Methods and systems to autonomously train drivers

In accordance with an exemplary embodiment, a method is provided for training a trainee using an autonomous vehicle, the method including: measuring, via one or more sensors, one or more manual inputs from the trainee with respect to controlling the autonomous vehicle; determining, via a processor using an autonomous driving algorithm stored in a memory of the autonomous vehicle, one or more recommended actions for the autonomous vehicle; comparing, via the processor, the one or more manual inputs from the trainee with the one or more recommended actions for the autonomous vehicle, generating a comparison; and determining, via the processor, a score for the trainee based on the comparison between the one or more manual inputs from the trainee with the one or more recommended actions for the autonomous vehicle.

TECHNICAL FIELD

The present disclosure generally relates to vehicles, and more particularly relates to systems and methods for training drivers using autonomous vehicles.

BACKGROUND

An autonomous vehicle is a vehicle that is capable of sensing its environment and navigating with little or no user input. It does so by using sensing devices such as radar, lidar, image sensors, and the like. Autonomous vehicles further use information from global positioning systems (GPS) technology, navigation systems, vehicle-to-vehicle communication, vehicle-to-infrastructure technology, and/or drive-by-wire systems to navigate the vehicle.

While autonomous vehicles offer many potential advantages over traditional vehicles, in certain circumstances it may be desirable to train humans to be able to drive vehicle without autonomous assistance for the vehicle. For example, in various embodiments, a human may wish to drive for personal satisfaction, and/or may need to drive in certain situations, for example, if the human may be in a situation at some point in the future in which an operational autonomous vehicle may not be available or permitted in a certain location or situation.

Current techniques for training humans for driving include a human instructor. However, in certain situations, typical techniques using a human instructor may not always be optimal, for example as this may introduce biases of the human instructor, and/or may be more time consuming, costly, and/or difficult to schedule, and/or may include risks and/or inefficiencies, in certain situations.

Accordingly, it is desirable to provide systems and methods for training of human drivers of vehicles. Furthermore, other desirable features and characteristics of the present invention will become apparent from the subsequent detailed description of the invention and the appended claims, taken in conjunction with the accompanying drawings and this background of the invention.

SUMMARY

In accordance with an exemplary embodiment, a method is provided for training a trainee using an autonomous vehicle, the method including: measuring, via one or more sensors, one or more manual inputs from the trainee with respect to controlling the autonomous vehicle; determining, via a processor using an autonomous driving algorithm stored in a memory of the autonomous vehicle, one or more recommended actions for the autonomous vehicle; comparing, via the processor, the one or more manual inputs from the trainee with the one or more recommended actions for the autonomous vehicle, generating a comparison; and determining, via the processor, a score for the trainee based on the comparison between the one or more manual inputs from the trainee with the one or more recommended actions for the autonomous vehicle.

Also in an exemplary embodiment, the step of measuring manual inputs includes measuring, via one or more sensors, one or more manual inputs from the trainee in engaging an accelerator pedal for the autonomous vehicle; the step of comparing the manual inputs with the recommended actions includes comparing, via the processor, the one or more manual inputs from the trainee in engaging the accelerator pedal with the one or more recommended actions for the autonomous vehicle, generating the comparison; and the step of determining the score includes determining, via the processor, the score for the trainee based on the comparison between the one or more manual inputs from the trainee in engaging the accelerator pedal with the one or more recommended actions for the autonomous vehicle.

Also in an exemplary embodiment, the step of measuring manual inputs includes measuring, via one or more sensors, one or more manual inputs from the trainee in engaging a steering wheel for the autonomous vehicle; the step of comparing the manual inputs with the recommended actions includes comparing, via the processor, the one or more manual inputs from the trainee in engaging the steering wheel with the one or more recommended actions for the autonomous vehicle, generating the comparison; and the step of determining the score includes determining, via the processor, the score for the trainee based on the comparison between the one or more manual inputs from the trainee in engaging the steering wheel with the one or more recommended actions for the autonomous vehicle.

Also in an exemplary embodiment, the step of measuring manual inputs includes measuring, via one or more sensors, one or more manual inputs from the trainee in engaging a brake pedal for the autonomous vehicle; the step of comparing the manual inputs with the recommended actions includes comparing, via the processor, the one or more manual inputs from the trainee in engaging the brake pedal with the one or more recommended actions for the autonomous vehicle, generating the comparison; and the step of determining the score includes determining, via the processor, the score for the trainee based on the comparison between the one or more manual inputs from the trainee in engaging the brake pedal with the one or more recommended actions for the autonomous vehicle.

Also in an exemplary embodiment, the method further includes providing instantaneous feedback for the trainee based on the comparison between the one or more manual inputs from the trainee with the one or more recommended actions for the autonomous vehicle.

Also in an exemplary embodiment, the method further includes providing results associated with the score for the trainee to one or more third parties.

Also in an exemplary embodiment, the method further includes: determining whether the score for the trainee exceeds one or more predetermined thresholds; and selectively allowing the trainee to control one or more features of driving functionality for the autonomous vehicle.

Also in an exemplary embodiment, the step of selectively allowing the trainee to control one or more features of the vehicle includes: selectively allowing the trainee to control certain features of driving functionality for the autonomous vehicle, based on whether the score exceeds one or more first thresholds; and automatically controlling certain other features of driving functionality, via instructions provided by the processor in accordance with the autonomous driving algorithm, based on whether the score exceeds one or more second thresholds that are different from the first thresholds.

In another exemplary embodiment, A system for training a trainee using an autonomous vehicle, the system including: one or more sensors configured to measure manual inputs from the trainee with respect to controlling the autonomous vehicle; a non-transitory computer readable memory configured to store an autonomous driving algorithm for the autonomous vehicle; and a processor coupled to the one or more sensors and to the non-transitory computer readable memory and configured to at least facilitate: determining, using the autonomous driving algorithm, one or more recommended actions for the autonomous vehicle; comparing the one or more manual inputs from the trainee with the one or more recommended actions for the autonomous vehicle, generating a comparison; and determining a score for the trainee based on the comparison between the one or more manual inputs from the trainee with the one or more recommended actions for the autonomous vehicle.

Also in an exemplary embodiment, the one or more sensors are configured to measure the one or more manual inputs from the trainee in engaging an accelerator pedal for the autonomous vehicle; and the processor is configured to at least facilitate: comparing the one or more manual inputs from the trainee in engaging the accelerator pedal with the one or more recommended actions for the autonomous vehicle, generating the comparison; and determining the score for the trainee based on the comparison between the one or more manual inputs from the trainee in engaging the accelerator pedal with the one or more recommended actions for the autonomous vehicle.

Also in an exemplary embodiment, the one or more sensors are configured to measure the one or more manual inputs from the trainee in engaging a steering wheel for the autonomous vehicle; and the processor is configured to at least facilitate: comparing the one or more manual inputs from the trainee in engaging the steering wheel with the one or more recommended actions for the autonomous vehicle, generating the comparison; and determining the score for the trainee based on the comparison between the one or more manual inputs from the trainee in engaging the steering wheel with the one or more recommended actions for the autonomous vehicle.

Also in an exemplary embodiment, the one or more sensors are configured to measure the one or more manual inputs from the trainee in engaging a brake pedal for the autonomous vehicle; and the processor is configured to at least facilitate: comparing the one or more manual inputs from the trainee in engaging the brake pedal with the one or more recommended actions for the autonomous vehicle, generating the comparison; and determining the score for the trainee based on the comparison between the one or more manual inputs from the trainee in engaging the brake pedal with the one or more recommended actions for the autonomous vehicle.

Also in an exemplary embodiment, the processor is further configured to at least facilitate providing instantaneous feedback for the trainee based on the comparison between the one or more manual inputs from the trainee with the one or more recommended actions for the autonomous vehicle.

Also in an exemplary embodiment, the processor is further configured to at least facilitate providing results associated with the score for the trainee to one or more third parties.

Also in an exemplary embodiment, the processor is further configured to at least facilitate: determining whether the score for the trainee exceeds one or more predetermined thresholds; and selectively allowing the trainee to control one or more features of driving functionality for the autonomous vehicle.

Also in an exemplary embodiment, the processor is further configured to at least facilitate: selectively allowing the trainee to control certain features of driving functionality for the autonomous vehicle, based on whether the score exceeds one or more first thresholds; and automatically controlling certain other features of driving functionality, via instructions provided by the processor in accordance with the autonomous driving algorithm, based on whether the score exceeds one or more second thresholds that are different from the first thresholds.

In another exemplary embodiment, an autonomous vehicle for training a trainee is provided that includes: an accelerator pedal; a brake pedal; a steering wheel; a plurality of sensors configured to measure manual inputs from the trainee with respect to controlling the autonomous vehicle via the trainee's engagement of the accelerator pedal, brake pedal, and steering wheel; a non-transitory computer readable memory configured to store an autonomous driving algorithm for the autonomous vehicle; and a processor coupled to the one or more sensors and to the non-transitory computer readable memory and configured to at least facilitate: determining, using the autonomous driving algorithm, one or more recommended actions for the autonomous vehicle; comparing the one or more manual inputs from the trainee with the one or more recommended actions for the autonomous vehicle, generating a comparison; and determining a score for the trainee based on the comparison between the one or more manual inputs from the trainee with the one or more recommended actions for the autonomous vehicle.

Also in an exemplary embodiment, the processor is further configured to at least facilitate: providing instantaneous feedback for the trainee based on the comparison between the one or more manual inputs from the trainee with the one or more recommended actions for the autonomous vehicle; and providing results associated with the score for the trainee to one or more third parties.

Also in an exemplary embodiment, the processor is further configured to at least facilitate: determining whether the score for the trainee exceeds one or more predetermined thresholds; and selectively allowing the trainee to control one or more features of driving functionality for the autonomous vehicle.

Also in an exemplary embodiment, the processor is further configured to at least facilitate: selectively allowing the trainee to control certain features of driving functionality for the autonomous vehicle, based on whether the score exceeds one or more first thresholds; and automatically controlling certain other features of driving functionality, via instructions provided by the processor in accordance with the autonomous driving algorithm, based on whether the score exceeds one or more second thresholds that are different from the first thresholds.

DETAILED DESCRIPTION

The following detailed description is merely exemplary in nature and is not intended to limit the application and uses. Furthermore, there is no intention to be bound by any expressed or implied theory presented in the preceding technical field, background, brief summary, or the following detailed description. As used herein, the term “module” refers to any hardware, software, firmware, electronic control component, processing logic, and/or processor device, individually or in any combination, including without limitation: application specific integrated circuit (ASIC), a field-programmable gate-array (FPGA), an electronic circuit, a processor (shared, dedicated, or group) and memory that executes one or more software or firmware programs, a combinational logic circuit, and/or other suitable components that provide the described functionality.

With reference toFIG.1, a driver training control system100shown generally as100is associated with a vehicle10in accordance with various embodiments. In general, the driver training control system (or simply “system”)100provides training of humans for driving, using the vehicle10. In various embodiments, the vehicle10comprises an autonomous vehicle.

As depicted inFIG.1, the vehicle10generally includes a chassis12, a body14, front wheels16, and rear wheels18. The body14is arranged on the chassis12and substantially encloses components of the vehicle10. The body14and the chassis12may jointly form a frame. The wheels16-18are each rotationally coupled to the chassis12near a respective corner of the body14. While an automotive vehicle is depicted inFIG.1, it will be appreciated that in various embodiments the vehicle10may comprise any number of other types of vehicles (such as, by way of example, boats, other marine vehicles, trains, aircraft, and/or spacecraft) and/or one or more other types of mobile platforms.

In various embodiments, the vehicle10is an autonomous vehicle and the driver training control system100, and/or components thereof, are incorporated into the autonomous vehicle10(hereinafter referred to as the autonomous vehicle10). The autonomous vehicle10is, for example, a vehicle that is automatically controlled to carry passengers from one location to another. The vehicle10is depicted in the illustrated embodiment as a passenger car, but it should be appreciated that any other vehicle, including motorcycles, trucks, sport utility vehicles (SUVs), recreational vehicles (RVs), marine vessels, aircraft, and the like, can also be used. As described in further greater further below, the vehicle10is also configured to train humans how to drive a vehicle, for example so that the human is able, once trained, to drive a vehicle without relying on the autonomous vehicle functionality.

In an exemplary embodiment, the autonomous vehicle10corresponds to a level four or level five automation system under the Society of Automotive Engineers (SAE) “J3016” standard taxonomy of automated driving levels. Using this terminology, a level four system indicates “high automation,” referring to a driving mode in which the automated driving system performs all aspects of the dynamic driving task, even if a human driver does not respond appropriately to a request to intervene. A level five system, on the other hand, indicates “full automation,” referring to a driving mode in which the automated driving system performs all aspects of the dynamic driving task under all roadway and environmental conditions that can be managed by a human driver. It will be appreciated, however, the embodiments in accordance with the present subject matter are not limited to any particular taxonomy or rubric of automation categories. Furthermore, systems in accordance with the present embodiment may be used in conjunction with any autonomous or other vehicle that utilizes a navigation system and/or other systems to provide route guidance and/or implementation.

As shown, the autonomous vehicle10generally includes a propulsion system20, a transmission system22, a steering system24, a brake system26, a sensor system28, an actuator system30, at least one data storage device32, at least one controller34, and a communication system36.

The propulsion system20may, in various embodiments, include an internal combustion engine, an electric machine such as a traction motor, and/or a fuel cell propulsion system. In various embodiments, the propulsion system20has or is coupled to an accelerator pedal21for a driver (and/or trainee) to provide manual acceleration inputs for the propulsion system20for proving acceleration for the vehicle10.

The transmission system22is configured to transmit power from the propulsion system20to the vehicle wheels16and18according to selectable speed ratios. According to various embodiments, the transmission system22may include a step-ratio automatic transmission, a continuously-variable transmission, or other appropriate transmission.

The brake system26is configured to provide braking torque to the vehicle wheels16and18. Brake system26may, in various embodiments, include friction brakes, brake by wire, a regenerative braking system such as an electric machine, and/or other appropriate braking systems. In various embodiments, the brake system26includes a brake pedal27for a driver (and/or trainee) to provide manual braking inputs for the brake system26.

The steering system24influences a position of the vehicle wheels16and/or18. In various embodiments, the steering system24includes a steering wheel25for a driver (and/or trainee) to provide manual steering inputs for the steering system24.

The sensor system28includes one or more sensing devices40a-40nthat sense observable conditions of the exterior environment and/or the interior environment of the autonomous vehicle10. The sensing devices40a-40nmight include, but are not limited to, brake pedal sensors, steering wheel sensors, accelerator pedal sensors, radars, lidars, global positioning systems, optical cameras, thermal cameras, ultrasonic sensors, turn signals and sensors therefor, cameras and/or other sensors for watching drivers (e.g., for correct mirror use, and so on) and/or other sensors. The actuator system30includes one or more actuator devices42a-42nthat control one or more vehicle features of the vehicle10. In various embodiments, the actuator devices42a-42n(also referred to as the actuators42) control one or more features such as, but not limited to, the propulsion system20, the transmission system22, the steering system24, and the brake system26. In various embodiments, autonomous vehicle10may also include interior and/or exterior vehicle features not illustrated inFIG.1, such as a trunk, and cabin features such as air, music, lighting, touch-screen display components (such as those used in connection with navigation systems), and the like.

The data storage device32stores data for use in automatically controlling the autonomous vehicle10and for conducting driver training using the autonomous vehicle10. In various embodiments, the data storage device32stores information pertaining to scoring of a current driver's (or trainee's) driving, including based on comparisons between the driver's (or trainee's) manual driving inputs as compared with the driving inputs recommended by the autonomous functionality of the vehicle10.

In addition, in various embodiments, the data storage device32stores defined maps of the navigable environment. In various embodiments, the defined maps may be predefined by and obtained from a remote system (described in further detail with regard toFIG.2). For example, the defined maps may be assembled by the remote system and communicated to the autonomous vehicle10(wirelessly and/or in a wired manner) and stored in the data storage device32. Route information may also be stored within data device32—i.e., a set of road segments (associated geographically with one or more of the defined maps) that together define a route that the user may take to travel from a start location (e.g., the user's current location) to a target location. Also in various embodiments, the data storage device32stores data pertaining to roadways on which the vehicle10may be travelling. As will be appreciated, the data storage device32may be part of the controller34, separate from the controller34, or part of the controller34and part of a separate system.

In various embodiments, the processor44provides driver training for a human driver based on scoring of a current driver's (or trainee's) driving, including based on comparisons between the driver's (or trainee's) manual driving inputs as compared with the driving inputs recommended by the autonomous functionality of the vehicle10.

The instructions may include one or more separate programs, each of which comprises an ordered listing of executable instructions for implementing logical functions. The instructions, when executed by the processor44, receive and process signals from the sensor system28, perform logic, calculations, methods and/or algorithms for the driver training as well as for automatically controlling the components of the autonomous vehicle10, and generate control signals that are transmitted to the actuator system30to automatically control the components of the autonomous vehicle10based on the logic, calculations, methods, and/or algorithms. Although only one controller34is shown inFIG.1, embodiments of the autonomous vehicle10may include any number of controllers34that communicate over any suitable communication medium or a combination of communication mediums and that cooperate to process the sensor signals, perform logic, calculations, methods, and/or algorithms, and generate control signals to automatically control features of the autonomous vehicle10. In one embodiment, as discussed in detail below, the controller34is configured for use in providing driver training using the autonomous vehicle10.

With reference now toFIG.2, in various embodiments, the autonomous vehicle10described with regard toFIG.1may be suitable for use in the context of a group or fleet of vehicles or may simply be managed by a remote system. For example, the autonomous vehicle10may be associated with a group or fleet of autonomous vehicles for training student drivers (and, in certain embodiments, also for transportation).FIG.2illustrates an exemplary embodiment of an operating environment shown generally at50that includes an autonomous vehicle based remote system (or simply “training and transportation system”)52that is associated with one or more autonomous vehicles10a-10nas described with regard toFIG.1. In various embodiments, the operating environment50(all or a part of which may correspond to entities48shown inFIG.1) further includes one or more user devices54that communicate with the autonomous vehicle10and/or the training and transportation system52via a communication network56.

The communication network56supports communication as needed between devices, systems, and components supported by the operating environment50(e.g., via tangible communication links and/or wireless communication links). For example, the communication network56may include a wireless carrier system60such as a cellular telephone system that includes a plurality of cell towers (not shown), one or more mobile switching centers (MSCs) (not shown), as well as any other networking components required to connect the wireless carrier system60with a land communications system. Each cell tower includes sending and receiving antennas and a base station, with the base stations from different cell towers being connected to the MSC either directly or via intermediary equipment such as a base station controller. The wireless carrier system60can implement any suitable communications technology, including for example, digital technologies such as CDMA (e.g., CDMA2000), LTE (e.g., 4G LTE or 5G LTE), GSM/GPRS, or other current or emerging wireless technologies. Other cell tower/base station/MSC arrangements are possible and could be used with the wireless carrier system60. For example, the base station and cell tower could be co-located at the same site or they could be remotely located from one another, each base station could be responsible for a single cell tower or a single base station could service various cell towers, or various base stations could be coupled to a single MSC, to name but a few of the possible arrangements.

Apart from including the wireless carrier system60, a second wireless carrier system in the form of a satellite communication system64can be included to provide uni-directional or bi-directional communication with the autonomous vehicles10a-10n. This can be done using one or more communication satellites (not shown) and an uplink transmitting station (not shown). Uni-directional communication can include, for example, satellite radio services, wherein programming content (news, music, and the like) is received by the transmitting station, packaged for upload, and then sent to the satellite, which broadcasts the programming to subscribers. Bi-directional communication can include, for example, satellite telephony services using the satellite to relay telephone communications between the vehicle10and the station. The satellite telephony can be utilized either in addition to or in lieu of the wireless carrier system60.

A land communication system62may further be included that is a conventional land-based telecommunications network connected to one or more landline telephones and connects the wireless carrier system60to the training and transportation system52. For example, the land communication system62may include a public switched telephone network (PSTN) such as that used to provide hardwired telephony, packet-switched data communications, and the Internet infrastructure. One or more segments of the land communication system62can be implemented through the use of a standard wired network, a fiber or other optical network, a cable network, power lines, other wireless networks such as wireless local area networks (WLANs), or networks providing broadband wireless access (BWA), or any combination thereof. Furthermore, the training and transportation system52need not be connected via the land communication system62, but can include wireless telephony equipment so that it can communicate directly with a wireless network, such as the wireless carrier system60.

Although only one user device54is shown inFIG.2, embodiments of the operating environment50can support any number of user devices54, including multiple user devices54owned, operated, or otherwise used by one person. Each user device54supported by the operating environment50may be implemented using any suitable hardware platform. In this regard, the user device54can be realized in any common form factor including, but not limited to: a desktop computer; a mobile computer (e.g., a tablet computer, a laptop computer, or a netbook computer); a smartphone; a video game device; a digital media player; a component of a home entertainment equipment; a digital camera or video camera; a wearable computing device (e.g., smart watch, smart glasses, smart clothing); or the like. Each user device54supported by the operating environment50is realized as a computer-implemented or computer-based device having the hardware, software, firmware, and/or processing logic needed to carry out the various techniques and methodologies described herein. For example, the user device54includes a microprocessor in the form of a programmable device that includes one or more instructions stored in an internal memory structure and applied to receive binary input to create binary output. In some embodiments, the user device54includes a GPS module capable of receiving GPS satellite signals and generating GPS coordinates based on those signals. In other embodiments, the user device54includes cellular communications functionality such that the device carries out voice and/or data communications over the communication network56using one or more cellular communications protocols, as are discussed herein. In various embodiments, the user device54includes a visual display, such as a touch-screen graphical display, or other display.

The training and transportation system52includes one or more backend server systems, not shown), which may be cloud-based, network-based, or resident at the particular campus or geographical location serviced by the training and transportation system52. The training and transportation system52can be manned by a live advisor, an automated advisor, an artificial intelligence system, or a combination thereof. The training and transportation system52can communicate with the user devices54and the autonomous vehicles10a-10nto provide driver training as well as schedule rides, dispatch autonomous vehicles10a-10n, and the like. In various embodiments, the training and transportation system52stores store account information such as subscriber authentication information, vehicle identifiers, profile records, biometric data, behavioral patterns, and other pertinent subscriber information.

In accordance with a typical use case workflow, a registered user of the training and transportation system52can create a ride request via the user device54. The ride request will typically indicate the passenger's desired pickup location (or current GPS location), the desired destination location (which may identify a predefined vehicle stop and/or a user-specified passenger destination), and a pickup time. The training and transportation system52receives the ride request, processes the request, and dispatches a selected one of the autonomous vehicles10a-10n(when and if one is available) to pick up the passenger at the designated pickup location and at the appropriate time. The transportation system52can also generate and send a suitably configured confirmation message or notification to the user device54, to let the passenger know that a vehicle is on the way.

As can be appreciated, the subject matter disclosed herein provides certain enhanced features and functionality to what may be considered as a standard or baseline autonomous vehicle10and/or an autonomous vehicle based training and transportation system52. To this end, an autonomous vehicle and autonomous vehicle based training and transportation system can be modified, enhanced, or otherwise supplemented to provide the additional features described in more detail below.

In accordance with various embodiments, controller34implements an autonomous driving system (ADS) as shown inFIG.3. That is, suitable software and/or hardware components of controller34(e.g., processor44and computer-readable storage device46) are utilized to provide an ADS that is used in conjunction with vehicle10.

In various embodiments, the instructions of the autonomous driving system70may be organized by function or system. For example, as shown inFIG.3, the autonomous driving system70can include a sensor fusion system74, a positioning system76, a guidance system78, and a vehicle control system80. As can be appreciated, in various embodiments, the instructions may be organized into any number of systems (e.g., combined, further partitioned, etc.) as the disclosure is not limited to the present examples.

In various embodiments, the sensor fusion system74synthesizes and processes sensor data and predicts the presence, location, classification, and/or path of objects and features of the environment of the vehicle10. In various embodiments, the sensor fusion system74can incorporate information from multiple sensors, including but not limited to cameras, lidars, radars, and/or any number of other types of sensors.

The positioning system76processes sensor data along with other data to determine a position (e.g., a local position relative to a map, an exact position relative to lane of a road, vehicle heading, velocity, etc.) of the vehicle10relative to the environment. The guidance system78processes sensor data along with other data to determine a path for the vehicle10to follow. The vehicle control system80generates control signals for controlling the vehicle10according to the determined path.

In various embodiments, the controller34implements machine learning techniques to assist the functionality of the controller34, such as feature detection/classification, obstruction mitigation, route traversal, mapping, sensor integration, ground-truth determination, and the like.

With reference back toFIG.1, in various embodiments, one or more instructions of the controller34are embodied in the user driver training control system100ofFIG.1, which controls driver training via the autonomous vehicle10.

Referring toFIG.4, an exemplary driver training control system400generally includes a sensing module402, an input module404, a determining module406, and a control module408.

In various embodiments, the sensing module402obtains sensor data for the vehicle10and its surroundings, including as to a location of the vehicle10, operating parameters for the vehicle10(e.g., heading, velocity, acceleration, steering angle, and so on), and an environment surrounding the vehicle10(e.g., roadways, other vehicles, and other objects in proximity to the vehicle10, weather, traffic conditions, road conditions, and so on). In various embodiments, the sensor data is obtained vi the sensing devices (e.g., sensors)40of the sensor system ofFIG.1. In various embodiments, the sensing module402provides the sensor data as inputs403for the input module404and/or determining module406, described below.

Also in various embodiments, the input module404receives manual inputs from a driver trainee for the vehicle10. For example, in various embodiments, the input module receives manual inputs from the trainee's engagement of vehicle input devices such as the accelerator pedal21, steering wheel25, and brake pedal27ofFIG.1, via sensors40ofFIG.1that are coupled to such input devices (e.g., that are coupled to the accelerator pedal21, steering wheel25, brake pedal27, and so on). In various embodiments, the driver manual inputs comprise a driver's intended operation of the vehicle10(e.g., for propulsion, steering, braking, and so on), and are provided as inputs405for the determining module406, described below.

Also in various embodiments, the determining module406determines recommended actions for the vehicle10based on the sensor inputs403provided by the sensing module402, using autonomous control functionality and associated algorithms of the autonomous vehicle10. In addition, also in various embodiments, the determining module406also compares the user manual inputs405with the recommended actions of the vehicle10, and determines scoring for the trainee based on this comparison. In various embodiments, the scoring is provided as inputs407for the control module407.

In addition, in various embodiments, the control module408reports the scoring for the trainee and/or one or back offices and/or authorities (e.g., an operator of a fleet of vehicles, a motor vehicle driving authority, and so on) based on the scoring inputs407. Also in various embodiments, the control module408controls vehicle operations (e.g., including propulsion, steering, and braking) generally in accordance with the sensor inputs403. Furthermore, in various embodiments, the control module408allows gradual control of the of the trainee over driving of the vehicle10(e.g., in certain embodiments, gradual access to various levels and/or control over operation of the vehicle10), based on the scoring for the trainee as reflected in the scoring inputs407.

In various embodiments, the sensing module402, input module404, determining module406, and control module408ofFIG.4, along with associated components of the vehicle10ofFIG.1(e.g., the sensors40, processor44, and computer readable storage device) provide these functions in accordance with the process500that is depicted inFIG.5and described below in connection therewith.

FIG.5is a flowchart for a control process500for training drivers using autonomous vehicles, in accordance with an exemplary embodiment. The control method400is discussed below in connection withFIG.5as well as continued reference toFIGS.1-4. In various embodiments, the control method500can be performed by the system100, the autonomous vehicle10, the driving training control system400, and the various components and associated implementations ofFIGS.1-4, in accordance with exemplary embodiments. As can be appreciated in light of the disclosure, the order of operation within the method is not limited to the sequential execution as illustrated inFIG.5, but may be performed in one or more varying orders as applicable and in accordance with the present disclosure. In various embodiments, the control method500can be scheduled to run based on one or more predetermined events, and/or can run continuously during operation of the autonomous vehicle10.

In various embodiments, the control method500may begin at step502. In various embodiments,602occurs when an occupant/trainee is within the vehicle10and enters a training mode for the vehicle10.

In various embodiments, profile and settings are determined for the trainee at step504. Specifically, in various embodiments, the trainee is identified (e.g., via a keyfob and/or other characteristic of the trainee, and/or a manual inputting of the trainee's name and/or other identification, and so on). Also in various embodiments, appropriate controls for the trainee are established based on the trainee's prior experience and/or scoring with respect to the vehicle10. For example, in certain embodiments, the trainee may be gradually be allowed to use different functionality of the vehicle10for driving and operation of the vehicle10after the trainee has met specific testing and/or scoring metrics with respect to such functionality, and so on. In various embodiments, these determinations are made via the processor44ofFIG.1using sensor inputs from the sensors40ofFIG.1and data stored regarding the trainee in the computer readable storage device (e.g., computer memory)46ofFIG.1.

Also in various embodiments, a determination is made at step506as to whether the trainee is attempting to take control of the vehicle10. In various embodiments, this determination is made by the processor44ofFIG.1via sensors40ofFIG.1, for example as to whether the trainee has engaged one or more input devices such as the accelerator pedal21, steering wheel25, and/or brake pedal27ofFIG.1.

If it is determined that the trainee is attempting to take control, then, in various embodiments, recommended vehicle actions are determined and compared with the user manual inputs in steps508-514, described below.

Specifically, in various embodiments, an autonomous algorithm for the vehicle10utilizes various inputs (including sensor data as to the operating of the vehicle10and the surrounding environment for the vehicle) in step508. In various embodiments, this is performed by the processor44ofFIG.1based on data provided by the sensors40ofFIG.1using autonomous driving algorithms stored in the memory46ofFIG.1.

In addition, in various embodiments, human manual inputs and control for the vehicle10are obtained in step510. Specifically, in various embodiments, the trainee's intended actions for the vehicle are ascertained via the trainee's engagement of vehicle input devices such as the accelerator pedal21, steering wheel25, and brake pedal27of the vehicle10, as measured via sensors40ofFIG.1that are coupled thereto.

Also in various embodiments, data from other vehicle sensors is obtained at step512. Specifically, in various embodiments, data from other vehicle sensors40, for example, such as cameras, radar, Lidar, sonar, and the like, are obtained and further utilized for determining vehicle action in accordance with the autonomous algorithm and used for comparison with the user manual inputs for the trainee's intended actions for the vehicle10. In various embodiments, the sensor data from the other vehicle sensors may include, among other sensor data, sensor data from other sensors, inputs, cameras, and the like that may not be part of the autonomous system, but that may be used for the driving scoring algorithm (e.g., inside cameras looking at the driver to see if he or she is paying attention and looking both ways while driving, and so on).

In addition, also in various embodiments, an algorithm is utilized at step514to compare user manual inputs with the outputs of the autonomous driving algorithm. For example, in various embodiments, the trainee's requested vehicle actions (for example, for requested acceleration, steering, and braking of the vehicle, as determined by the trainee's engagement of the accelerator pedal21, steering wheel25, and brake pedal27, respectively, of the vehicle10) are compared with recommended vehicle actions as determined by the autonomous driving algorithm (for example, as to recommended acceleration, steering, and braking of the vehicle10).

In various embodiments, the processor44ofFIG.1makes these determinations. In addition, in various embodiments, the processor44also calculates a score for the trainee based on these comparisons. In addition, in various embodiments, the processor44provides instructions for various notifications and/or warnings to the trainee and/or one or more other parties (e.g., to a back office, fleet operator, government driving authority, and so on) as to the scoring, as well as to specific instructions for improvement for the trainee. For example, in various embodiments, when the trainee engages a particular user device (e.g. accelerator pedal21, steering wheel25, or brake pedal27) of the vehicle10, the processor44provides immediate audible, visual, haptic, and/or other feedback for the trainee as to whether or not the trainee's intended action (e.g., acceleration, steering, or braking) is consistent with the recommended vehicle action as determined by the autonomous driving algorithm that is stored in the memory46of the computer system of the autonomous vehicle10and executed by the processor44thereof.

Also in various embodiments, one or more determinations are made at step516as to whether or not the trainee has earned control over specific features of vehicle functionality. Specifically, in various embodiments, the processor44ofFIG.1determines whether the scoring of the trainee has exceeded one or more thresholds required for the trainee to be allowed to control one or more specific features of vehicle functionality (e.g., certain types of acceleration, steering, braking, and so on).

For example, in different embodiments, different levels of scoring are required for a trainee/driver to be allowed different specific types of features of vehicle functionality, under a phased or gradual approach. For example, in certain embodiments: (i) a first threshold is required for the trainee to control steering within a lane on a highway; (ii) a second threshold (higher, or more difficult to meet, as compared with the first threshold) is required for the trainee to provide acceleration for the vehicle10; (iii) a third threshold (higher, or more difficult to meet, as compared with the second threshold) is required for the trainee to provide braking for the vehicle10; and (iv) a fourth threshold (higher, or more difficult to meet, as compared with the third threshold) is required for the trainee to provide full control of the vehicle10, and so on.

If it is determined at step516that the trainee's scoring has not met respective predetermined threshold values for particular features of driving functionality, then the processor44disallows the trainee's control over such features of driving functionality, and provides instructions for the control of such driving features to be provided via the autonomous driving algorithm, for example via instructions provided from the processor44ofFIG.1to the actuators42.

Conversely, if it is instead determined at step516that the trainee's scoring has met respective predetermined threshold values for particular features of driving functionality, then the processor44allows the trainee's control over such driving features, for example based on the trainee/driver's engagement of input devices such as the accelerator pedal21, steering wheel25, and brake pedal27of the vehicle10.

As noted above, in various embodiments, a gradual or phased approach is utilizing for allowing control to the driver for different features of driving functionality. Accordingly, in certain embodiments, when the trainee has achieved scoring that is high enough to allow trainee control over some driving features but not others, it will be appreciated that in certain embodiments the processor44may simultaneously: (i) allow the trainee control over certain driving features in step518while also (ii) maintain autonomous control over certain other driving features in step516.

In various embodiments, a determination is made in step520as to whether the vehicle drive continues. In various embodiments, during step520, the processor44determines whether the trainee is still utilizing the vehicle10in a training mode.

In various embodiments, if it is determined in step520that the vehicle drive continues, then the process returns to step506. In various embodiments, steps506-520thereafter repeat, in new iterations, until a determination is made in a subsequent iteration of step520that the vehicle drive is complete.

In various embodiments, once it is determined in an iteration of step520that the vehicle drive no longer continues (e.g. that the vehicle drive is complete), then the process instead proceeds to step522. Specifically, in various embodiments, during step522, the scoring results for the trainee are provided. In various embodiments, the scoring for the trainee is provided to the trainee (e.g., to the user device54ofFIG.1) as well as to a back office (e.g., for an operator of a fleet of vehicles) and to governing entities (e.g., that monitor driving examinations, issue drivers licenses, regulate driving, and so on).

Also in various embodiments, an evaluation of the trainee's scoring is also uploaded to the cloud in step524. For example, in certain embodiments, the scoring for the trainee is stored in one or more central database for future use, for example for when the trainee subsequently enters the vehicle10and/or another vehicle10of a similar type, and so on.

In various embodiments, the process then terminates at step526, as the driving session ends.

Accordingly, in various embodiments, an autonomous vehicle is utilized in training human trainees to be able to drive a vehicle. In various embodiments, manual inputs from the driver are compared with outputs from an autonomous driving algorithm in order to provide training, assessments, and scoring for the trainee. In addition, in various embodiment, real time feedback is provided for the trainee. Moreover, also in various embodiments, as different thresholds are met with respect to the trainee's scoring, different driving features for the vehicle are effectively unlocked, thereby gradually transferring driving control to the trainee/driver as the trainee/driver's scoring gradually exceeds various increasing thresholds. In addition, in various embodiments, the scoring results are provided to the trainee/driver as well as to one or more other interested entities such as, by way of example, the controller of fleet of vehicles and/or governmental authorities that are in charge of regulating driving, administering driving examinations, issuing drivers licenses, and so on. Finally, it is noted that the gradual approach of incrementally providing control to the driver is the reverse of what is typically performed today, for example in which autonomous vehicles may be moving away from manually controlled accelerator pedals, steering wheels, brake pedals, and so on.