Autonomous forward looking method and system

Abstract

Methods and systems for controlling an autonomous vehicle are provided. In an 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, executing, by the processor, the constraint activation logic to determine a status of the constraint as at least one of active and inactive, validating, by the processor, a motion plan for the autonomous vehicle based on the constraint when the status of the constraint is active, and selectively controlling the autonomous vehicle based on the validation of the motion plan.

Description

Technical Field

[0001] The technology field generally relates to methods and systems for controlling autonomous vehicles, and more specifically, to methods and systems for constraining driving strategies using environmental rule-based foresight. Background Technology

[0002] An autonomous vehicle is a vehicle capable of sensing its environment and navigating with little or no user input. Autonomous vehicles use sensing devices such as inertial measurement units, radar, lidar, and image sensors to perceive their environment. Autonomous vehicle systems also use information from Global Positioning System (GPS) technology, navigation systems, vehicle-to-vehicle communication, vehicle-to-infrastructure technology, and / or drive-by-wire systems to navigate the vehicle.

[0003] Vehicle automation has been categorized into numerical levels, ranging from zero to five, where zero corresponds to no automation with complete human control and five corresponds to full automation without human control. Various automated driver assistance systems, such as cruise control, adaptive cruise control, and parking assistance systems, correspond to lower levels of automation, while truly "driverless" vehicles correspond to higher levels of automation.

[0004] Autonomous vehicles drive in environments where they must obey traffic rules, such as speed limits, stop signs, and yielding to traffic. Therefore, autonomous driving policies must include a look-ahead component to ensure compliance with these rules or constraints both within and outside their planned line of sight. Thus, it is desirable to provide a framework for using look-ahead to constrain driving strategies based on environmental conditions or rules. Furthermore, other desirable features and characteristics of the invention will become apparent from the following detailed description and appended claims, taken in conjunction with the accompanying drawings and the foregoing technical and background information. Summary of the Invention

[0005] Methods and systems for controlling autonomous vehicles are provided. In one embodiment, a method includes: identifying at least one constraint on the longitudinal dimension of an approaching road by a processor; defining constraint activation logic by the processor based on the type of the at least one constraint; executing the constraint activation logic by the processor to determine the state of the constraint as at least one of active and inactive; verifying a motion plan of the autonomous vehicle by the processor based on the constraint when the constraint is active; and selectively controlling the autonomous vehicle based on the verification of the motion plan.

[0006] In various embodiments, verification includes verifying the autonomous vehicle's motion plan within a limited line of sight based on the expected speed of the motion plan.

[0007] In various embodiments, verification also includes verifying the autonomous vehicle's motion plan beyond a limited line-of-sight based on the terminal attitude of the motion plan.

[0008] In various embodiments, the verification is also based on backpropagation of constraints on the terminal pose.

[0009] In various embodiments, the constraint activation logic defines the state of the constraint as active based on a determined no-return point, wherein the no-return point is determined based on a braking distance based on all possible speeds of the autonomous vehicle and a location of the constraint.

[0010] In various embodiments, the constraint activation logic defines the state of the constraint as active when the autonomous vehicle has not reached the no-return point for the current speed.

[0011] In various embodiments, the constraint activation logic defines the state of the constraint as active when the autonomous vehicle has reached or passed the no-return point for the current speed and the plan is predicted to be safe for a determined worst-case scenario.

[0012] In various embodiments, the method further comprises identifying, by the processor, a constraint type of the constraint as a physical stop bar related to a stop sign.

[0013] In various embodiments, the constraint activation logic defines the state of the constraint as active until feedback is received from the driver to override the plan.

[0014] In various embodiments, the constraint activation logic defines the state of the constraint as active until a vehicle stop is determined for a predetermined time.

[0015] In various embodiments, the method further comprises identifying, by the processor, a constraint type of the constraint as a virtual stop bar related to a traffic control device.

[0016] In various embodiments, the constraint activation logic defines the state of the constraint as active when the light is either flashing yellow or displaying red.

[0017] In various embodiments, the method comprises identifying, by the processor, a constraint type of the constraint as a virtual bar created in front of a yield traffic maneuver.

[0018] In various embodiments, the constraint activation logic defines the state of the constraint as active based on a prediction of other vehicles in a connected lane.

[0019] In various embodiments, a constraint type of the constraint is identified, by the processor, as a point along a circular curve that limits a maximum driving speed.

[0020] 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 define, by a processor, a constraint activation logic based on a type of the at least one constraint, execute the constraint activation logic to determine a status of the constraint as at least one of active and inactive, validate a motion plan of an autonomous vehicle based on the constraint when the status of the constraint is active, and selectively control the autonomous vehicle based on the validation of the motion plan.

[0021] In various embodiments, the control module is further configured to determine, by the processor, the type of the constraint as one of a physical stop bar associated with a stop sign, a virtual stop bar associated with a traffic control device, a virtual bar created in front of a yielding traffic maneuver, and a point along a circular curve that limits a maximum driving speed.

[0022] In various embodiments, the control module is configured to validate the motion plan of the autonomous vehicle within a defined visibility based on a desired speed of the motion plan, and validate the motion plan of the autonomous vehicle outside the defined visibility based on a terminal pose of the motion plan.

[0023] In various embodiments, the control module is configured to validate the motion plan outside the defined visibility based on a backpropagation limit of the constraint on the terminal pose.

[0024] In various embodiments, the control module is configured to define the status of the constraint as active based on a determination of a no-return point, wherein the no-return point is determined based on a braking distance that is based on all possible speeds of the autonomous vehicle and a location of the constraint. BRIEF DESCRIPTION OF DRAWINGS

[0025] Exemplary embodiments will be described below with reference to the following drawings, in which like reference numerals refer to like elements, and in which:

[0026] Figure 1 is a functional block diagram illustrating an autonomous vehicle having a motion plan validation system according to various embodiments;

[0027] Figure 2 is a functional block diagram illustrating a transportation system having Figure 1 one or more autonomous vehicles according to various embodiments;

[0028] Figure 3 is a functional block diagram illustrating an autonomous driving system of an autonomous vehicle according to various embodiments and in conjunction with motion plan validation;

[0029] Figure 4 is a schematic diagram illustrating a motion plan validation system according to various embodiments; and

[0030] Figure 5 is a flowchart illustrating a method for validating a motion plan of an autonomous vehicle, in accordance with various embodiments. DETAILED DESCRIPTION

[0031] The following detailed description is merely exemplary in nature and is not intended to limit the application and use. 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 but not limited to: application specific integrated circuits (ASICs), electronic circuits, processors (shared, dedicated, or group) and memories that execute one or more software or firmware programs, combinational logic circuits, and / or other suitable components that provide the described functionality.

[0032] Embodiments of the disclosure can be described herein in terms of functional and / or logical block components and various processing steps. It should be appreciated that such block components can be realized by any number of hardware, software, and / or firmware components configured to perform the specified functions. For example, an embodiment of the disclosure can employ various integrated circuit components, e.g., memory elements, digital signal processing elements, logic elements, look-up tables, or the like, which can carry out a variety of functions under the control of one or more microprocessors or other control devices. In addition, those skilled in the art will appreciate that embodiments of the disclosure can be practiced with other systems than the system described herein and that the system described herein is merely one example embodiment of the disclosure.

[0033] For the sake of brevity, conventional techniques related to signal processing, data transmission, signaling, control, and other functional aspects of the systems (and the individual operating components of the systems) can not be described in detail herein. Furthermore, the connecting lines shown in the various figures contained herein are intended to represent example functional relationships and / or physical couplings between the various elements. It should be noted that many alternatives or additional functional relationships or physical connections can be present in an embodiment of the disclosure.

[0034] Reference Figure 1 In accordance with various embodiments, a motion plan validation system is generally shown at 100 in relation to a vehicle 10. Generally, the motion plan validation system 100 provides a framework to constrain driving strategies with foresight based on environmental conditions or rules. Furthermore, the strategies are evaluated, validated, or invalidated regardless of the planning method employed. Thus, the motion planning system intelligently controls the vehicle 10 based thereon.

[0035] As Figure 1As shown, vehicle 10 generally includes a chassis 12, a body 14, front wheels 16 and rear wheels 18. Body 14 is disposed on chassis 12 and substantially encloses components of vehicle 10. Body 14 and chassis 12 can collectively form a frame. Wheels 16-18 are each rotatably coupled to chassis 12 near a respective corner of body 14.

[0036] In various embodiments, vehicle 10 is an autonomous vehicle, and motion plan verification system 100 is incorporated into autonomous vehicle 10 (hereinafter autonomous vehicle 10). Autonomous vehicle 10 is, for example, a vehicle that is automatically controlled to transport passengers from one location to another. In the illustrated embodiment, vehicle 10 is described as a passenger car, but it should be appreciated that any other conveyance including motorcycles, trucks, sport utility vehicles (SUVs), recreational vehicles (RVs), sea craft, aircraft, and the like can also be used. In the example embodiment, autonomous vehicle 10 is a so-called Level 4 or Level 5 automated system. Level 4 indicates "high automation," referring to driving mode-specific performance by an automated driving system for all aspects of the dynamic driving task under sustained conditions that can be managed by a human driver without intervention requests. Level 5 indicates "full automation," referring to full-time performance by an automated driving system for all aspects of the dynamic driving task under all roadway and environmental conditions that can be managed by a human driver.

[0037] As shown, autonomous vehicle 10 generally includes a propulsion system 20, a drivetrain 22, a steering system 24, a braking system 26, a sensor system 28, an actuator system 30, at least one data storage device 32, at least one controller 34, and a communication system 36. In various embodiments, propulsion system 20 can include an internal combustion engine, an electric machine such as a traction motor, and / or a fuel cell propulsion system. Drivetrain 22 is configured to transfer power from propulsion system 20 to wheels 16-18 according to a selectable speed ratio. According to various embodiments, drivetrain 22 can include a stepped automatic transmission, a continuously variable transmission, or other suitable transmission. Braking system 26 is configured to provide braking torque to wheels 16-18. In various embodiments, braking system 26 can include friction brakes, regenerative brakes such as electric machines, and / or other suitable braking systems. Steering system 24 affects the position of wheels 16-18. Although depicted as including a steering wheel for illustrative purposes, steering system 24 can not include a steering wheel in some embodiments contemplated within the scope of the present disclosure.

[0038] The sensor system 28 includes one or more sensing devices 40a-40n that sense observable conditions of the external environment and / or the internal environment of the autonomous vehicle 10. The sensing devices 40a-40n can include, but are not limited to, radar, lidar, global positioning systems, optical cameras, thermal cameras, ultrasonic sensors, inertial measurement units, and / or other sensors. The actuator system 30 includes one or more actuator devices 42a-42n that control one or more vehicle features, such as, but not limited to, the propulsion system 20, the drivetrain system 22, the steering system 24, and the braking system 26. In various embodiments, the vehicle features can further include internal and / or external vehicle features, such as, but not limited to, doors, trunks, and cabin features, such as ventilation, music, lighting, etc. (not numbered).

[0039] The communication system 36 is configured to wirelessly communicate information to and from other entities 48, such as, but not limited to, other vehicles ("V2V" communication), infrastructure ("V2I" communication), remote systems, and / or personal devices (with respect to Figure 2 Described in further detail). In an exemplary embodiment, the communication system 36 is a wireless communication system configured to communicate via a wireless local area network (WLAN) using the IEEE 802.11 standard or by using cellular data communication. However, additional or alternative communication methods, such as a dedicated short-range communications (DSRC) channel, are also contemplated to be within the scope of the present disclosure. A DSRC channel refers to a one-way or two-way short-to-medium range wireless communication channel specifically designed for automotive applications, as well as a corresponding set of protocols and standards.

[0040] The data storage device 32 stores data used to automatically control the autonomous vehicle 10. In various embodiments, the data storage device 32 stores a defined map of a navigable environment. In various embodiments, the defined map can be pre-defined by a remote system and obtained from the remote system (with respect to Figure 2 Described in further detail). For example, the defined map can be assembled by the remote system and communicated to the autonomous vehicle 10 (wirelessly and / or in a wired manner) and stored in the data storage device 32. It can be appreciated that the data storage device 32 can be part of the controller 34, separate from the controller 34, or part of the controller 34 and part of a separate system.

[0041] The controller 34 includes at least one processor 44 and a computer- readable storage device or media 46. The processor 44 can be any custom made or commercially available processor, a central processing unit (CPU), a graphics processing unit (GPU), a co-processor of several processors associated with the controller 34, a semiconductor-based microprocessor (in the form of a microchip or chip set), a macroprocessor, any combination thereof, or generally any device for executing instructions. The computer-readable storage device or media 46 can include volatile and nonvolatile storage in, for example, read-only memory (ROM), random access memory (RAM), and keep-alive memory (KAM). KAM is a nonvolatile memory that is used to store various operational variables when the processor 44 is powered down. The computer-readable storage device or media 46 can be implemented using any of a number of known memory devices, such as PROM (programmable read-only memory), EPROM (erasable PROM), EEPROM (electrically erasable PROM), flash memory, or any other electrical, magnetic, optical, or combination memory device capable of storing data, some of which represent executable instructions that the controller 34 uses when controlling the autonomous vehicle 10.

[0042] The instructions can include one or more separate programs, each of which comprises an ordered listing of executable instructions for implementing logical functions. When executed by the processor 44, these instructions receive and process signals from the sensor system 28, perform logic, calculations, methods, and / or algorithms for automatically controlling components of the autonomous vehicle 10, and generate control signals to the actuator system 30 to automatically control components of the autonomous vehicle 10 based on the logic, calculations, methods, and / or algorithms. Although only one controller 34 is shown in Figure 1 Although only one controller 34 is shown in FIG. 1, embodiments of the autonomous vehicle 10 can include any number of controllers 34 that communicate through any suitable communication medium or combination of communication media and cooperate to process sensor signals, perform logic, calculations, methods, and / or algorithms, and generate control signals to automatically control features of the autonomous vehicle 10.

[0043] In various embodiments, one or more instructions of the controller 34 are embodied in the motion plan validation system 100 and, when executed by the processor 44, identify physical or virtual constraints in the road longitudinal dimension (e.g., speed constraints including position and velocity constraints). This can be a physical stop bar associated with a nearby stop sign (always on), a virtual stop bar associated with a traffic control device (switched according to device state), a point along a circular curve that limits maximum travel speed, or a virtual stop bar created ahead of a yielding traffic maneuver. Thereafter, the instructions define constraint activation logic and identify an activation state as one of active or inactive. As will be discussed in greater detail below, if the constraint is determined to be active, the instructions validate the motion plan against the limit at the specified longitudinal position of the constraint— both within and outside of the vehicle's planned sight distance, or invalidate the motion plan if it does not comply with the limit.

[0044] Reference is now made to Figure 2 In various embodiments, reference is made to Figure 1 The autonomous vehicle 10 described can be suitable for use in the context of a taxi or shuttle system in a particular geographic area (e.g., a city, a school or business campus, a shopping center, an amusement park, a convention center, etc.), or can simply be managed by a remote system. For example, the autonomous vehicle 10 can be associated with an autonomous vehicle-based remote transportation system. Figure 2 An exemplary embodiment of an operating environment, generally shown at 50, is shown that includes an autonomous vehicle-based remote transportation system 52 associated with one or more autonomous vehicles 10a-10n, as described with reference to Figure 1 In various embodiments, the operating environment 50 also includes one or more user devices 54 in communication with the autonomous vehicles 10 and / or the remote transportation system 52 via a communication network 56.

[0045] The communication network 56 supports communications as needed between devices, systems, and components supported by the operating environment 50 (e.g., via tangible communication links and / or wireless communication links). For example, the communication network 56 can include a wireless carrier system 60, such as a cellular telephone system including a plurality of cell towers (not shown), one or more mobile switching centers (MSCs) (not shown), and any other network components as desired to connect the wireless carrier system 60 with the land communication system. Each cell tower includes a transmission and receiving antenna as well as a base station, and the base stations from different cell towers are connected to the MSCs either directly, or through intermediate equipment such as a base station controller. The wireless carrier system 60 can implement any suitable communication technique, including, for example, digital techniques, 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 can be used with the wireless carrier system 60. For example, the base stations and cell towers can be co-located at the same site, or they can be remote from one another, each base station can be responsible for a single cell tower, or a single base station can serve various cell towers, or various base stations can be coupled to a single MSC, just to name a few possible arrangements.

[0046] In addition to including the wireless carrier system 60, a second wireless carrier system in the form of a satellite communication system 64 can be included to provide one-way or two-way communication with the autonomous vehicles 10a-10n. This can be accomplished using one or more communication satellites (not shown) and uplink transmitting stations (not shown). One-way communication can include, for example, a satellite radio service in which program content (news, music, etc.) is received by a transmitting station, packaged for upload, and then transmitted to a satellite, which broadcasts the program to users. Two-way communication can include, for example, a satellite telephone service that uses a satellite to relay telephone communications between a vehicle 10 and a station. In addition to, or instead of, the wireless carrier system 60, a satellite telephone can be used.

[0047] A land communication system 62, which is a conventional land-based telecommunications network connected to one or more landline telephones, and connecting the wireless carrier system 60 to the remote transportation system 52, can also be included. For example, the land communication system 62 can include a public switched telephone network (PSTN), such as a network used to provide hardwired telephones, packet-switched data communications, and Internet infrastructure. One or more segments of the land communication system 62 can be implemented through the use of a standard wire-based network, a fiber or other optical network, a cable network, a power line, other wireless networks such as a wireless local area network (WLAN) or a network providing broadband wireless access (BWA), or any combination thereof. Further, the remote transportation system 52 need not be connected via the land communication system 62, but can include a wireless telephone device such that it can communicate directly with a wireless network, such as the wireless carrier system 60.

[0048] Although only one user device 54 is shown in Figure 2 embodiments of the operating environment 50 can support any number of user devices 54, including multiple user devices 54 owned, operated, or otherwise used by a single person. Each user device 54 supported by the operating environment 50 can be implemented using any suitable hardware platform. In this regard, the user device 54 can be implemented in any common form, including but not limited to: a desktop computer; a mobile computer (such as a tablet computer, a laptop computer, or a netbook computer); a smartphone; a video game device; a digital media player; a home entertainment device; a digital camera or camcorder; a wearable computing device (such as a smartwatch, smartglasses, smartclothing), etc. Each user device 54 supported by the operating environment 50 is implemented as a computer-implemented or computer-based device having the hardware, software, firmware, and / or processing logic necessary to perform the various techniques and methodologies described herein. For example, the user device 54 includes a microprocessor in the form of a programmable device that includes one or more instructions stored in an internal memory structure and is configured to receive binary inputs to create binary outputs. In some embodiments, the user device 54 includes a GPS module capable of receiving GPS satellite signals and generating GPS coordinates based on these signals. In other embodiments, the user device 54 includes cellular communication functionality such that the device performs voice and / or data communications over a communication network 56 using one or more cellular communication protocols, as described herein. In various embodiments, the user device 54 includes a visual display, such as a touchscreen graphical display or other display.

[0049] The remote transportation system 52 includes one or more backend server systems, which can be cloud-based, web-based, or resident at a particular campus or geographic location served by the remote transportation system 52. The remote transportation system 52 can be navigated by a live advisor or an automated advisor, or a combination of both. The remote transportation system 52 can communicate with the user device 54 and the autonomous vehicles 10a-10n to arrange rides, dispatch autonomous vehicles 10a-10n, etc. In various embodiments, the remote transportation system 52 stores account information, such as subscriber authentication information, vehicle identifiers, profile records, behavioral patterns, and other relevant subscriber information.

[0050] According to a typical use case workflow, a registered user of the remote transportation system 52 can create a ride request through the user device 54. The ride request will typically indicate a desired pickup location (or current GPS location) for the passenger, a desired destination location (which can identify a predefined vehicle stop and / or a user-specified passenger destination), and a pickup time. The remote transportation system 52 receives the ride request, processes the request, and dispatches a selected one of the autonomous vehicles 10a-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 system 52 can also generate and send an appropriately configured confirmation message or notification to the user device 54 to let the passenger know that the vehicle is on the way.

[0051] It can be appreciated that the subject matter disclosed herein provides certain enhanced features and functionality to what can be considered a standard or baseline autonomous vehicle 10 and / or autonomous vehicle-based remote transportation system 52. To this end, the autonomous vehicle and autonomous vehicle-based remote transportation system can be modified, enhanced, or supplemented to provide additional features described in greater detail below.

[0052] According to various embodiments, the controller 34 implements an autonomous driving system (ADS) 70 as shown in Figure 3 That is, suitable software and / or hardware components of the controller 34 (e.g., the processor 44 and the computer-readable storage device 46) are used to provide the autonomous driving system 70 for use in conjunction with the vehicle 10.

[0053] In various embodiments, the instructions of the autonomous driving system 70 can be organized by function, module, or system. For example, as shown in Figure 3 the autonomous driving system 70 can include a computer vision system 74, a localization system 76, a guidance system 78, and a vehicle control system 80. As can be appreciated, in various embodiments, the instructions can be organized into any number of systems (e.g., combined, further divided, etc.), as the present disclosure is not limited to the current example.

[0054] In various embodiments, the computer vision system 74 synthesizes and processes sensor data and predicts the presence, location, classification, and / or path of objects and features of the environment of the vehicle 10. In various embodiments, the computer vision system 74 can incorporate information from multiple sensors, including but not limited to cameras, lidar, radar, and / or any number of other types of sensors.

[0055] The localization system 76 processes sensor data and other data to determine the position of the vehicle relative to the environment (e.g., a local position relative to a map, an exact position relative to a road lane, vehicle heading, speed, etc.). The guidance system 78 processes sensor data and other data to determine a path for the vehicle 10 to follow. The vehicle control system 80 generates control signals for controlling the vehicle 10 in accordance with the determined path.

[0056] In various embodiments, the controller 34 implements machine learning techniques to assist in the functionality of the controller 34, such as feature detection / classification, obstacle mitigation, route traversal, mapping, sensor integration, ground truth determination, etc.

[0057] As briefly mentioned above, Figure 1 The motion plan verification system 100 is included within the ADS 70, e.g., as a separate system or as part of the guidance system 78. In various embodiments, as with the Figure 4 and with reference to Figure 3 embodiments of the motion plan verification system 100 according to the present disclosure can include any number of sub-modules embedded within the controller 34, which can be combined and / or further divided to similarly implement the systems and methods described herein. Further, the inputs to the motion plan verification system 100 can be received from the sensor system 28, retrieved from the data storage device 32, received from other control modules (not shown) associated with the autonomous vehicle 10, received from the communication system 36, and / or determined / modelled by other sub-modules (not shown) within the controller 34 of the autonomous vehicle 10. Further, the inputs can also be subject to pre-processing, such as sub-sampling, noise reduction, normalization, feature extraction, missing data reduction, etc. Figure 1

[0058] In various embodiments, the constraint identification module 102 identifies physical or virtual speed-related constraints at a road longitudinal position, and generates constraint type data 110 and constraint location data 111 based thereon. As described above, the constraint type can be, but is not limited to, a physical stop bar associated with a stop sign in the vicinity, a virtual stop bar associated with a traffic control device, a point along a circular curve that limits the maximum driving speed, a virtual bar created in front of a give-way traffic maneuver, etc. In various embodiments, the constraint identification module 102 determines the constraint type and location based on map data 112 stored in the data storage device 46 of the vehicle 10, based on road information 114 received from, e.g., other vehicles or infrastructure ahead of the vehicle 10, and / or based on sensor data 116 generated by the sensing devices 40a-40n of the vehicle 10.

[0059] ​In various embodiments, the constraint status determination module 104 defines constraint activation logic based on the constraint type data 110 and executes the logic to identify the constraint status 118. In various embodiments, the constraint activation logic defines the constraint status 118 as active or inactive. For example, when the constraint type data 110 indicates that the constraint type is a virtual strip created in front of a yielding traffic maneuver, the constraint status determination module 104 first determines a point of no return (PoNR) and then determines a worst case based on other actors in the area and generates a heuristic plan based on the worst case. In various embodiments, the constraint status determination module 104 determines the PoNR as a virtual location s i based on an offset from the actual location of the stop constraint indicated by the constraint location data 111. Using each possible speed v [T] at which the vehicle 10 can be, the offset is calculated backwards from the location of the stop constraint. The offset is related to the braking distance D(v [T] , 0).

[0060] In various embodiments, the constraint status determination module 104 determines that the vehicle 10 can safely commit to executing the motion plan and thus sets the constraint status 118 to inactive when any of the following conditions apply to the final state of the vehicle: s [T] + D(v [T] , 0) <= s i (the vehicle has not reached the PoNR; or s [T] + D(v [T] , 0) > s i (the vehicle has passed the PoNR) and there exists a safe plan p from this terminal state under worst case prediction. When there does not exist a safe plan p from this terminal state under worst case prediction, the constraint status determination module 104 sets the constraint status 118 to inactive.

[0061] When the constraint type data 110 indicates that the constraint type is a point along a circular curve that limits the maximum travel speed, points along the longitude of the travel lane are sampled at a certain frequency. For each sampled point i, the longitude along the lane s i and the curvature K i of the central curve of the lane (or any base curve along the lane) are stored (determined by the geometry of the road, so can be cached offline). Based on the desired maximum lateral acceleration limit the curvature value is converted to a longitudinal speed limit. These longitudinal speed limits are then used to calculate the braking distance and evaluate the PoNR to set the constraint status 118 as described above.

[0062] When constraint type data 110 indicates that the constraint type is a virtual stop bar associated with a traffic control device, position and speed [(s1,v1)...(s1,v1)] are collected for the traffic control device identified as active. k ,v k For example, traffic control devices such as stop signs are always considered active until feedback from the driver is sent to exceed the plan, or alternatively, vehicle 10 has come to a complete stop for a sufficient period of time. In another example, traffic control devices such as yield signs dynamically switch between active and inactive based on the status and predictions of other vehicles in the connecting lane. In yet another example, traffic control devices such as traffic lights are considered active as long as the traffic light is flashing yellow or displaying full red.

[0063] In various embodiments, the verification module 106 verifies the motion plan against speed limits at specified longitudinal positions of constraints—both within and outside its line-of-sight. The verification module 106 invalidates any motion plan that does not meet the limits. For example, the verification module compares the expected speed from motion plan data 122 with the activity constraints. When the expected speed is less than or equal to all activity constraints within the defined line-of-sight, the motion plan is verified for that line-of-sight, and line-of-sight verification data 124 is generated. However, when the expected speed is greater than at least one activity constraint within the defined line-of-sight, the motion plan is invalidated for that line-of-sight.

[0064] In another example, verification module 106 verifies the out-of-line-of-sight motion plan based on the final pose of the motion plan indicated by motion plan data 122. For example, the motion plan is inherently limited in line of sight. Each plan has a final pose for vehicle 1 to aim at. Therefore, (s [T] ,v [T] The longitudinal position and velocity at time T represent the end pose of the candidate trajectory. If activity constraints are identified outside of this end pose, then all k velocity-related constraint sets {(s1, v1) ... (s...)} are included. k ,v k The constraints of )} are propagated back to the terminal pose (s) [T] ,v [T] This ensures that "dead ends" will not occur in the future. Predefined deceleration kinematic models (e.g., reflecting emergency braking) are used for propagation. For example, D(v [T] ,v i () uses a predefined deceleration curve from velocity v [T] Convert to v i The minimum required travel distance. Therefore, when s [T] +D(v [T] ,v i )<=s i ,(si >s [T] When the exercise plan is validated as "beyond visual range", beyond visual range validation data 126 is generated.

[0065] Now for reference Figure 5 And continue to refer to Figures 1-4 The flowchart illustrates the process that can be performed according to this disclosure by Figure 1 The control method 400 executed by the exercise planning verification system 100. As can be understood from this disclosure, the order of operations in this method is not limited to... Figure 5 The method 400 may not be executed in the order shown, but may be executed in one or more different orders as applicable and in accordance with this disclosure. In various embodiments, method 400 may be scheduled to run based on one or more predetermined events, and / or may run continuously during the operation of the autonomous vehicle 10.

[0066] In one embodiment, the method may begin at 405. Constraints are identified at 410. Subsequently, at 420, it is determined whether the constraint is active. For example, the PoNR method discussed above is performed based on constraint type, where: when it is determined that the vehicle has not passed the PoNR, the state is set to inactive; when it is determined that the vehicle has passed the PoNR, a worst-case scenario is determined. It is then determined whether a safety plan can be established for this worst-case scenario. When a safety plan for the worst-case scenario can be established, the state is set to inactive. When a safety plan for the worst-case scenario cannot be established, the state is set to active.

[0067] If a constraint is determined to be active at 430, the kinematic constraints of the active constraint are extracted at 440, and the existence of other constraints is determined at 450. If a constraint is determined to be inactive at 430, the existence of other constraints is determined at 450.

[0068] If other constraints exist at 450, the method continues to identify constraints at 410. If no additional constraints exist at 450, for example as described above, the in-line-of-sight motion plan is verified at 460 based on the kinematic constraints, and the out-of-sight motion plan is verified at 470 based on the kinematic constraints of all activity constraints. The method may then terminate at 480.

[0069] While at least one exemplary embodiment has been described in the foregoing detailed description, it should be understood that numerous variations exist. It should also be understood that the one or more exemplary embodiments are merely examples and are not intended to limit the scope, applicability, or configuration of this disclosure in any way. Rather, the foregoing detailed description will provide those skilled in the art with a convenient roadmap for implementing one or more exemplary embodiments. It should be understood that various changes can be made to the function and arrangement of the elements without departing from the scope of this disclosure as set forth in the appended claims and their legal equivalents.

Claims

1. A method of controlling an autonomous vehicle, comprising: 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; executing, by the processor, the constraint activation logic to determine a status of the constraint as at least one of active and inactive; verifying, by the processor, a motion plan of the autonomous vehicle based on the constraint when the status of the constraint is active; and selectively controlling the autonomous vehicle based on verification of the motion plan, wherein the constraint activation logic defines the status of the constraint as active based on a determined no-return point, wherein the no-return point is determined based on a braking distance based on all possible speeds of the autonomous vehicle and a location of the constraint. the verifying includes verifying the motion plan within a defined visibility of the autonomous vehicle based on a desired speed of the motion plan.

2. The method of claim 1, wherein, the verifying further includes verifying the motion plan outside the defined visibility of the autonomous vehicle based on a terminal pose of the motion plan.

3. The method of claim 2, wherein, the verifying is further based on a backpropagation of constraints on the terminal pose.

4. The method of claim 3, wherein, 5. The method of claim 1, further comprising identifying a constraint type of the constraint as a physical stop bar associated with a stop sign.

6. The method of claim 1, further comprising identifying, by the processor, a constraint type of the constraint as a virtual stop bar associated with a traffic control device.

7. The method of claim 1, further comprising identifying, by the processor, a constraint type of the constraint as a virtual bar created in front of a yield traffic maneuver.

8. The method of claim 1, further comprising identifying, by the processor, a constraint type of the constraint as a point along a circular curve that limits a maximum driving speed.

9. A system for controlling a vehicle, comprising: 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 define, by a processor, constraint activation logic based on a type of the at least one constraint, execute the constraint activation logic to determine a status of the constraint as at least one of active and inactive, verify a motion plan of the autonomous vehicle based on the constraint when the status of the constraint is active, and selectively control the autonomous vehicle based on verification of the motion plan, wherein the constraint activation logic defines the status of the constraint as active based on a determined no-return point, wherein the no-return point is determined based on a braking distance based on all possible speeds of the autonomous vehicle and a location of the constraint. ​

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