomous vehicles. In some embodiments, the ego vehicle and the remote connected vehicles may be equipped with DSRC equipment such as a GPS unit that has lane-level accuracy and a DSRC radio that is capable of transmitting DSRC messages.

In some embodiments, the ego vehicle and some or all of the remote connected vehicles include their own instance of an origin system. For example, in addition to the ego vehicle, some or all of the remote connected vehicles include an onboard unit having an instance of the origin system installed therein.

In some embodiments, the ego vehicle and one or more of the remote connected vehicles are members of a vehicular micro cloud. In some embodiments, the remote connected vehicles are members of a vehicular micro cloud, but the ego vehicle is not a member of the vehicular micro cloud. In some embodiments, the ego vehicle and some, but not all, of the remote connected vehicles are members of the vehicular micro cloud. In some embodiments, the ego vehicle and some or all of the remote connected vehicles are members of the same vehicular macro cloud but not the same vehicular micro cloud, meaning that they are members of various vehicular micro clouds that are all members of the same vehicular macro cloud so that they are still interrelated to one another by the vehicular macro cloud.

An example of a vehicular micro cloud according to some embodiments includes the vehicular micro cloud194depicted inFIG. 1. The vehicular micro cloud194is depicted inFIG. 1using a dashed line to indicate that it is an optional feature of the operating environment100.

Accordingly, in some embodiments multiple instances of the origin system are installed in a group of connected vehicles. The group of connected vehicles are arranged as a vehicular micro cloud. As described in more detail below, the origin system further organizes the vehicular micro cloud into a set of nano clouds which are each assigned responsibility for completion of a sub-task. Each nano cloud includes at least one member of the vehicular micro cloud so that each nano cloud is operable to complete assigned sub-tasks of a vehicular micro cloud task for the benefit of the members of the vehicular micro cloud.

In some embodiments, a nano cloud includes a subset of a vehicular micro cloud that is organized within the vehicular micro cloud as an entity managed by a hub wherein the entity is organized for the purpose of a completing one or more sub-tasks of a vehicular micro cloud task.

Hub or Hub Vehicle

Hub vehicles are an optional feature of the embodiments described herein. Some of the embodiments described herein include a hub vehicle. Some of the embodiments described herein do not include a hub vehicle.

In some embodiments, the origin system that executes a method as described herein (e.g., the method300depicted inFIG. 3or the example general method described herein, etc.) is an element of a hub or a hub vehicle. For example, the vehicular micro cloud formed by the origin system includes a hub vehicle that provides the following example functionality in addition to the functionality of the methods described herein: (1) controlling when the set of member vehicles leave the vehicular micro cloud (i.e., managing the membership of the vehicular micro cloud, such as who can join, when they can join, when they can leave, etc.); (2) determining how to use the pool of vehicular computing resources to complete a set of tasks in an order for the set of member vehicles wherein the order is determined based on a set of factors that includes safety; (3) determining how to use the pool of vehicular computing resources to complete a set of tasks that do not include any tasks that benefit the hub vehicle; and determining when no more tasks need to be completed, or when no other member vehicles are present except for the hub vehicle, and taking steps to dissolve the vehicular micro cloud responsive to such determinations.

The “hub vehicle” may be referred to herein as the “hub.” An example of a hub vehicle according to some embodiments includes the ego vehicle123depicted inFIG. 1. In some embodiments, the operating environment100includes a roadside unit or some other roadway device, and this roadway device is the hub of the vehicular micro cloud.

In some embodiments, the origin system determines which member vehicle from a group of vehicles (e.g., the ego vehicle and one or more remote connected vehicles) will serve as the hub vehicle based on a set of factors that indicate which vehicle (e.g., the ego vehicle or one of the remote connected vehicles) is the most technologically sophisticated. For example, the member vehicle that has the fastest onboard computer may be the hub vehicle. Other factors that may qualify a vehicle to be the hub include one or more of the following: having the most accurate sensors relative to the other members; having the most bandwidth relative to the other members; and having the most unused memory relative to the other members. Accordingly, the designation of which vehicle is the hub vehicle may be based on a set of factors that includes which vehicle has: (1) the fastest onboard computer relative to the other members; (2) the most accurate sensors relative to the other members; (3) the most bandwidth relative to the other members or other network factors such having radios compliant with the most modern network protocols; and (4) most available memory relative to the other members.

In some embodiments, the designation of which vehicle is the hub vehicle changes over time if the origin system determines that a more technologically sophisticated vehicle joins the vehicular micro cloud. Accordingly, the designation of which vehicle is the hub vehicle is dynamic and not static. In other words, in some embodiments the designation of which vehicle from a group of vehicles is the hub vehicle for that group changes on the fly if a “better” hub vehicle joins the vehicular micro cloud. The factors described in the preceding paragraph are used to determine whether a new vehicle would be better relative to the existing hub vehicle.

In some embodiments, the hub vehicle includes a memory that stores technical data. The technical data includes digital data describing the technological capabilities of each vehicle included in the vehicular micro cloud. The hub vehicle also has access to each vehicle's sensor data because these vehicles broadcast V2X messages that include the sensor data as the payload for the V2X messages. An example of such V2X messages include Basic Safety Messages (BSMs) which include such sensor data in part 2 of their payload. In some embodiments, the technical data is included in the member data (and/or sensor data) depicted inFIG. 1which vehicles such as the ego vehicle123and the remote connected vehicle124broadcast to one another via BSMs. In some embodiments, the member data also includes the sensor data of the vehicle that transmits the BSM as well as some or all of the other digital data described herein as being an element of the member data.

In some embodiments, the technical data is an element of the sensor data (e.g., ego sensor data or remote sensor data provided by the remote connected data) which is included in the V2X data.

A vehicle's sensor data is the digital data recorded by that vehicle's onboard sensor set126. In some embodiments, an ego vehicle's sensor data includes the sensor data recorded by another vehicle's sensor set126; in these embodiments, the other vehicle transmits the sensor data to the ego vehicle via a V2X communication such as a BSM or some other V2X communication.

In some embodiments, the technical data is an element of the sensor data. In some embodiments, the vehicles distribute their sensor data by transmitting BSMs that includes the sensor data in its payload and this sensor data includes the technical data for each vehicle that transmits a BSM; in this way, the hub vehicle receives the technical data for each of the vehicles included in the vehicular micro cloud.

In some embodiments, the hub vehicle is whichever member vehicle of a vehicular micro cloud has a fastest onboard computer relative to the other member vehicles.

In some embodiments, the origin system is operable to provide its functionality to operating environments and network architectures that do not include a server. Use of servers is problematic in some scenarios because they create latency. For example, some prior art systems require that groups of vehicles relay all their messages to one another through a server. By comparison, the use of server is an optional feature for the origin system. For example, the origin system is an element of a roadside unit that includes a communication unit145but not a server. In another example, the origin system is an element of another vehicle such as one of the remote connected vehicles124.

In some embodiments, the operating environment of the origin system includes servers. Optionally, in these embodiments the origin system includes code and routines that predict the expected latency of V2X communications involving serves and then time the transmission of these V2X communications so that the latency is minimized or reduced.

In some embodiments, the origin system is operable to provide its functionality even though the vehicle which includes the origin system does not have a Wi-Fi antenna as part of its communication unit. By comparison, some of the existing solutions require the use of a Wi-Fi antenna in order to provide their functionality. Because the origin system does not require a Wi-Fi antenna, it is able to provide its functionality to more vehicles, including older vehicles without Wi-Fi antennas.

In some embodiments, the origin system includes code and routines that, when executed by a processor, cause the processor to control when a member of the vehicular micro cloud may leave or exit the vehicular micro cloud. This approach is beneficial because it means the hub vehicle has certainty about how much computing resources it has at any given time since it controls when vehicles (and their computing resources) may leave the vehicular micro cloud. The existing solutions do not provide this functionality.

In some embodiments, the origin system includes code and routines that, when executed by a processor, cause the processor to designate a particular vehicle to serve as a hub vehicle responsive to determining that the particular vehicle has sufficient unused computing resources and/or trustworthiness to provide micro cloud services to a vehicular micro cloud using the unused computing resources of the particular vehicle. This is beneficial because it guarantees that only those vehicles having something to contribute to the members of the vehicular micro cloud may join the vehicular micro cloud. In some embodiments, vehicles which the origin system determines are ineligible to participate as members of the vehicular micro cloud are also excluded from providing rides to users as part of the Service.

In some embodiments, the origin system manages the vehicular micro cloud so that it is accessible for membership by vehicles which do not have V2V communication capability. This is beneficial because it ensures that legacy vehicles have access to the benefits provided by the vehicular micro cloud. The existing approaches to task completion by a plurality of vehicles do not provide this functionality.

In some embodiments, the origin system is configured so that a particular vehicle (e.g., the ego vehicle) is pre-designated by a vehicle manufacturer to serve as a hub vehicle for any vehicular micro cloud that it joins. The existing approaches to task completion by a plurality of vehicles do not provide this functionality.

The existing solutions generally do not include vehicular micro clouds. Some groups of vehicles (e.g., cliques, platoons, etc.) might appear to be a vehicular micro cloud when they in fact are not a vehicular micro cloud. For example, in some embodiments a vehicular micro cloud requires that all its members share it unused computing resources with the other members of the vehicular micro cloud. Any group of vehicles that does not require all its members to share their unused computing resources with the other members is not a vehicular micro cloud.

In some embodiments, a vehicular micro cloud does not require a server and preferably would not include one because of the latency created by communication with a server. Accordingly, in some but not all embodiments, any group of vehicles that includes a server or whose functionality incorporates a server is not a vehicular micro cloud as this term is used herein.

In some embodiments, a vehicular micro cloud formed by an origin system is operable to harness the unused computing resources of many different vehicles to perform complex computational tasks that a single vehicle alone cannot perform due to the computational limitations of a vehicle's onboard vehicle computer which are known to be limited. Accordingly, any group of vehicles that does harness the unused computing resources of many different vehicles to perform complex computational tasks that a single vehicle alone cannot perform is not a vehicular micro cloud.

In some embodiments, a vehicular micro cloud can include vehicles that are parked, vehicles that are traveling in different directions, infrastructure devices, or almost any endpoint that is within communication range of a member of the vehicular micro cloud.

In some embodiments, the origin system is configured so that vehicles are required to have a predetermined threshold of unused computing resources to become members of a vehicular micro cloud. Accordingly, any group of vehicles that does not require vehicles to have a predetermined threshold of unused computing resources to become members of the group is not a vehicular micro cloud in some embodiments.

In some embodiments, a hub of a vehicular micro cloud is pre-designated by a vehicle manufacturer by the inclusion of one a bit or a token in a memory of the vehicle at the time of manufacture that designates the vehicle as the hub of all vehicular micro clouds which it joins. Accordingly, if a group of vehicles does not include a hub vehicle having a bit or a token in their memory from the time of manufacture that designates it as the hub for all groups of vehicles that it joins, then this group is not a vehicular micro cloud in some embodiments.

A vehicular micro cloud is not a V2X network or a V2V network. For example, neither a V2X network nor a V2V network include a cluster of vehicles in a same geographic region that are computationally joined to one another as members of a logically associated cluster that make available their unused computing resources to the other members of the cluster. In some embodiments, any of the steps of a method described herein (e.g., the method300depicted inFIG. 3) is executed by one or more vehicles which are working together collaboratively using V2X communications for the purpose of completing one or more steps of the method(s). By comparison, solutions which only include V2X networks or V2V networks do not necessarily include the ability of two or more vehicles to work together collaboratively to complete one or more steps of a method.

In some embodiments, a vehicular micro cloud includes vehicles that are parked, vehicles that are traveling in different directions, infrastructure devices, or almost any endpoint that is within communication range of a member of the vehicular micro cloud. By comparison, a group of vehicles that exclude such endpoints as a requirement of being a member of the group are not vehicular micro clouds according to some embodiments.

In some embodiments, a vehicular micro cloud is operable to complete computational tasks itself, without delegation of these computational tasks to a cloud server, using the onboard vehicle computers of its members; this is an example of a vehicular micro cloud task according to some embodiments. In some embodiments, a group of vehicles which relies on a cloud server for its computational analysis, or the difficult parts of its computational analysis, is not a vehicular micro cloud. AlthoughFIG. 1depicts a server in an operating environment that includes the origin system, the server is an optional feature of the operating environment. An example of a preferred embodiment of the origin system does not include the server in the operating environment which includes the origin system.

In some embodiments, the origin system enables a group of vehicles to perform computationally expensive tasks that could not be completed by any one vehicle in isolation.

An existing solution to vehicular micro cloud task execution involves vehicle platoons. As explained herein, a platoon is not a vehicular micro cloud and does not provide the benefits of a vehicular micro cloud, and some embodiments of the origin system requires vehicular micro cloud; this distinction alone differentiates the origin system from the existing solutions. The origin system is different from the existing solution for additional reasons. For example, the existing solution that relies on vehicle platooning does not include functionality whereby the members of a platoon are changed among the platoons dynamically during the task execution. As another example, the existing solution does not consider the task properties, road geometry, actual and/or predicted traffic information and resource capabilities of vehicles to determine the number of platoons. The existing solution also does not include functionality whereby platoons swap which sub-task they are performing among themselves while the sub-tasks are still being performed by the platoons in parallel. The existing solution also does not include functionality whereby platoons are re-organized based on monitored task executions results/performance and/or available vehicles and resources. As described herein, the origin system includes code and routines that provide, among other things, all of this functionality which is lacking in the existing solution.

Vehicle Control System

Modern vehicles include Advanced Driver Assistance Systems (ADAS systems) or automated driving systems. These systems are referred to herein collectively or individually as “vehicle control systems.” An automated driving system includes a sufficient number of ADAS systems so that the vehicle which includes these ADAS systems is rendered autonomous by the benefit of the functionality received by the operation of the ADAS systems by a processor of the vehicle. An example of a vehicle control system according to some embodiments includes the vehicle control system153depicted inFIGS. 1 and 2.

A particular vehicle that includes these vehicle control systems is referred to herein as an “ego vehicle” and other vehicles in the vicinity of the ego vehicle as “remote connected vehicles.” As used herein, the term “vehicle” includes a connected vehicle that includes a communication unit and is operable to send and receive V2X communications via a wireless network (e.g., the network105depicted inFIG. 1).

Modern vehicles collect a lot of data describing their environment, in particular image data. An ego vehicle uses this image data to understand their environment and operate their vehicle control systems (e.g., ADAS systems or automated driving systems).

As automated vehicles and ADAS systems become increasingly popular, it is important that vehicles have access to the best possible digital data that describes their surrounding environment. In other words, it is important for modern vehicles to have the best possible environmental perception abilities.

Vehicles perceive their surrounding environment by having their onboard sensors record sensor measurements and then analyzing the sensor data to identify one or more of the following: which objects are in their environment; where these objects are located in their environment; and various measurements about these objects (e.g., speed, heading, path history, etc.). This invention is about helping vehicles to have the best possible environmental perception abilities.

Vehicles use their onboard sensors and computing resources to execute perception algorithms that inform them about the objects that are in their environment, where these objects are located in their environment, and various measurements about these objects (e.g., speed, heading, path history, etc.).

Cellular Vehicle to Everything (C-V2X)

C-V2X is an optional feature of the embodiments described herein. Some of the embodiments described herein utilize C-V2X communications. Some of the embodiments described herein do not utilize C-V2X communications. For example, the embodiments described herein utilize V2X communications other than C-V2X communications. C-V2X is defined as 3GPP direct communication (PC5) technologies that include LTE-V2X, 5G NR-V2X, and future 3GPP direct communication technologies.

Dedicated Short-Range Communication (DSRC) is now introduced. A DSRC-equipped device is any processor-based computing device that includes a DSRC transmitter and a DSRC receiver. For example, if a vehicle includes a DSRC transmitter and a DSRC receiver, then the vehicle may be described as “DSRC-enabled” or “DSRC-equipped.” Other types of devices may be DSRC-enabled. For example, one or more of the following devices may be DSRC-equipped: an edge server; a cloud server; a roadside unit (“RSU”); a traffic signal; a traffic light; a vehicle; a smartphone; a smartwatch; a laptop; a tablet computer; a personal computer; and a wearable device.

In some embodiments, instances of the term “DSRC” as used herein may be replaced by the term “C-V2X.” For example, the term “DSRC radio” is replaced by the term “C-V2X radio,” the term “DSRC message” is replaced by the term “C-V2X message,” and so on.

In some embodiments, instances of the term “V2X” as used herein may be replaced by the term “C-V2X.”

In some embodiments, one or more of the connected vehicles described above are DSRC-equipped vehicles. A DSRC-equipped vehicle is a vehicle that includes a standard-compliant GPS unit and a DSRC radio which is operable to lawfully send and receive DSRC messages in a jurisdiction where the DSRC-equipped vehicle is located. A DSRC radio is hardware that includes a DSRC receiver and a DSRC transmitter. The DSRC radio is operable to wirelessly send and receive DSRC messages on a band that is reserved for DSRC messages.

A DSRC message is a wireless message that is specially configured to be sent and received by highly mobile devices such as vehicles, and is compliant with one or more of the following DSRC standards, including any derivative or fork thereof: EN 12253:2004 Dedicated Short-Range Communication—Physical layer using microwave at 5.8 GHz (review); EN 12795:2002 Dedicated Short-Range Communication (DSRC)—DSRC Data link layer: Medium Access and Logical Link Control (review); EN 12834:2002 Dedicated Short-Range Communication—Application layer (review); and EN 13372:2004 Dedicated Short-Range Communication (DSRC)—DSRC profiles for RTTT applications (review); EN ISO 14906:2004 Electronic Fee Collection—Application interface.

A DSRC message is not any of the following: a WiFi message; a 3G message; a 4G message; an LTE message; a millimeter wave communication message; a Bluetooth message; a satellite communication; and a short-range radio message transmitted or broadcast by a key fob at 315 MHz or 433.92 MHz. For example, in the United States, key fobs for remote keyless systems include a short-range radio transmitter which operates at 315 MHz, and transmissions or broadcasts from this short-range radio transmitter are not DSRC messages since, for example, such transmissions or broadcasts do not comply with any DSRC standard, are not transmitted by a DSRC transmitter of a DSRC radio and are not transmitted at 5.9 GHz. In another example, in Europe and Asia, key fobs for remote keyless systems include a short-range radio transmitter which operates at 433.92 MHz, and transmissions or broadcasts from this short-range radio transmitter are not DSRC messages for similar reasons as those described above for remote keyless systems in the United States.

In some embodiments, a DSRC-equipped device (e.g., a DSRC-equipped vehicle) does not include a conventional global positioning system unit (“GPS unit”), and instead includes a standard-compliant GPS unit. A conventional GPS unit provides positional information that describes a position of the conventional GPS unit with an accuracy of plus or minus 10 meters of the actual position of the conventional GPS unit. By comparison, a standard-compliant GPS unit provides GPS data that describes a position of the standard-compliant GPS unit with an accuracy of plus or minus 1.5 meters of the actual position of the standard-compliant GPS unit. This degree of accuracy is referred to as “lane-level accuracy” since, for example, a lane of a roadway is generally about 3 meters wide, and an accuracy of plus or minus 1.5 meters is sufficient to identify which lane a vehicle is traveling in even when the roadway has more than one lanes of travel each heading in a same direction.

In some embodiments, a standard-compliant GPS unit is operable to identify, monitor and track its two-dimensional position within 1.5 meters, in all directions, of its actual position 68% of the time under an open sky.

GPS data includes digital data describing the location information outputted by the GPS unit. An example of a standard-compliant GPS unit according to some embodiments includes the standard-compliant GPS unit150depicted inFIG. 1.

In some embodiments, the connected vehicle described herein, and depicted inFIG. 1, includes a V2X radio instead of a DSRC radio. In these embodiments, all instances of the term DSRC” as used in this description may be replaced by the term “V2X.” For example, the term “DSRC radio” is replaced by the term “V2X radio,” the term “DSRC message” is replaced by the term “V2X message,” and so on.

75 MHz of the 5.9 GHz band may be designated for DSRC. However, in some embodiments, the lower 45 MHz of the 5.9 GHz band (specifically, 5.85-5.895 GHz) is reserved by a jurisdiction (e.g., the United States) for unlicensed use (i.e., non-DSRC and non-vehicular related use) whereas the upper 30 MHz of the 5.9 GHz band (specifically, 5.895-5.925 GHz) is reserved by the jurisdiction for Cellular Vehicle to Everything (C-V2X) use. In these embodiments, the V2X radio depicted inFIG. 1is a C-V2X radio which is operable to send and receive C-V2X wireless messages on the upper 30 MHz of the 5.9 GHz band (i.e., 5.895-5.925 GHz). In these embodiments, the origin system199is operable to cooperate with the C-V2X radio and provide its functionality using the content of the C-V2X wireless messages.

In some of these embodiments, some or all of the digital data depicted inFIG. 1is the payload for one or more C-V2X messages. In some embodiments, the C-V2X message is a BSM.

Vehicular Network

In some embodiments, the origin system utilizes a vehicular network. A vehicular network includes, for example, one or more of the following: V2V; V2X; vehicle-to-network-to-vehicle (V2N2V); vehicle-to-infrastructure (V2I); C-V2X; any derivative or combination of the networks listed herein; and etc.

In some embodiments, the origin system includes software installed in an onboard unit of a connected vehicle. This software is the “origin system” described herein.

An example operating environment for the embodiments described herein includes an ego vehicle, one or more remote connected vehicles, and a recipient vehicle. The ego vehicle the remote connected vehicle are connected vehicles having communication units that enable them to send and receive wireless messages via one or more vehicular networks. In some embodiments, the recipient vehicle is a connected vehicle. In some embodiments, the ego vehicle and the remote connected vehicle include an onboard unit having an origin system stored therein.

Some of the embodiments described herein include a server. However, some of the embodiments described herein do not include a server. A serverless operating environment is an operating environment which includes at least one origin system and does not include a server.

In some embodiments, the origin system includes code and routines that are operable, when executed by a processor of the onboard unit, to cause the processor to execute one or more of the steps of the method300depicted inFIG. 3or any other method described herein (e.g., the example general method).

This patent application is related to U.S. patent application Ser. No. 15/644,197 filed on Jul. 7, 2017 and entitled “Computation Service for Mobile Nodes in a Roadway Environment,” the entirety of which is hereby incorporated by reference. This patent application is also related to U.S. patent application Ser. No. 16/457,612 filed on Jun. 28, 2019 and entitled “Context System for Providing Cyber Security for Connected Vehicles,” the entirety of which is hereby incorporated by reference.

Example Overview

In some embodiments, the origin system is software that is operable, when executed by a processor, to cause the processor to execute one or more of the methods described herein. An example operating environment100for the origin system is depicted inFIG. 1.

In some embodiments, the origin system199is software installed in an onboard unit (e.g., an electronic control unit (ECU)) of a particular make of vehicle having V2X communication capability. For example, the ego vehicle123includes a communication unit145. The communication unit145includes a V2X radio. For example, the communication unit145includes a C-V2X radio.FIG. 1depicts an example operating environment100for the origin system199according to some embodiments.

Example Operative Environment

Embodiments of the origin system are now described. Referring now toFIG. 1, depicted is a block diagram illustrating an operating environment100for an origin system199according to some embodiments. The operating environment100is present in a roadway environment140. In some embodiments, each of the elements of the operating environment100is present in the same roadway environment140at the same time. In some embodiments, some of the elements of the operating environment100are not present in the same roadway environment140at the same time.

The operating environment100may include one or more of the following elements: an ego vehicle123(referred to herein as a “vehicle123” or an “ego vehicle123”) operated by a driver109; a remote connected vehicle124(which has a driver too, which is not pictured, in embodiments where the remote connected vehicle124is not at least a Level III autonomous vehicle); a cloud server103; and an edge server198. These elements are communicatively coupled to one another via a network105. These elements of the operating environment100are depicted by way of illustration. In practice, the operating environment100may include one or more of the elements depicted inFIG. 1. For example, although only two vehicles123,124are depicted inFIG. 1, in practice the operating environment100can include a plurality of these elements.

The operating environment100also includes the roadway environment140. The roadway environment140was described above, and that description will not be repeated here.

In some embodiments, one or more of the ego vehicle123, the remote connected vehicle124, and the network105are elements (e.g., members) of a vehicular micro cloud194.

In some embodiments, the ego vehicle123and the remote connected vehicle124include similar elements. For example, each of these elements of the operating environment100include their own processor125, bus121, memory127, communication unit145, processor125, sensor set126, onboard unit139, standard-compliant GPS unit150, and origin system199. These elements of the ego vehicle123and the remote connected vehicle124provide the same or similar functionality regardless of whether they are included in the ego vehicle123or the remote connected vehicle124. Accordingly, the descriptions of these elements will not be repeated in this description for each of the ego vehicle123and the remote connected vehicle124.

In the depicted embodiment, the ego vehicle123and the remote connected vehicle124store similar digital data. The system data129includes digital data that describes some or all of the digital data stored in the memory127or otherwise described herein. The system data129is depicted inFIG. 1as being an element of the cloud server103, but in practice the system data129is stored on one or more of the server, the ego vehicle123, and one or more of the remote connected vehicles124.

In some embodiments, the vehicular micro cloud194is a stationary vehicular micro cloud such as described by U.S. patent application Ser. No. 15/799,964 filed on Oct. 31, 2017 and entitled “Identifying a Geographic Location for a Stationary Micro-Vehicular Cloud,” the entirety of which is herein incorporated by reference. The vehicular micro cloud194is depicted with a dashed line inFIG. 1to indicate that it is an optional element of the operating environment100.

In some embodiments, the vehicular micro cloud194includes a stationary vehicular micro cloud or a mobile vehicular micro cloud. For example, each of the ego vehicle123and the remote connected vehicle124are vehicular micro cloud members because they are connected endpoints that are members of the vehicular micro cloud194that can access and use the unused computing resources (e.g., their unused processing power, unused data storage, unused sensor capabilities, unused bandwidth, etc.) of the other vehicular micro cloud members using wireless communications that are transmitted via the network105and these wireless communicates are not required to be relayed through a cloud server. As used herein, the terms a “vehicular micro cloud” and a “micro-vehicular cloud” mean the same thing.

In some embodiments, the vehicular micro cloud194is a vehicular micro cloud such as the one described in U.S. patent application Ser. No. 15/799,963.

In some embodiments, the vehicular micro cloud194includes a dynamic vehicular micro cloud. In some embodiments, the vehicular micro cloud194includes an interdependent vehicular micro cloud. In some embodiments, the vehicular micro cloud194is sub-divided into a set of nano clouds.

In some embodiments, the operating environment100includes a plurality of vehicular micro clouds194. For example, the operating environment100includes a first vehicular micro cloud and a second vehicular micro cloud.

In some embodiments, a vehicular micro cloud194is not a V2X network or a V2V network because, for example, such networks do not include allowing endpoints of such networks to access and use the unused computing resources of the other endpoints of such networks. By comparison, a vehicular micro cloud194requires allowing all members of the vehicular micro cloud194to access and use designated unused computing resources of the other members of the vehicular micro cloud194. In some embodiments, endpoints must satisfy a threshold of unused computing resources in order to join the vehicular micro cloud194. The hub vehicle of the vehicular micro cloud194executes a process to: (1) determine whether endpoints satisfy the threshold as a condition for joining the vehicular micro cloud194; and (2) determine whether the endpoints that do join the vehicular micro cloud194continue to satisfy the threshold after they join as a condition for continuing to be members of the vehicular micro cloud194.

In some embodiments, a member of the vehicular micro cloud194includes any endpoint (e.g., the ego vehicle123, the remote connected vehicle124, etc.) which has completed a process to join the vehicular micro cloud194(e.g., a handshake process with the coordinator of the vehicular micro cloud194). Cloud servers are excluded from membership in some embodiments. A member of the vehicular micro cloud194is described herein as a “member” or a “micro cloud member.” In some embodiments, a coordinator of the vehicular micro cloud194is the hub of the vehicular micro cloud (e.g., the ego vehicle123).

In some embodiments, the memory127of one or more of the endpoints stores member data171. The member data171is digital data that describes one or more of the following: the identity of each of the micro cloud members; what digital data, or bits of data, are stored by each micro cloud member; what computing services are available from each micro cloud member; what computing resources are available from each micro cloud member and what quantity of these resources are available; and how to communicate with each micro cloud member.

In some embodiments, the member data171describes logical associations between endpoints which are a necessary component of the vehicular micro cloud194and serves to differentiate the vehicular micro cloud194from a mere V2X network. In some embodiments, a vehicular micro cloud194must include a hub vehicle and this is a further differentiation from a vehicular micro cloud194and a V2X network or a group, clique, or platoon of vehicles which is not a vehicular micro cloud194.

In some embodiments, the member data171describes the logical associations between more than one vehicular micro cloud. For example, the member data171describes the logical associations between the first vehicular micro cloud and the second vehicular micro cloud. Accordingly, in some embodiments the memory127includes member data171for more than one vehicular micro cloud194.

In some embodiments, the vehicular micro cloud194does not include a hardware server. Accordingly, in some embodiments the vehicular micro cloud194may be described as serverless.

In some embodiments, the vehicular micro cloud194includes a server. For example, in some embodiments the vehicular micro cloud194includes the cloud server103.

The network105is a conventional type, wired or wireless, and may have numerous different configurations including a star configuration, token ring configuration, or other configurations. Furthermore, the network105may include a local area network (LAN), a wide area network (WAN) (e.g., the Internet), or other interconnected data paths across which multiple devices and/or entities may communicate. In some embodiments, the network105may include a peer-to-peer network. The network105may also be coupled to or may include portions of a telecommunications network for sending data in a variety of different communication protocols. In some embodiments, the network105includes Bluetooth® communication networks or a cellular communications network for sending and receiving data including via short messaging service (SMS), multimedia messaging service (MMS), hypertext transfer protocol (HTTP), direct data connection, wireless application protocol (WAP), e-mail, DSRC, full-duplex wireless communication, mmWave, WiFi (infrastructure mode), WiFi (ad-hoc mode), visible light communication, TV white space communication and satellite communication. The network105may also include a mobile data network that may include 3G, 4G, 5G, millimeter wave (mmWave), LTE, LTE-V2X, LTE-D2D, VoLTE or any other mobile data network or combination of mobile data networks. Further, the network105may include one or more IEEE 802.11 wireless networks.

In some embodiments, the network105is a V2X network. For example, the network105must include a vehicle, such as the ego vehicle123, as an originating endpoint for each wireless communication transmitted by the network105. An originating endpoint is the endpoint that initiated a wireless communication using the network105. In some embodiments, the network105is a vehicular network. In some embodiments, the network105is a C-V2X network.

In some embodiments, the network105is an element of the vehicular micro cloud194. Accordingly, the vehicular micro cloud194is not the same thing as the network105since the network is merely a component of the vehicular micro cloud194. For example, the network105does not include member data. The network105also does not include a hub vehicle.

In some embodiments, one or more of the ego vehicle123and the remote connected vehicle124are C-V2X equipped vehicles. For example, the ego vehicle123includes a standard-compliant GPS unit150that is an element of the sensor set126and a C-V2X radio that is an element of the communication unit145. The network105may include a C-V2X communication channel shared among the ego vehicle123and a second vehicle such as the remote connected vehicle124.

A C-V2X radio is hardware radio that includes a C-V2X receiver and a C-V2X transmitter. The C-V2X radio is operable to wirelessly send and receive C-V2X messages on a band that is reserved for C-V2X messages.

The ego vehicle123includes a car, a truck, a sports utility vehicle, a bus, a semi-truck, a drone, or any other roadway-based conveyance. In some embodiments, the ego vehicle123includes an autonomous vehicle or a semi-autonomous vehicle. Although not depicted inFIG. 1, in some embodiments, the ego vehicle123includes an autonomous driving system. The autonomous driving system includes code and routines that provides sufficient autonomous driving features to the ego vehicle123to render the ego vehicle123an autonomous vehicle or a highly autonomous vehicle. In some embodiments, the ego vehicle123is a Level III autonomous vehicle or higher as defined by the National Highway Traffic Safety Administration and the Society of Automotive Engineers. In some embodiments, the vehicle control system153is an autonomous driving system.

The ego vehicle123is a connected vehicle. For example, the ego vehicle123is communicatively coupled to the network105and operable to send and receive messages via the network105. For example, the ego vehicle123transmits and receives V2X messages via the network105.

The ego vehicle123includes one or more of the following elements: a processor125; a sensor set126; a standard-compliant GPS unit150; a vehicle control system153; a communication unit145; an onboard unit139; a memory127; and an origin system199. These elements may be communicatively coupled to one another via a bus121. In some embodiments, the communication unit145includes a V2X radio.

The processor125includes an arithmetic logic unit, a microprocessor, a general-purpose controller, or some other processor array to perform computations and provide electronic display signals to a display device. The processor125processes data signals and may include various computing architectures including a complex instruction set computer (CISC) architecture, a reduced instruction set computer (RISC) architecture, or an architecture implementing a combination of instruction sets. AlthoughFIG. 1depicts a single processor125present in the ego vehicle123, multiple processors may be included in the ego vehicle123. The processor125may include a graphical processing unit. Other processors, operating systems, sensors, displays, and physical configurations may be possible.

In some embodiments, the processor125is an element of a processor-based computing device of the ego vehicle123. For example, the ego vehicle123may include one or more of the following processor-based computing devices and the processor125may be an element of one of these devices: an onboard vehicle computer; an electronic control unit; a navigation system; a vehicle control system (e.g., an ADAS system or autonomous driving system); and a head unit. In some embodiments, the processor125is an element of the onboard unit139.

The onboard unit139is a special purpose processor-based computing device. In some embodiments, the onboard unit139is a communication device that includes one or more of the following elements: the communication unit145; the processor125; the memory127; and the origin system199. In some embodiments, the onboard unit139is the computer system200depicted inFIG. 2. In some embodiments, the onboard unit139is an electronic control unit (ECU).

The sensor set126includes one or more onboard sensors. The sensor set126records sensor measurements that describe the ego vehicle123and/or the physical environment (e.g., the roadway environment140) that includes the ego vehicle123. The ego sensor data195includes digital data that describes the sensor measurements.

In some embodiments, the sensor set126may include one or more sensors that are operable to measure the physical environment outside of the ego vehicle123. For example, the sensor set126may include cameras, lidar, radar, sonar and other sensors that record one or more physical characteristics of the physical environment that is proximate to the ego vehicle123.

In some embodiments, the sensor set126may include one or more sensors that are operable to measure the physical environment inside a cabin of the ego vehicle123. For example, the sensor set126may record an eye gaze of the driver (e.g., using an internal camera), where the driver's hands are located (e.g., using an internal camera) and whether the driver is touching a head unit or infotainment system with their hands (e.g., using a feedback loop from the head unit or infotainment system that indicates whether the buttons, knobs or screen of these devices is being engaged by the driver).

In some embodiments, the sensor set126may include one or more of the following sensors: an altimeter; a gyroscope; a proximity sensor; a microphone; a microphone array; an accelerometer; a camera (internal or external); a LIDAR sensor; a laser altimeter; a navigation sensor (e.g., a global positioning system sensor of the standard-compliant GPS unit150); an infrared detector; a motion detector; a thermostat; a sound detector, a carbon monoxide sensor; a carbon dioxide sensor; an oxygen sensor; a mass air flow sensor; an engine coolant temperature sensor; a throttle position sensor; a crank shaft position sensor; an automobile engine sensor; a valve timer; an air-fuel ratio meter; a blind spot meter; a curb feeler; a defect detector; a Hall effect sensor, a manifold absolute pressure sensor; a parking sensor; a radar gun; a speedometer; a speed sensor; a tire-pressure monitoring sensor; a torque sensor; a transmission fluid temperature sensor; a turbine speed sensor (TSS); a variable reluctance sensor; a vehicle speed sensor (VSS); a water sensor; a wheel speed sensor; and any other type of automotive sensor.

The sensor set126is operable to record ego sensor data195. The ego sensor data195includes digital data that describes images or other measurements of the physical environment such as the conditions, objects, and other vehicles present in the roadway environment. Examples of objects include pedestrians, animals, traffic signs, traffic lights, potholes, etc. Examples of conditions include weather conditions, road surface conditions, shadows, leaf cover on the road surface, any other condition that is measurable by a sensor included in the sensor set126.

The physical environment may include a roadway region, parking lot, or parking garage that is proximate to the ego vehicle123. In some embodiments, the roadway environment140is a roadway that includes a roadway region. The ego sensor data195may describe measurable aspects of the physical environment. In some embodiments, the physical environment is the roadway environment140. As such, in some embodiments, the roadway environment140includes one or more of the following: a roadway region that is proximate to the ego vehicle123; a parking lot that is proximate to the ego vehicle123; a parking garage that is proximate to the ego vehicle123; the conditions present in the physical environment proximate to the ego vehicle123; the objects present in the physical environment proximate to the ego vehicle123; and other vehicles present in the physical environment proximate to the ego vehicle123; any other tangible object that is present in the real-world and proximate to the ego vehicle123or otherwise measurable by the sensors of the sensor set126or whose presence is determinable from the digital data stored on the memory127. An item is “proximate to the ego vehicle123” if it is directly measurable by a sensor of the ego vehicle123or its presence is inferable and/or determinable by the origin system199based on analysis of the ego sensor data195which is recorded by the ego vehicle123and/or one or more members of the vehicular micro cloud194.

In some embodiments, the ego sensor data195includes digital data that describes all of the sensor measurements recorded by the sensor set126of the ego vehicle.

For example, the ego sensor data195includes, among other things, one or more of the following: lidar data (i.e., depth information) recorded by an ego vehicle; or camera data (i.e., image information) recorded by the ego vehicle. The lidar data includes digital data that describes depth information about a roadway environment140recorded by a lidar sensor of a sensor set126included in the ego vehicle123. The camera data includes digital data that describes the images recorded by a camera of the sensor set126included in the ego vehicle123. The depth information and the images describe the roadway environment140, including tangible objects in the roadway environment140and any other physical aspects of the roadway environment140that are measurable using a depth sensor and/or a camera.

In some embodiments, the sensors of the sensor set126are operable to collect ego sensor data195. The sensors of the sensor set126include any sensors that are necessary to measure and record the measurements described by the ego sensor data195. In some embodiments, the ego sensor data195includes any sensor measurements that are necessary to generate the other digital data stored by the memory127. In some embodiments, the ego sensor data195includes digital data that describes any sensor measurements that are necessary for the origin system199provides its functionality as described herein with reference to the method300depicted inFIG. 3and/or the example general method described herein.

In some embodiments, the sensor set126includes any sensors that are necessary to record ego sensor data195that describes the roadway environment140in sufficient detail to create a digital twin of the roadway environment140. In some embodiments, the origin system199generates the set of nano clouds and assigns sub-tasks to the nano clouds based on the outcomes observed by the origin system199during the execution of a set of digital twins that simulate the real-life circumstances of the ego vehicle123.

For example, in some embodiments the origin system199includes simulation software. The simulation software is any simulation software that is capable of simulating an execution of a vehicular micro cloud task. For example, the simulation software is operable simulate the origin system199providing its functionality to generate some or all of the system data129. In some embodiments, the vehicular micro cloud194is divided into a set of nano clouds.

A digital twin is a simulated version of a specific real-world vehicle that exists in a simulation. A structure, condition, behavior, and responses of the digital twin are similar to a structure, condition, behavior, and responses of the specific real-world vehicle that the digital twin represents in the simulation. The digital environment included in the simulation is similar to the real-world roadway environment140of the real-world vehicle. The simulation software includes code and routines that are operable to execute simulations based on digital twins of real-world vehicles in the roadway environment.

In some embodiments, the simulation software is integrated with the origin system199. In some other embodiments, the simulation software is a standalone software that the origin system199can access to execute digital twin simulations to determine the best way to divide the vehicular micro cloud194into nano clouds and which sub-tasks to assign which nano clouds. The digital twin simulations may also be used by the origin system199to determine how to break down the vehicular micro cloud task into sub-tasks.

Digital twins, and an example process for generating and using digital twins which is implemented by the origin system199in some embodiments, are described in U.S. patent application Ser. No. 16/521,574 entitled “Altering a Vehicle based on Driving Pattern Comparison” filed on Jul. 24, 2019, the entirety of which is hereby incorporated by reference.

The ego sensor data195includes digital data that describes any measurement that is taken by one or more of the sensors of the sensor set126.

The standard-compliant GPS unit150includes a GPS unit that is compliant with one or more standards that govern the transmission of V2X wireless communications (“V2X communication” if singular, “V2X communications” if plural). For example, some V2X standards require that BSMs are transmitted at intervals by vehicles and that these BSMs must include within their payload GPS data having one or more attributes.

An example of an attribute for GPS data is accuracy. In some embodiments, the standard-compliant GPS unit150is operable to generate GPS measurements which are sufficiently accurate to describe the location of the ego vehicle123with lane-level accuracy. Lane-level accuracy is necessary to comply with some of the existing and emerging standards for V2X communication (e.g., C-V2X communication). Lane-level accuracy means that the GPS measurements are sufficiently accurate to describe which lane of a roadway that the ego vehicle123is traveling (e.g., the geographic position described by the GPS measurement is accurate to within 1.5 meters of the actual position of the ego vehicle123in the real-world). Lane-level accuracy is described in more detail below.

In some embodiments, the standard-compliant GPS unit150is compliant with one or more standards governing V2X communications but does not provide GPS measurements that are lane-level accurate.

In some embodiments, the standard-compliant GPS unit150includes any hardware and software necessary to make the ego vehicle123or the standard-compliant GPS unit150compliant with one or more of the following standards governing V2X communications, including any derivative or fork thereof: EN 12253:2004 Dedicated Short-Range Communication—Physical layer using microwave at 5.8 GHz (review); EN 12795:2002 Dedicated Short-Range Communication (DSRC)—DSRC Data link layer: Medium Access and Logical Link Control (review); EN 12834:2002 Dedicated Short-Range Communication—Application layer (review); and EN 13372:2004 Dedicated Short-Range Communication (DSRC)—DSRC profiles for RTTT applications (review); EN ISO 14906:2004 Electronic Fee Collection—Application interface.

In some embodiments, the standard-compliant GPS unit150is operable to provide GPS data describing the location of the ego vehicle123with lane-level accuracy. For example, the ego vehicle123is traveling in a lane of a multi-lane roadway. Lane-level accuracy means that the lane of the ego vehicle123is described by the GPS data so accurately that a precise lane of travel of the ego vehicle123may be accurately determined based on the GPS data for this ego vehicle123as provided by the standard-compliant GPS unit150.

An example process for generating GPS data describing a geographic location of an object (e.g., a vehicle, a roadway object, an object of interest, a remote connected vehicle124, the ego vehicle123, or some other tangible object or construct located in a roadway environment140) is now described according to some embodiments. In some embodiments, the origin system199include code and routines that are operable, when executed by the processor125, to cause the processor to: analyze (1) GPS data describing the geographic location of the ego vehicle123and (2) ego sensor data describing the range separating the ego vehicle123from an object and a heading for this range; and determine, based on this analysis, GPS data describing the location of the object. The GPS data describing the location of the object may also have lane-level accuracy because, for example, it is generated using accurate GPS data of the ego vehicle123and accurate sensor data describing information about the object.

In some embodiments, the standard-compliant GPS unit150includes hardware that wirelessly communicates with a GPS satellite (or GPS server) to retrieve GPS data that describes the geographic location of the ego vehicle123with a precision that is compliant with a V2X standard. One example of a V2X standard is the DSRC standard. Other standards governing V2X communications are possible. The DSRC standard requires that GPS data be precise enough to infer if two vehicles (one of which is, for example, the ego vehicle123) are located in adjacent lanes of travel on a roadway. In some embodiments, the standard-compliant GPS unit150is operable to identify, monitor and track its two-dimensional position within 1.5 meters of its actual position 68% of the time under an open sky. Since roadway lanes are typically no less than 3 meters wide, whenever the two-dimensional error of the GPS data is less than 1.5 meters the origin system199described herein may analyze the GPS data provided by the standard-compliant GPS unit150and determine what lane the ego vehicle123is traveling in based on the relative positions of two or more different vehicles (one of which is, for example, the ego vehicle123) traveling on a roadway at the same time.

By comparison to the standard-compliant GPS unit150, a conventional GPS unit which is not compliant with the DSRC standard is unable to determine the location of a vehicle (e.g., the ego vehicle123) with lane-level accuracy. For example, a typical roadway lane is approximately 3 meters wide. However, a conventional GPS unit only has an accuracy of plus or minus 10 meters relative to the actual location of the ego vehicle123. As a result, such conventional GPS units are not sufficiently accurate to enable the origin system199to determine the lane of travel of the ego vehicle123. This measurement improves the accuracy of the GPS data describing the location of lanes used by the ego vehicle123when the origin system199is providing its functionality.

In some embodiments, the memory127stores two types of GPS data. The first is GPS data of the ego vehicle123and the second is GPS data of one or more objects (e.g., the remote connected vehicle124or some other object in the roadway environment). The GPS data of the ego vehicle123is digital data that describes a geographic location of the ego vehicle123. The GPS data of the objects is digital data that describes a geographic location of an object. One or more of these two types of GPS data may have lane-level accuracy.

In some embodiments, one or more of these two types of GPS data are described by the ego sensor data195. For example, the standard-compliant GPS unit150is a sensor included in the sensor set126and the GPS data is an example type of ego sensor data195.

The communication unit145transmits and receives data to and from a network105or to another communication channel. In some embodiments, the communication unit145may include a DSRC transmitter, a DSRC receiver and other hardware or software necessary to make the ego vehicle123a DSRC-equipped device. In some embodiments, the origin system199is operable to control all or some of the operation of the communication unit145.

In some embodiments, the communication unit145includes a port for direct physical connection to the network105or to another communication channel. For example, the communication unit145includes a USB, SD, CAT-5, or similar port for wired communication with the network105. In some embodiments, the communication unit145includes a wireless transceiver for exchanging data with the network105or other communication channels using one or more wireless communication methods, including: IEEE 802.11; IEEE 802.16, BLUETOOTH®; EN ISO 14906:2004 Electronic Fee Collection—Application interface EN 11253:2004 Dedicated Short-Range Communication—Physical layer using microwave at 5.8 GHz (review); EN 12795:2002 Dedicated Short-Range Communication (DSRC)—DSRC Data link layer: Medium Access and Logical Link Control (review); EN 12834:2002 Dedicated Short-Range Communication—Application layer (review); EN 13372:2004 Dedicated Short-Range Communication (DSRC)—DSRC profiles for RTTT applications (review); the communication method described in U.S. patent application Ser. No. 14/471,387 filed on Aug. 28, 2014 and entitled “Full-Duplex Coordination System”; or another suitable wireless communication method.

In some embodiments, the communication unit145includes a radio that is operable to transmit and receive V2X messages via the network105. For example, the communication unit145includes a radio that is operable to transmit and receive any type of V2X communication described above for the network105.

In some embodiments, the communication unit145includes a full-duplex coordination system as described in U.S. Pat. No. 9,369,262 filed on Aug. 28, 2014 and entitled “Full-Duplex Coordination System,” the entirety of which is incorporated herein by reference. In some embodiments, some, or all of the communications necessary to execute the methods described herein are executed using full-duplex wireless communication as described in U.S. Pat. No. 9,369,262.

In some embodiments, the communication unit145includes a cellular communications transceiver for sending and receiving data over a cellular communications network including via short messaging service (SMS), multimedia messaging service (MMS), hypertext transfer protocol (HTTP), direct data connection, WAP, e-mail, or another suitable type of electronic communication. In some embodiments, the communication unit145includes a wired port and a wireless transceiver. The communication unit145also provides other conventional connections to the network105for distribution of files or media objects using standard network protocols including TCP/IP, HTTP, HTTPS, and SMTP, millimeter wave, DSRC, etc.

In some embodiments, the communication unit145includes a V2X radio. The V2X radio is a hardware unit that includes one or more transmitters and one or more receivers that is operable to send and receive any type of V2X message. In some embodiments, the V2X radio is a C-V2X radio that is operable to send and receive C-V2X messages. In some embodiments, the C-V2X radio is operable to send and receive C-V2X messages on the upper 30 MHz of the 5.9 GHz band (i.e., 5.895-5.925 GHz). In some embodiments, some or all of the wireless messages described above with reference to the method300depicted inFIG. 3are transmitted by the C-V2X radio on the upper 30 MHz of the 5.9 GHz band (i.e., 5.895-5.925 GHz) as directed by the origin system199.

In some embodiments, the V2X radio includes a DSRC transmitter and a DSRC receiver. The DSRC transmitter is operable to transmit and broadcast DSRC messages over the 5.9 GHz band. The DSRC receiver is operable to receive DSRC messages over the 5.9 GHz band. In some embodiments, the DSRC transmitter and the DSRC receiver operate on some other band which is reserved exclusively for DSRC.

In some embodiments, the V2X radio includes a non-transitory memory which stores digital data that controls the frequency for broadcasting BSMs or CPMs. In some embodiments, the non-transitory memory stores a buffered version of the GPS data for the ego vehicle123so that the GPS data for the ego vehicle123is broadcast as an element of the BSMs or CPMs which are regularly broadcast by the V2X radio (e.g., at an interval of once every 0.10 seconds).

In some embodiments, the V2X radio includes any hardware or software which is necessary to make the ego vehicle123compliant with the DSRC standards or any other wireless communication standard that applies to wireless vehicular communications. In some embodiments, the standard-compliant GPS unit150is an element of the V2X radio.

The memory127may include a non-transitory storage medium. The memory127may store instructions or data that may be executed by the processor125. The instructions or data may include code for performing the techniques described herein. The memory127may be a dynamic random-access memory (DRAM) device, a static random-access memory (SRAM) device, flash memory, or some other memory device. In some embodiments, the memory127also includes a non-volatile memory or similar permanent storage device and media including a hard disk drive, a floppy disk drive, a CD-ROM device, a DVD-ROM device, a DVD-RAM device, a DVD-RW device, a flash memory device, or some other mass storage device for storing information on a more permanent basis.

In some embodiments, the memory127may store any or all of the digital data or information described herein.

As depicted inFIG. 1, the memory127stores the following digital data: the ego sensor data195; the threshold data196; the member data171; the digital twin data162; the V2X data133; the GPS data (as an element of the ego sensor data195); the GUI data187; the analysis data181; the approved strategy data182; the selected strategy data186; the origin data184; the profile data183; the reference data188; the remote sensor data193; the time data154; the ego sensor data195; the time data155; and the profile update data172. The system data129includes some or all of this digital data. In some embodiments, the V2X messages (or C-V2X messages or the set of wireless messages) described herein are also stored in the memory127. The above-described elements of the memory127were described above, and so, those descriptions will not be repeated here.

In some embodiments, the ego vehicle123includes a vehicle control system153. A vehicle control system153includes one or more ADAS systems or an autonomous driving system. In some embodiments, the origin system199uses some or all of the payload of the set of wireless messages described herein as inputs to the vehicle control system153to improve the operation of the vehicle control system153by increasing the quantity of data it has access to when controlling the operation of the ego vehicle123.

Examples of an ADAS system include one or more of the following elements of a vehicle: an adaptive cruise control (“ACC”) system; an adaptive high beam system; an adaptive light control system; an automatic parking system; an automotive night vision system; a blind spot monitor; a collision avoidance system; a crosswind stabilization system; a driver drowsiness origin system; a driver monitoring system; an emergency driver assistance system; a forward collision warning system; an intersection assistance system; an intelligent speed adaption system; a lane keep assistance (“LKA”) system; a pedestrian protection system; a traffic sign recognition system; a turning assistant; and a wrong-way driving warning system. Other types of ADAS systems are possible. This list is illustrative and not exclusive.

An ADAS system is an onboard system that is operable to identify one or more factors (e.g., using one or more onboard vehicle sensors) affecting the ego vehicle123and modify (or control) the operation of its host vehicle (e.g., the ego vehicle123) to respond to these identified factors. Described generally, ADAS system functionality includes the process of (1) identifying one or more factors affecting the ego vehicle and (2) modifying the operation of the ego vehicle, or some component of the ego vehicle, based on these identified factors.

For example, an ACC system installed and operational in an ego vehicle may identify that a subject vehicle being followed by the ego vehicle with the cruise control system engaged has increased or decreased its speed. The ACC system may modify the speed of the ego vehicle based on the change in speed of the subject vehicle, and the detection of this change in speed and the modification of the speed of the ego vehicle is an example the ADAS system functionality of the ADAS system.

Similarly, an ego vehicle123may have a LKA system installed and operational in an ego vehicle123may detect, using one or more external cameras of the ego vehicle123, an event in which the ego vehicle123is near passing a center yellow line which indicates a division of one lane of travel from another lane of travel on a roadway. The LKA system may provide a notification to a driver of the ego vehicle123that this event has occurred (e.g., an audible noise or graphical display) or take action to prevent the ego vehicle123from actually passing the center yellow line such as making the steering wheel difficult to turn in a direction that would move the ego vehicle over the center yellow line or actually moving the steering wheel so that the ego vehicle123is further away from the center yellow line but still safely positioned in its lane of travel. The process of identifying the event and acting responsive to this event is an example of the ADAS system functionality provided by the LKA system.

The other ADAS systems described above each provide their own examples of ADAS system functionalities which are known in the art, and so, these examples of ADAS system functionality will not be repeated here.

In some embodiments, the ADAS system includes any software or hardware included in the vehicle that makes that vehicle be an autonomous vehicle or a semi-autonomous vehicle. In some embodiments, an autonomous driving system is a collection of ADAS systems which provides sufficient ADAS functionality to the ego vehicle123to render the ego vehicle123an autonomous or semi-autonomous vehicle.

An autonomous driving system includes a set of ADAS systems whose operation render sufficient autonomous functionality to render the ego vehicle123an autonomous vehicle (e.g., a Level III autonomous vehicle or higher as defined by the National Highway Traffic Safety Administration and the Society of Automotive Engineers).

In some embodiments, the origin system199includes code and routines that are operable, when executed by the processor125, to execute one or more steps of the example general method described herein. In some embodiments, the origin system199includes code and routines that are operable, when executed by the processor125, to execute one or more steps of the method300described below with reference toFIG. 3.

An example embodiment of the origin system199is depicted inFIG. 2. This embodiment is described in more detail below.

In some embodiments, the origin system199is an element of the onboard unit139or some other onboard vehicle computer. In some embodiments, the origin system199includes code and routines that are stored in the memory127and executed by the processor125or the onboard unit139. In some embodiments, the origin system199is an element of an onboard unit of the ego vehicle123which executes the origin system199and controls the operation of the communication unit145of the ego vehicle123based at least in part on the output from executing the origin system199.

In some embodiments, the origin system199is implemented using hardware including a field-programmable gate array (“FPGA”) or an application-specific integrated circuit (“ASIC”). In some other embodiments, the origin system199is implemented using a combination of hardware and software.

In some embodiments, the origin system199is an element of the cloud server103and not an element of the ego vehicle123or any other vehicle such as the remote connected vehicle124.

The remote connected vehicle124includes elements and functionality which are similar to those described above for the ego vehicle123, and so, those descriptions will not be repeated here. In some embodiments, the ego vehicle123and the remote connected vehicle124are members of a vehicular micro cloud194.

The roadway environment140is now described according to some embodiments. In some embodiments, some, or all of the ego vehicle123and the remote connected vehicle124(or a plurality of remote connected vehicles) are located in a roadway environment140. In some embodiments, the roadway environment140includes one or more vehicular micro clouds194. The roadway environment140is a portion of the real-world that includes a roadway, the ego vehicle123and the remote connected vehicle124. The roadway environment140may include other elements such as roadway signs, environmental conditions, traffic, etc. The roadway environment140includes some or all of the tangible and/or measurable qualities described above with reference to the ego sensor data195and the remote sensor data197. The remote sensor data197includes digital data that describes the sensor measurements recorded by the sensor set126of the remote connected vehicle124.

In some embodiments, the real-world includes the real of human experience comprising physical objects and excludes artificial environments and “virtual” worlds such as computer simulations.

In some embodiments, the roadway environment140includes a roadside unit that in includes an edge server198. In some embodiments, the edge server198is a connected processor-based computing device that includes an instance of the origin system199and the other elements described above with reference to the ego vehicle123(e.g., a processor125, a memory127storing the system data129, a communication unit145, etc.). In some embodiments, the roadway device is a member of the vehicular micro cloud194.

In some embodiments, the edge server198includes one or more of the following: a hardware server; a personal computer; a laptop; a device such as a roadside unit; or any other processor-based connected device that is not a member of the vehicular micro cloud194and includes an instance of the origin system199and a non-transitory memory that stores some or all of the digital data that is stored by the memory127of the ego vehicle123or otherwise described herein. For example, the memory127stores the system data129. The system data129includes some or all of the digital data depicted inFIG. 1as being stored by the memory127.

In some embodiments, the edge server198includes a backbone network. In some embodiments, the edge server198includes an instance of the origin system199. The functionality of the origin system199is described above with reference to the ego vehicle123, and so, that description will not be repeated here.

In some embodiments, the cloud server103one or more of the following: a hardware server; a personal computer; a laptop; a device such as a roadside unit; or any other processor-based connected device that is not a member of the vehicular micro cloud194and includes an instance of the origin system199and a non-transitory memory that stores some or all of the digital data that is stored by the memory127of the ego vehicle123or otherwise described herein. For example, the memory127stores the system data129. In some embodiments, the cloud server103is operable to enable a driver109to provide their profile data183and/or approved strategy data182, receive requests for profile data183, respond to these requests with appropriate profile data183for a driver, receive requests to update profile data183with profile update data172, and help an origin system199to implement a selected strategy. The cloud server103is operable to provide any other functionality described herein. For example, the cloud server103is operable to execute some or all of the steps of the methods described herein.

In some embodiments, the cloud server103includes a data structure131. The data structure131includes a non-transitory memory that stores an organized set of digital data. For example, the data structure131includes an organized set of profile data183for a plurality of different drivers (e.g., the drivers of the ego vehicle123and the remote connected vehicle124).

In some embodiments, the vehicular micro cloud194is stationary. In other words, in some embodiments the vehicular micro cloud194is a “stationary vehicular micro cloud.” A stationary vehicular micro cloud is a wireless network system in which a plurality of connected vehicles (such as the ego vehicle123, the remote connected vehicle124, etc.), and optionally devices such as a roadway device, form a cluster of interconnected vehicles that are located at a same geographic region. These connected vehicles (and, optionally, connected devices) are interconnected via C-V2X, Wi-Fi, mmWave, DSRC or some other form of V2X wireless communication. For example, the connected vehicles are interconnected via a V2X network which may be the network105or some other wireless network that is only accessed by the members of the vehicular micro cloud194and not non-members such as the cloud server103. Connected vehicles (and devices such as a roadside unit) which are members of the same stationary vehicular micro cloud make their unused computing resources available to the other members of the stationary vehicular micro cloud.

In some embodiments, the vehicular micro cloud194is “stationary” because the geographic location of the vehicular micro cloud194is static; different vehicles constantly enter and exit the vehicular micro cloud194over time. This means that the computing resources available within the vehicular micro cloud194is variable based on the traffic patterns for the geographic location at different times of day: increased traffic corresponds to increased computing resources because more vehicles will be eligible to join the vehicular micro cloud194; and decreased traffic corresponds to decreased computing resources because less vehicles will be eligible to join the vehicular micro cloud194.

In some embodiments, the V2X network is a non-infrastructure network. A non-infrastructure network is any conventional wireless network that does not include infrastructure such as cellular towers, servers, or server farms. For example, the V2X network specifically does not include a mobile data network including third generation (3G), fourth generation (4G), fifth generation (5G), long-term evolution (LTE), Voice-over-LTE (VoLTE) or any other mobile data network that relies on infrastructure such as cellular towers, hardware servers or server farms.

In some embodiments, the non-infrastructure network includes Bluetooth® communication networks for sending and receiving data including via one or more of DSRC, mmWave, full-duplex wireless communication and any other type of wireless communication that does not include infrastructure elements. The non-infrastructure network may include vehicle-to-vehicle communication such as a Wi-Fi™ network shared among two or more vehicles123,124.

In some embodiments, the wireless messages described herein are encrypted themselves or transmitted via an encrypted communication provided by the network105. In some embodiments, the network105may include an encrypted virtual private network tunnel (“VPN tunnel”) that does not include any infrastructure components such as network towers, hardware servers or server farms. In some embodiments, the origin system199includes encryption keys for encrypting wireless messages and decrypting the wireless messages described herein.

Referring now toFIG. 2, depicted is a block diagram illustrating an example computer system200including an origin system199according to some embodiments.

In some embodiments, the computer system200may include a special-purpose computer system that is programmed to perform one or more steps of one or more of the method300described herein with reference toFIG. 3and the example general method described herein.

In some embodiments, the computer system200may include a processor-based computing device. For example, the computer system200may include an onboard vehicle computer system of the ego vehicle123or the remote connected vehicle124.

The computer system200may include one or more of the following elements according to some examples: the origin system199; a processor125; a communication unit145; a vehicle control system153; a storage241; and a memory127. The components of the computer system200are communicatively coupled by a bus220.

In some embodiments, the computer system200includes additional elements such as those depicted inFIG. 1as elements of the origin system199.

In the illustrated embodiment, the processor125is communicatively coupled to the bus220via a signal line237. The communication unit145is communicatively coupled to the bus220via a signal line246. The vehicle control system153is communicatively coupled to the bus220via a signal line247. The storage241is communicatively coupled to the bus220via a signal line242. The memory127is communicatively coupled to the bus220via a signal line244. The sensor set126is communicatively coupled to the bus220via a signal line248.

In some embodiments, the sensor set126includes standard-compliant GPS unit. In some embodiments, the communication unit145includes a sniffer.

The following elements of the computer system200were described above with reference toFIG. 1, and so, these descriptions will not be repeated here: the processor125; the communication unit145; the vehicle control system153; the memory127; and the sensor set126.

The storage241can be a non-transitory storage medium that stores data for providing the functionality described herein. The storage241may be a DRAM device, a SRAM device, flash memory, or some other memory devices. In some embodiments, the storage241also includes a non-volatile memory or similar permanent storage device and media including a hard disk drive, a floppy disk drive, a CD-ROM device, a DVD-ROM device, a DVD-RAM device, a DVD-RW device, a flash memory device, or some other mass storage device for storing information on a more permanent basis.

In some embodiments, the origin system199includes code and routines that are operable, when executed by the processor125, to cause the processor125to execute one or more steps of the method300described herein with reference toFIG. 3. In some embodiments, the origin system199includes code and routines that are operable, when executed by the processor125, to cause the processor125to execute one or more steps of the example general method.

In the illustrated embodiment shown inFIG. 2, the origin system199includes a communication module202.

The communication module202can be software including routines for handling communications between the origin system199and other components of the computer system200. In some embodiments, the communication module202can be a set of instructions executable by the processor125to provide the functionality described below for handling communications between the origin system199and other components of the computer system200. In some embodiments, the communication module202can be stored in the memory127of the computer system200and can be accessible and executable by the processor125. The communication module202may be adapted for cooperation and communication with the processor125and other components of the computer system200via signal line222.

The communication module202sends and receives data, via the communication unit145, to and from one or more elements of the operating environment100.

In some embodiments, the communication module202receives data from components of the origin system199and stores the data in one or more of the storage241and the memory127.

In some embodiments, the communication module202may handle communications between components of the origin system199or the computer system200.

Referring now toFIG. 3, depicted is a flowchart of an example method300. The method300includes step305, step310, step315, and step320as depicted inFIG. 3. The steps of the method300may be executed in any order, and not necessarily those depicted inFIG. 3. In some embodiments, one or more of the steps are skipped or modified in ways that are described herein or known or otherwise determinable by those having ordinary skill in the art of vehicular micro clouds.

Example differences in technical effect between the method300and the prior art are described below. These examples are illustrative and not exhaustive of the possible differences.

The existing solutions do not determine the origin of abnormal driving behavior and implement strategies to reduce, minimize, or eliminate the origin so that the abnormal driving behavior does not occur in the future.

The existing solutions do not utilize vehicular micro clouds to implement a Service. The existing solutions also do not use digital twin simulations or other methods described herein to determine origin data and or selected strategy data.

The existing references also do not describe vehicular micro clouds as described herein. Some of the existing solutions require the use of vehicle platooning. A platoon is not a vehicular micro cloud and does not provide the benefits of a vehicular micro cloud, and some embodiments of the origin system that require a vehicular micro cloud. For example, among various differences between a platoon and a vehicular micro cloud, a platoon does not include a hub or a vehicle that provides the functionality of a hub vehicle. By comparison, in some embodiments the origin system includes codes and routines that are operable, when executed by a processor, to cause the processor to utilize vehicular micro clouds to resolve version differences among common vehicle applications installed in different connected vehicles.

Referring now toFIG. 4, depicted is a block diagram of a happens-before analysis400for a first use case according to some embodiments.

In the first use case either the ego vehicle123itself or nearby remote connected vehicles124A,124B,124C (i.e., those that are in sensor detection range of the ego vehicle123) detect abnormal driving behavior (e.g., swerving) by the ego vehicle123which is operated by a driver named Ava.

A naïve approach to solving this problem can be measuring the lane centering property of Ava. When the lane centering measurement deviates a lot, the system can conclude that Ava gets distracted. The origin system does not operate in this manner. Instead of correcting the abnormal driving behavior directly, the origin system determines the origin of the abnormal driving behavior and selects a strategy that reduces, minimizes, or removes the origin from future driving experiences of the driver so that the abnormal driving behavior does not occur for the same reason so that in the future.

In some embodiments, Δt can be a value which is predefined according to type of unsafe driving behavior. For example, our research indicates that driver distraction generally happens in couple of seconds. Our research indicates that setting Δt to 5-10 seconds will reveal the origin/cause behind the distraction. In some embodiments, Δt can be dynamically set a value according to driver behavior and/or current traffic condition.

Referring now toFIG. 5, depicted is a block diagram of a cause-and-effect analysis500for the first use case according to some embodiments.

The origin system of the ego vehicle123executes a happens-before relationship analysis and cause-and-effect analysis to infer the origin of Ava's abnormal driving behavior.

FIG. 5is a continuation of the analysis of the first use case by the origin system introduced withFIG. 4. Accordingly,FIGS. 4 and 5can be read together for a more complete understanding of the analysis provided by the origin system in some embodiments. After executing the happens-before relationship analysis depicted inFIG. 4, the origin system executes a cause-and-effect analysis as depicted inFIG. 5according to some embodiments. In this analysis the origin system causes the processor to execute code and routines that are configured to determine the origin of the abnormal driving behavior (e.g., Ava's distracted driving behavior) through cause-and-effect analysis.

The origin system includes code and routines that are operable, when executed by the processor, to cause the processor to execute one or more of the following steps: includes one or more of the following steps: (1) analyzing the happens-before relationship diagram described by the analysis data by grouping the factor data chronologically in groups of one-by-one (e.g., one factor by one factor), two-by-two (e.g., two factors by two factors), and N-by-N; (2) analyzing the groups of factors in chronological order relative to one another to determine which factors precipitated one another (e.g., determining the cause-and-effect relationships among the factors relative to one another chronologically), including, or in some embodiments with exclusive emphasis on, the factors that precipitated the abnormal driving behavior at time “T”; (3) constructing one or more cause-and-effect diagrams based at least in part on the on the analysis of step 2; (4) analyzing the cause-and-effect diagrams to determine the origin of the abnormal driving behavior diagram; and (5) outputting origin data describing the origin of the abnormal driving behavior.

Here, in this example the origin system causes the processor to group the factors one-by-one and a retrieve the set of time “Δt” used in the happens-before relationship analysis depicted inFIG. 4, which in this example was 5 seconds before the time “T.” In step 4 above, this analysis includes, in some embodiments, the origin system analyzing Ava's driving history (which is an element of the historical data) to determine if Ava has demonstrated abnormal driving behavior at this same location in the past under the same factors found in the time range defined by “T-Δt.” In this way, the factors behind Ava's the abnormal driving behavior are analyzed to determine a chain of cause-and-effect relationships that chronologically lead to the abnormal driving behavior. For example, beginning at the abnormal driving behavior, the origin system works backwards in time to construct a cause-and-effect chain that terminates at a factor (or a set of factors) which has no other factor which caused it; this factor is the terminal end of the cause-and-effect chain. The terminal end of this chain that is opposite the abnormal driving behavior is determined by the origin system to be the origin of the abnormal driving behavior. The origin data includes digital data that describes the set of factors (e.g., one or more factors) that is at this terminal end of the cause-and-effect chain. The analysis data includes digital data that describes this cause-and-effect chain. In some embodiments, this analysis data and the origin data are shared with the cloud server and/or the edge server via V2X communications initiated by the origin system to transmit a V2X message whose V2X data includes this analysis data and the origin data.

The origin system selects a strategy from the pre-approved strategies which is known to reduce, minimize, or eliminate the factor which was the origin of the abnormal driving behavior sufficient so that the abnormal driving behavior does not reoccur in the future because of this same set of factors.

For example, in the example depicted inFIG. 5the origin of Ava's distracted driving is that she becomes a distracted driver whenever the following set of factors is present: Ava receives a phone call; Ava talks on her phone; and Ava is driving at a speed that exceeds 55 miles per hour. The selected strategy is, in this example, the following: the origin system controls an ADAS systems of the ego vehicle to slow down the speed of Ava's vehicle below 55 miles per hour. Another possible selected strategy is that, if Ava's phone is connected to the ego vehicle's infotainment system via Bluetooth or USB, then the origin system detects phone calls that are received by Ava's phone and diverts them to a voicemail system so that Ava is not aware that she received the phone call.

Referring now toFIG. 6, depicted is a block diagram of a happens-before analysis600for a second use case according to some embodiments. The origin system, when executed by the processor, causes the processor to execute a happens-before relationship analysis and generate the happens-before relationship diagram depicted inFIG. 6. This diagram is described by the analysis data that is outputted by the happens-before relationship analysis.

Referring now toFIG. 7, depicted is a block diagram of a cause-and-effect analysis700for the second use case according to some embodiments.FIG. 7is a continuation of the analysis of the second use case by the origin system introduced withFIG. 6. Accordingly,FIGS. 6 and 7can be read together for a more complete understanding of the analysis provided by the origin system in some embodiments.

The origin system, when executed by the processor, causes the processor to execute a cause-and-effect analysis and conclude that missing navigation instruction followed by the new navigation suggestion makes Ava drive abnormally like a distracted driver. The cause-and-effect chain indicates that the origin of Ava's abnormal driving behavior is navigation confusion.

When selecting a strategy, the origin system focuses on the origin behind Ava's abnormal driving behavior and generates to reduce, minimize, or completely remove the origin of Ava's abnormal driving behavior so that Ava does not demonstrate this same abnormal driving behavior in the future under similar factors.

The origin system determines that Ava gets distracted when she misses a navigation instruction given by the ego vehicles navigation system and the navigation system re-routes her trip. The origin system determines that the selected strategy is to modify the operation of the navigation system so that it provides Ava with micro-level navigation instructions so that Ava is less likely to miss a navigation instruction or become confused by her navigation instructions, thereby completely removing the origin of Ava's abnormal driving behavior.

Reference in the specification to “some embodiments” or “some instances” means that a particular feature, structure, or characteristic described in connection with the embodiments or instances can be included in at least one embodiment of the description. The appearances of the phrase “in some embodiments” in various places in the specification are not necessarily all referring to the same embodiments.

The specification can take the form of some entirely hardware embodiments, some entirely software embodiments or some embodiments containing both hardware and software elements. In some preferred embodiments, the specification is implemented in software, which includes, but is not limited to, firmware, resident software, microcode, etc.

Network adapters may also be coupled to the system to enable the origin system to become coupled to other origin systems or remote printers or storage devices through intervening private or public networks. Modems, cable modem, and Ethernet cards are just a few of the currently available types of network adapters.