Autonomous look ahead methods and systems

Methods and systems are provided for controlling an autonomous vehicle. In one embodiment, a method includes: identifying, by a processor, at least one constraint on a longitudinal dimension of an upcoming road; defining, by the processor, constraint activation logic based on a type of the at least one constraint; performing, by the processor, the constraint activation logic to determine a state of the constraint to be at least one of active and inactive; when the state of the constraint is active, validating, by the processor, a motion plan of the autonomous vehicle based on the constraint; and selectively controlling the autonomous vehicle based on the validating of the motion plan.

INTRODUCTION

The technical field generally relates to methods and systems for controlling an autonomous vehicle, and more particularly relates to methods and systems to constrain driving polices with a lookahead that is based on environmental rules.

An autonomous vehicle is a vehicle that is capable of sensing its environment and navigating with little or no user input. An autonomous vehicle senses its environment using sensing devices such as inertial measurement units, radar, LIDAR, image sensors, and the like. The autonomous vehicle system further uses 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.

Vehicle automation has been categorized into numerical levels ranging from Zero, corresponding to no automation with full human control, to Five, corresponding to full automation with no human control. Various automated driver-assistance systems, such as cruise control, adaptive cruise control, and parking assistance systems correspond to lower automation levels, while true “driverless” vehicles correspond to higher automation levels.

Autonomous agents drive in an environment in which they must obey traffic rules such as: slowing down for speed limits, stopping for stop signs, yielding for traffic and more. As a result, autonomous driving policies must include a lookahead component that assures compliance to those rules or constraints within and beyond its planning horizon. Accordingly, it is desirable to provide a framework to constrain driving policies with a lookahead based on environmental conditions or rules. Furthermore, other desirable features and characteristics of the present invention will become apparent from the subsequent detailed description and the appended claims, taken in conjunction with the accompanying drawings and the foregoing technical field and background.

SUMMARY

Methods and systems are provided for controlling an autonomous vehicle. In one embodiment, a method includes: identifying, by a processor, at least one constraint on a longitudinal dimension of an upcoming road; defining, by the processor, constraint activation logic based on a type of the at least one constraint; performing, by the processor, the constraint activation logic to determine a state of the constraint to be at least one of active and inactive; when the state of the constraint is active, validating, by the processor, a motion plan of the autonomous vehicle based on the constraint; and selectively controlling the autonomous vehicle based on the validating of the motion plan.

In various embodiments, the validating comprises validating the motion plans within a defined horizon of the autonomous vehicle based on a desired velocity of the motion plan.

In various embodiments, the validating further comprises validating the motion plan beyond the defined horizon of the autonomous vehicle based on a terminal pose of the motion plan.

In various embodiments, the validating is further based on back propagating limitations of the constraint to the terminal pose.

In various embodiments, the constraint activation logic defines the state of the constraint as active based on a determined point of no return, wherein the point of no return is determined based on a braking distance that is based on all possible velocities of the autonomous vehicle and a position of the constraint.

In various embodiments, the constraint activation logic defines the state of the constraint as active when the autonomous vehicle has not reached the point of no return for a current velocity.

In various embodiments, the constraint activation logic defines the state of the constraint as active when the autonomous vehicle has reached or passed the point of no return for a current velocity and plan is predicted as safe for a determined worst case scenario.

In various embodiments, the method further includes identifying the constraint type of the constraint to be a physical stop-bar that relates to a stop-sign.

In various embodiments, the constraint activation logic defines the state of the constraint as active until a feedback is received from the driver to override a plan.

In various embodiments, the constraint activation logic defines the state of the constraint as active until the vehicle is determined to be at a stop for a predetermined time.

In various embodiments, the method further includes identifying, by the processor, the constraint type of the constraint to be a virtual stop bar that relates to a traffic control device.

In various embodiments, the constraint activation logic defines the state of the constraint as active when the status of the light is flashing yellow or displays red.

In various embodiments, the method includes identifying, by the processor the constraint type of the constraint to be a virtual bar created at the front of a yield-to-traffic maneuver.

In various embodiments, the constraint activation logic defines the state of the constraint as active based on predictions of other vehicles in a connecting lane.

In various embodiments, identifying, by the processor, the constraint type of the constraint to be a point along a rounded curve that limits a maximum driving speed.

In another embodiment, a system for controlling a vehicle is provided. The system includes: an input device that receives information indicative of at least one constraint on a longitudinal dimension of an upcoming road; and a control module configured to, by a processor, define constraint activation logic based on a type of the at least one constraint, perform the constraint activation logic to determine a state of the constraint to be at least one of active and inactive, when the state of the constraint is active, validate a motion plan of the autonomous vehicle based on the constraint, and selectively control the autonomous vehicle based on the validating of the motion plan.

In various embodiments, the control module is further configured to, by the processor, determine the type of the constraint to be one of a physical stop-bar that relates to a stop-sign, a virtual stop bar that relates to a traffic control device, a virtual bar created at the front of a yield-to-traffic maneuver, and a point along a rounded curve that limits a maximum driving speed.

In various embodiments, the control module is configured to validate the motion plans within a defined horizon of the autonomous vehicle based on a desired velocity of the motion plan and validate the motion plan beyond the defined horizon of the autonomous vehicle based on a terminal pose of the motion plan.

In various embodiments, the control module is configured to validate the motion plan beyond the defined horizon based on back propagating limitations of the constraint to the terminal pose.

In various embodiments, the control module is configured to define the state of the constraint as active based on a determined point of no return, wherein the point of no return is determined based on a braking distance that is based on all possible velocities of the autonomous vehicle and a position of the constraint.

DETAILED DESCRIPTION

With reference toFIG.1, a motion plan validation system shown generally at100is associated with a vehicle10in accordance with various embodiments. In general, the motion plan validation system100provides a framework to constrain driving policies with a lookahead based on environmental conditions or rules. Moreover, policies are evaluated and validated or invalidated regardless of the planning approach employed. The motion planning system thus, intelligently controls the vehicle10based thereon.

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.

In various embodiments, the vehicle10is an autonomous vehicle and the motion plan validation system100is 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, etc., can also be used. In an exemplary embodiment, the autonomous vehicle10is a so-called Level Four or Level Five automation system. A Level Four system indicates “high automation”, referring to the driving mode-specific performance by an automated driving system of 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 indicates “full automation”, referring to the full-time performance by an automated driving system of all aspects of the dynamic driving task under all roadway and environmental conditions that can be managed by a human driver.

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. The transmission system22is configured to transmit power from the propulsion system20to the vehicle wheels16-18according 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 wheels16-18. The 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. The steering system24influences a position of the of the vehicle wheels16-18. While depicted as including a steering wheel for illustrative purposes, in some embodiments contemplated within the scope of the present disclosure, the steering system24may not include a steering wheel.

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-40ncan include, but are not limited to, radars, lidars, global positioning systems, optical cameras, thermal cameras, ultrasonic sensors, inertial measurement units, and/or other sensors. The actuator system30includes one or more actuator devices42a-42nthat control one or more vehicle features such as, but not limited to, the propulsion system20, the transmission system22, the steering system24, and the brake system26. In various embodiments, the vehicle features can further include interior and/or exterior vehicle features such as, but are not limited to, doors, a trunk, and cabin features such as air, music, lighting, etc. (not numbered).

The data storage device32stores data for use in automatically controlling the autonomous vehicle10. 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. As can 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, one or more instructions of the controller34are embodied in the motion plan validation system100and, when executed by the processor44identify a physical or virtual constraint on the longitudinal dimension of the road (such as a speed constraint including both a position and a velocity constraint). This may be a physical stop-bar that relates to a nearby stop-sign (always on), a virtual stop bar that relates to a traffic control device (toggled according to device's state), a point along a rounded curve that limits max driving speed, or a virtual bar created at the front of a yield-to-traffic maneuver. Thereafter, the instructions define constraint activation logic and identify the activation state to be one of active or non-active. As will be discussed in more detail below, if the constraint is determined to be active, the instructions validate motion plans against the limits at the designated longitudinal positions of the constraint—both within the vehicle's planned horizon and beyond the planned horizon or invalidate motion plans that do not adhere to the limits.

With reference now toFIG.2, in various embodiments, the autonomous vehicle10described with regard toFIG.1may be suitable for use in the context of a taxi or shuttle system in a certain geographical area (e.g., a city, a school or business campus, a shopping center, an amusement park, an event center, or the like) or may simply be managed by a remote system. For example, the autonomous vehicle10may be associated with an autonomous vehicle based remote transportation system.FIG.2illustrates an exemplary embodiment of an operating environment shown generally at50that includes an autonomous vehicle based remote transportation system52that is associated with one or more autonomous vehicles10a-10nas described with regard toFIG.1. In various embodiments, the operating environment50further includes one or more user devices54that communicate with the autonomous vehicle10and/or the remote 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 network56can 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, etc.) 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 remote 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 remote transportation system52need not be connected via the land communication system62but 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 piece of 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 remote transportation system52includes one or more backend server systems, which may be cloud-based, network-based, or resident at the particular campus or geographical location serviced by the remote transportation system52. The remote transportation system52can be manned by a live advisor, or an automated advisor, or a combination of both. The remote transportation system52can communicate with the user devices54and the autonomous vehicles10a-10nto schedule rides, dispatch autonomous vehicles10a-10n, and the like. In various embodiments, the remote transportation system52stores account information such as subscriber authentication information, vehicle identifiers, profile records, behavioral patterns, and other pertinent subscriber information.

In accordance with a typical use case workflow, a registered user of the remote 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 remote 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 remote 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 remote transportation system52. To this end, an autonomous vehicle and autonomous vehicle based remote transportation system can be modified, enhanced, or otherwise supplemented to provide the additional features described in more detail below.

In accordance with various embodiments, the controller34implements an autonomous driving system (ADS)70as shown inFIG.3. That is, suitable software and/or hardware components of the controller34(e.g., the processor44and the computer-readable storage device46) are utilized to provide an autonomous driving system70that is used in conjunction with vehicle10.

In various embodiments, the instructions of the autonomous driving system70may be organized by function, module, or system. For example, as shown inFIG.3, the autonomous driving system70can include a computer vision 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 computer vision 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 computer vision 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.

As mentioned briefly above, the motion plan validation system100ofFIG.1is included within the ADS70, for example, as a separate system or as part of the guidance system78. In various embodiments, as shown in more detail with regard toFIG.4and with continued reference toFIG.3, the motion plan validation system100includes a constraint identification module102, a constraint state determination module104, and a validation module106. It will be understood that various embodiments of the motion plan validation system100according to the present disclosure may include any number of sub-modules embedded within the controller34which may be combined and/or further partitioned to similarly implement systems and methods described herein. Furthermore, inputs to the motion plan validation system100may be received from the sensor system28, retrieved from the data storage device32, received from other control modules (not shown) associated with the autonomous vehicle10, received from the communication system36, and/or determined/modeled by other sub-modules (not shown) within the controller34ofFIG.1. Furthermore, the inputs might also be subjected to preprocessing, such as sub-sampling, noise-reduction, normalization, feature-extraction, missing data reduction, and the like.

In various embodiments, the constraint identification module102identifies a physical or virtual speed related constraint at a longitudinal position of the road and generates constraint type data110and constraint position data111based thereon. As discussed above, the constraint types can be, but are not limited to, a physical stop-bar that relates to a nearby stop-sign, a virtual stop bar that relates to a traffic control device, a point along a rounded curve that limits max driving speed, a virtual bar created at the front of a yield-to-traffic maneuver, etc. In various embodiments, the constraint identification module102determines the constraint type and position based on map data112stored in the data storage device46of the vehicle10, based on road information114received from other vehicles or infrastructure, for example, ahead of the vehicle10, and/or based on sensor data116generated by the sensing devices40a-40nof the vehicle10.

In various embodiments, the constraint state determination module104defines constraint activation logic based on the constraint type data110and performs the logic to identify a constraint state118. In various embodiments the constraint activation logic defines the constraint state118to be active or inactive. For example, when the constraint type data110indicates the constraint type is a virtual bar created at the front of a yield-to-traffic maneuver, the constraint state determination module104first determines a point of no return (PoNR), then determines a worst case scenario based on other actors in the area, and generates a heuristic plan based on the worst case scenario. In various embodiments, the constraint state determination module104determines the PoNR as a virtual location sithat is based on an offset from the actual location of the stop-constraint indicated by the constraint position data111. The offset is computed backwards from the position of the stop constraint using every possible velocity v[T]that the vehicle10may be at. The offset relates to a braking distance D(v[T], 0).

In various embodiments, the constraint state determination module104determines that the vehicle10can safely commit to execute a motion plan and thus sets the constraint state118to inactive when either of the following conditions apply over the vehicle's terminal state: s[T]+D(v[T], 0)<=si(the vehicle has not reached the PoNR; or s[T]+D(v[T], 0)>si(the vehicle has crossed the PoNR) and there exists a safe plan π from this terminal state under the worst-case prediction. The constraint state determination module104sets the constraint state118to inactive when there does not exists a safe plan π from this terminal state under the worst-case prediction.

When the constraint type data110indicates that the constraint type is a point along a rounded curve that limits a maximum driving speed, points along the longitude of the driven lane are sampled with some frequency. For each sampled point i, the longitude along the lane siand the curvature Kiof the center curve of (or any baseline curve along) that lane (dictated by road's geometry and therefore can be cached offline) are stored. The curvature values are transformed to longitudinal speed limits based on a desired max lateral acceleration limit

Amaxlat:vi=Amaxlatκi2.
These longitudinal speed limits are then used to compute the braking distances and evaluate the PoNR to set the constraint state118as discussed above.

When the constraint type data110indicates that the constraint type is a virtual stop bar that relates to a traffic control device, the positions and velocities {(s1, v1) (sk, vk)} are collected for traffic control devices that are determined to be active. For example, a traffic control device such as a stop-sign is always considered active until a feedback from the driver is sent to override the plan, or alternatively, the vehicle10has been at a full stop for some sufficient time period. In another example, a traffic control device such as a yield-sign is dynamically toggled between active and inactive based on the state and predictions of other vehicles in connecting lanes. In still another example, a traffic control device such as a traffic light is considered as active as long as the light is flashing yellow or displays full red.

In various embodiments, the validation module106validates motion plans against speed limits at the designated longitudinal positions of the constraint—both within its horizon and beyond its horizon. The validation module106invalidates any motion plans that do not adhere to the limits. For example, the validation module compares a desired velocity from the motion plan data122to the active constraints. When the desired velocity is less than or equal to all active constraints within the defined horizon, the motion plan is validated for the horizon and horizon validation data124is generated. However, when the desired velocity is greater than at least one active constraint within the defined horizon, the motion plan is invalidated for the horizon.

In another example, the validation module106validates the motion plan beyond the horizon based on a terminal pose of the motion plan indicated by the motion plan data122. For example, motion plans are of limited horizon in nature. Each plan has a terminal pose for the vehicle 1 to target. Accordingly, (s[T], v[T]) is the longitudinal position and velocity at time T that represents the end pose of a trajectory candidate. If there are active constraints identified beyond that terminal pose, the constraints including the set of all k speed related constraints {(s1, v1) . . . (sk, vk)} are propagated back to the terminal pose (s[T], v[T]) to guarantee no future “dead-end” situation occurs. A predefined kinematic model of deceleration (e.g., that reflects an emergency brake) is used for the propagation. For example, D(v[T], vi) is the minimal distance to travel required to transition from velocity v[T]to viusing a predefined deceleration profile. Thus, the motion plan is validated “beyond-horizon” when: ∀i∈1 . . . k: s[T]+D(v[T], vi)<=si, (si>s[T]) and beyond horizon validation data126is generated.

Referring now toFIG.5, and with continued reference toFIGS.1-4, a flowchart illustrates a control method400that can be performed by the motion plan validation system100ofFIG.1in accordance with the present disclosure. 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.5but may be performed in one or more varying orders as applicable and in accordance with the present disclosure. In various embodiments, the method400can be scheduled to run based on one or more predetermined events, and/or can run continuously during operation of the autonomous vehicle10.

In one embodiment, the method may begin at405. A constraint is identified at410. Thereafter, it is determined whether the constraint is active at420. For example, the PoNR method as discussed above is performed based on the constraint type where: when it is determined that the vehicle has not passed the PoNR, then the state is set to not active, when it is determined that the vehicle has passed the PoNR, a worst case scenario is determined. It is then determined whether a safe plan for this worst case scenario can be established. When a safe plan for the worst case scenario can be established, the state is set to not active. When a safe plan for the worst case scenario cannot be established, the state is set to active.

When the constraint is determined to be active at430, the kinematic limitations of the active constraint are extracted at440and it is determined whether other constraints exist at450. When the constraint is determined to be not active at430, it is determined whether other constraints exist at450.

When other constraints exist at450, the method continues with identifying the constraints at410. When no additional constraints exist at450, motion plans within the horizon are validated based on the kinematic limitations of the constraint at460and motion plans beyond the horizon are validated based on the kinematic limitations of all active constraint at470, for example as discussed above. Thereafter, the method may end at480.