Methods and processes for degradation mitigation in autonomous driving

Abstract

A vehicle and a system method for operating the vehicle are disclosed. The system includes a monitoring module and a mitigation module running on a processor. The monitoring module is configured to measure degradation of the vehicle's operating parameters, with the vehicle operating in a first state based on a first value of a set of adaptive parameters. The mitigation module is configured to determine a threat to the vehicle due to operating it in the first state with degraded operating parameters, and to adjust the set of adaptive parameters from the first value to a second value that mitigates the threat to the vehicle, with the processor operating the vehicle in a second state based on the second value.

Description

Technical Field

[0001] This disclosure relates to the operation of autonomous vehicles, and more specifically to systems and methods for mitigating the impact of malfunctions or degradation in the operation of autonomous vehicles on the safety of the autonomous vehicles, wherein the degradation may be in hardware or software. Background Technology

[0002] Autonomous vehicles acquire data from their environment and navigate based on that data. Data from the environment is obtained using sensors or communication networks, and the vehicle's motion is controlled using actuation systems (i.e., propulsion, steering, braking, etc.). Therefore, the safe operation of a vehicle depends on the performance and relative health of its sensors and drive systems. For example, when the data from the environment is highly deterministic and known, the vehicle can plan its trajectory with a similar sense of certainty regarding vehicle safety. However, when the performance of sensors or actuation systems degrades or is impaired, the trajectory generated by the vehicle can lead to handling challenges. Therefore, it is desirable to be able to adjust the planning and control of autonomous vehicles as these systems degrade to provide accurate vehicle operation. Summary of the Invention

[0003] In one exemplary embodiment, a method for operating a vehicle is disclosed. Degradation of vehicle operating parameters is measured, and the vehicle operates in a first state based on a first value of a set of adaptive parameters. A threat to the vehicle due to operating in the first state with degraded operating parameters is determined. The set of adaptive parameters is adjusted from the first value to a second value that mitigates the threat to the vehicle. The vehicle operates in a second state based on the second value.

[0004] In addition to one or more features described herein, measuring the degradation of the vehicle's operating parameters also includes determining a health metric of at least one of the vehicle's motion system and sensing system. Adjusting the set of adaptive parameters also includes adjusting at least one of the planning parameters of the planning module and the control parameters of the control module. The method further includes operating the vehicle in a second state when the observability of degradation resulting from adjusting the set of adaptive parameters from a first value to a second value is analyzed. The method further includes simulating the second state by simulating perturbations of the adaptive parameter set from the first value to the second value and determining observable degradation of the adaptive parameters in at least one of the planning and control modules. The method further includes taking preventative measures when adjusting the adaptive parameters does not mitigate the threat. The method further includes reducing the cost function to determine the second value of the adaptive parameters.

[0005] In another exemplary embodiment, a system for operating a vehicle is disclosed. The system includes a monitoring module and a mitigation module running on a processor. The monitoring module is configured to measure degradation of operating parameters of the vehicle, which operates in a first state based on a first value of a set of adaptive parameters. The mitigation module is configured to determine a threat to the vehicle due to operation in the first state with degraded operating parameters, and to adjust the set of adaptive parameters from the first value to a second value that mitigates the threat to the vehicle, wherein the processor operates the vehicle in a second state based on the second value.

[0006] In addition to one or more features described herein, the monitoring module is configured to determine a health metric of at least one of the vehicle's motion system and its sensing system. The adaptive parameters also include at least one of the planning parameters of the planning module and the control parameters of the control module. The processor operates the vehicle in a second state when the set of adaptive parameters is adjusted from a first value to a second value at the mitigation module such that the observability of degradation is analyzed. The mitigation module is also configured to simulate the second state by simulating a perturbation of the adaptive parameters from the first value to the second value and to determine observable degradation of the adaptive parameters of at least one of the planning and control modules. When adjusting the adaptive parameters does not mitigate the threat, the mitigation module takes preventative measures. The mitigation module reduces the cost function to determine the second value of the adaptive parameters.

[0007] In yet another exemplary embodiment, a vehicle is disclosed. The vehicle includes a monitoring module and a mitigation module running on a processor. The monitoring module is configured to measure degradation of operating parameters of the vehicle, the vehicle operating in a first state based on a first value of a set of adaptive parameters. The mitigation module is configured to determine a threat to the vehicle due to operating the vehicle in the first state with degraded operating parameters, and to adjust the set of adaptive parameters from the first value to a second value that mitigates the threat to the vehicle, wherein the processor operates the vehicle in a second state based on the second value.

[0008] In addition to one or more features described herein, the monitoring module is configured to determine a health metric of at least one of the vehicle's motion system and its sensing system. This set of adaptive parameters also includes at least one of planning parameters from the planning module and control parameters from the control module. The processor operates the vehicle in a second state when the set of adaptive parameters is adjusted from a first value to a second value at the mitigation module such that the observability of degradation is analyzed. The mitigation module is also configured to simulate the second state by simulating a perturbation of the adaptive parameter set from the first value to the second value and to determine degradation observable by the adaptive parameters of at least one of the planning and control modules. When adjusting the set of adaptive parameters fails to mitigate the threat, the mitigation module takes preventative measures. Attached Figure Description

[0009] The above-described features and advantages, as well as other features and advantages, of this disclosure will become apparent from the following detailed description when taken in conjunction with the accompanying drawings. Brief description of the attached diagram

[0011] Other features, advantages, and details appear only by way of example in the following detailed description, which refers to the accompanying drawings, wherein:

[0012] Figure 1 An autonomous vehicle according to an exemplary embodiment is shown;

[0013] Figure 2 A schematic diagram is shown of a system for adjusting the operation of an autonomous vehicle when it experiences degradation of its various operating parameters.

[0014] Figure 3 The illustration shows Figure 2 The flowchart of the method executed at the system's mitigation module;

[0015] Figure 4 A flowchart illustrating a method for determining whether uncertainty / degradation can be observed through adaptive parameters is shown.

[0016] Figure 5 It shows the method for... Figure 2 The flowchart shows the process of adjusting the planning parameters of the system's planning module through offline and online calculations.

[0017] Figure 6 A flowchart of the control module's adaptation strategy is shown;

[0018] Figure 7 The graphs shown illustrate the impact of adjusting the adaptation parameters of the planning and control modules on the final trajectory of the vehicle.

[0019] Figure 8 A top view of a road segment with a left lane and a right lane or overtaking lane is shown in an illustrative embodiment;

[0020] Figure 9 It shows Figure 8 The curves showing the lateral positions of the first and second trajectories over time;

[0021] Figure 10 It shows Figure 8 The graphs showing the lateral acceleration values ​​of the first and second trajectories as a function of time; and

[0022] Figure 11 A graph illustrating the effect of adjusting only the control parameters is shown. Detailed Implementation

[0023] The following description is exemplary in nature only and is not intended to limit this disclosure, its application, or use. It should be understood that in all the figures, corresponding reference numerals denote the same or corresponding parts and features. As used herein, the term module refers to processing circuitry, which may include application-specific integrated circuits (ASICs), electronic circuitry, processors (shared, dedicated, or grouped) and memories executing one or more software or firmware programs, combinational logic circuitry, and / or other suitable components that provide the described functionality.

[0024] According to an exemplary embodiment, Figure 1 An autonomous vehicle 10 is illustrated. In an exemplary embodiment, the autonomous vehicle 10 is a so-called Level 4 or Level 5 automation system. A Level 4 system signifies "high automation," referring to the driving mode-specific performance of the automated driving system in all aspects of a dynamic driving task, even if the human driver does not respond appropriately to intervention requests. A Level 5 system signifies "full automation," referring to the full-time performance of the automated driving system in all aspects of a dynamic driving task under all road and environmental conditions that a human driver can manage. It should be understood that the systems and methods disclosed herein can also be used with autonomous vehicles operating at any level from Level 1 to Level 5.

[0025] Autonomous vehicle 10 typically includes at least a navigation system 20, a propulsion system 22, a transmission system 24, a steering system 26, a braking system 28, a sensing system 30, an actuator system 32, and a controller 34. The propulsion system 22, transmission system 24, steering system 26, braking system 28, and actuator system 32 can be collectively referred to as motion system 33. Navigation system 20 determines road-level route plans for autonomous driving of autonomous vehicle 10. Propulsion system 22 provides power for generating prime mover for autonomous vehicle 10 and, in various embodiments, may include an internal combustion engine, an electric motor such as a traction motor, and / or a fuel cell propulsion system. Transmission system 24 is configured to transmit power from propulsion system 22 to two or more wheels 16 of autonomous vehicle 10 according to a selectable speed ratio. Steering system 26 affects the position of two or more wheels 16. Although depicted as including a steering wheel 27 for illustrative purposes, in some embodiments contemplated within the scope of this disclosure, steering system 26 may not include a steering wheel 27. Braking system 28 is configured to provide braking torque to two or more wheels 16.

[0026] The sensing system 30 includes a radar system 40 that senses objects in the external environment of the autonomous vehicle 10 and determines various parameters of the objects for locating the position and relative speed of various remote vehicles in the autonomous vehicle environment. These parameters can be provided to the controller 34. In operation, the transmitter 42 of the radar system 40 emits a radio frequency (RF) reference signal 48, which is reflected back at the autonomous vehicle 10 by one or more objects 50 in the field of view of the radar system 40 as one or more echo signals 52, which are received at the receiver 44. The one or more echo signals 52 can be used to determine various parameters of the one or more objects 50, such as the distance of the object, the Doppler frequency or relative radial velocity of the object, and the azimuth angle, etc. The sensing system 30 includes additional sensors for identifying road features, such as digital cameras, lidar, inertial navigation systems, GPS, etc.

[0027] The communication system 60 enables communication with remote devices, such as traffic servers, infrastructure equipment, and GPS systems. It receives data on weather conditions, traffic conditions and flow, road construction and maintenance, etc. The controller 34 constructs the trajectory of the autonomous vehicle 10 based on the output of the sensing system 30 and the information received via the communication system 60. The controller 34 can provide the trajectory to the motion system 33 to navigate the autonomous vehicle 10 relative to one or more objects 50 and through traffic conditions.

[0028] The controller 34 includes a processor 36 and a computer-readable storage device or storage medium 38. The storage medium includes a program or instructions 39 that, when executed by the processor 36, operate the autonomous vehicle 10. The storage medium 38 may also include the program or instructions 39 that, when executed by the processor 36, enable the processor to plan a trajectory and control the vehicle to follow that trajectory. Furthermore, the processor 36 can measure vehicle operating parameters (i.e., operating parameters of its sensing system 30 or motion system 33) that affect its precise operation and adjust adaptive parameters to influence trajectory planning and vehicle control, thereby achieving precise maneuvering given any constraints imposed by degradation of operating parameters.

[0029] Figure 2 A schematic diagram of system 200 is shown, which is used to adjust the operation of autonomous vehicle 10 as it experiences degradation of various operating parameters. Autonomous vehicle 10 includes a sensing system 30, a processor 36, and a motion system 33. The sensing system 30 measures or receives data about the surrounding environment and transmits that data to the processor 36. In various embodiments, the sensing system 30 may also include sensors that monitor the movements or attention of passengers or the driver, such as eye trackers. The processor 36 performs various calculations to plan the vehicle's trajectory, thereby controlling the operation of the motion system 33 to move the vehicle along that trajectory.

[0030] Processor 36 operates operation module 202 to control the autonomous operation of the vehicle. Operation module 202 includes route module 204, perception / localization module 206, planning module 208, and control module 210. Route module 204 may be a route program running on a processor such as processor 36. Similarly, perception / localization module 206 may be a perception / localization program running on a processor, planning module 208 may be a planning program running on a processor, and control module 210 may be a control program running on a processor. These modules may run on a single processor, or each module may run on its own dedicated processor, or some modules may run on a common processor while others run on dedicated processors. Route module 204 plans a route for the vehicle based on traffic information and input from passengers, such as the vehicle's desired destination. Perception / localization module 206 extracts information from sensed data, which gives the autonomous vehicle perception of its surrounding environment, such as speed limits, traffic lights, and traffic conditions. Perception / localization module 206 may also use data, such as the distance, orientation, height, and speed of objects relative to the vehicle, to determine the position and orientation of the autonomous vehicle 10 in its environment. The planning module 208 plans the trajectory of the autonomous vehicle 10 based on the results of the perception / localization module 206. The control module 210 controls the vehicle to move along the trajectory provided by the planning module 208.

[0031] The sensing / positioning module 206, planning module 208, and control module 210 use data and feedback from the sensing system 30 to perform their functions. Furthermore, the planning module 208 and control module use data and feedback from the motion system 33 to perform their functions.

[0032] In one embodiment, each of these operation modules 202 generates an output parameterized by an adaptive parameter. For example, the planning module 208 may be constrained by an adaptive distance parameter indicating the maximum distance at which a trajectory can be planned. This maximum distance can be adjusted (e.g., shortened) when the input data does not provide sufficient data for a safe trajectory at the maximum distance, or when the operation of the motion system is weakened to the point where precise operation at the maximum distance is uncertain.

[0033] The processor 36 also operates the monitoring module 212 and the mitigation module 214. The monitoring module 212 receives data from the motion system 33 and the sensing system 30 and determines the degree of degradation or uncertainty in their operation. The health status of the motion system 33 and / or the sensing system 30 is represented by measurable operating parameters. Illustrative operating parameters include the range of the radar system 40, the resolution of the digital camera, the tire friction against the road, the effectiveness of the braking system 28, etc. The monitoring module 212 measures the operating parameters and generates a health metric representing the degree of degradation of those operating parameters.

[0034] The monitoring module 212 also receives information from the infrastructure system 216, which can indicate road construction, poor road conditions, traffic conditions, etc. The monitoring module 212 also receives environmental data 218 that can affect vehicle operation, such as the presence of snow, wet road conditions, poor lighting conditions, etc. Health measurements can also be based on information from the infrastructure system 216 and environmental data 218. The monitoring module 212 can also receive information from the passengers of vehicle 10.

[0035] The mitigation module 214 receives health metrics and all other information required for planning and control, such as traffic data and traffic forecasts 220, from the monitoring module 212, and uses this input to determine the impact of degradation of operating parameters on vehicle operation. The mitigation module 214 further determines any adjustments to the adaptive parameters of the operation module 202 that can mitigate the effects of degradation and change the adaptive parameters to maintain accurate vehicle operation under given degradation conditions.

[0036] Figure 3 A flowchart 300 illustrating the method performed at mitigation module 214 in an illustrative embodiment is shown. Mitigation module 214 performs the method across activation layer 302, adaptation layer 304, and communication layer 306. Activation layer 302 determines whether degradation of various operating parameters affects vehicle operation. Adaptation layer 304 adjusts one or more adaptation parameters of operation module 202, such as planning module 208 and control module 210. Communication layer 306 communicates the adjustments to the passenger or driver, depending on the success of the adjustments in mitigating the effects of degraded operating parameters.

[0037] The method begins at box 308 in activation layer 302. In box 310, health metrics (i.e., uncertainty metrics, driving performance metrics, etc.) are received at mitigation module 214. In box 312, threat analysis is performed to determine the extent to which any degradation of health metrics affects the operation of vehicle 10. Threat analysis may include current operating parameters of planning module 208 and control module 210, as shown in box 314, as well as various perception data and predictions, as shown in box 316 (received from traffic data and traffic prediction 220). In box 318, processor 36 determines the course of action based on the results of the threat analysis performed in box 312. If no threat is detected, the method proceeds to box 320, and the vehicle operates without adjusting adaptive parameters. If a threat is detected, the method proceeds to box 322 of adaptation layer 304.

[0038] In adaptation layer 304, processor 36 determines which adaptive parameter or set of adaptive parameters will be used to mitigate the effects of operational parameter degradation and makes appropriate adjustments. In block 322, the processor simulates changes in the adaptive parameters of both the planning and control modules and determines whether such changes make degradation observable. If degradation is observed due to adjustments in the adaptive parameters of the planning and control modules, the method proceeds to block 324, where adjustments are made at planning module 208 and control module 210. If no degradation is observed by adjusting the adaptive parameters of the two modules, the method proceeds to block 326.

[0039] In block 326, the processor simulates changes in a set of adaptive parameters of the planning module and determines whether degradation is observable through one or more parameters. If degradation is observable, the process proceeds to block 328, where the adaptive parameters are adjusted at planning module 208.

[0040] If the planning module does not observe degradation, the method proceeds to block 330. In block 330, processor 36 simulates changes in a set of adaptive parameters of the control module and determines whether degradation can be observed through the parameters, or in other words, whether it is a result of parameter changes. If degradation is observed by adjusting the adaptive parameters of the control module, the method proceeds to block 332, where the adaptive parameters are adjusted at control module 210.

[0041] From any of blocks 324, 328, and 332, the method proceeds to block 334 of the communication layer 306. In block 334, the processor 36 determines whether fault mitigation (i.e., adjustment of the adaptive parameters) has successfully mitigated the effects of degradation. If the threat has been successfully mitigated, the method proceeds to block 336, where the driver or passenger is notified of the degradation of health indicators, and the resulting adjustment is input to the appropriate one of the operation modules 202. The vehicle then changes from a first state using a trajectory and control based on a first value or old value of the adaptive parameters to a second state using a trajectory and control based on a second value or new value of the adaptive parameters. If, in block 334, the adjustment is unsuccessful in avoiding the threat, the method proceeds to block 338, where the vehicle takes further preventative measures, such as deactivating vehicle features, generating an alert indicating a fault to the driver or passenger, suggesting alternative routes including repair shops, or any combination thereof.

[0042] Figure 4 Flowchart 400 illustrates a method for determining whether uncertainty / degradation can be observed through adaptive parameters. In block 402, a basic utility function J for operating the vehicle is determined using adaptive parameters in its current state (i.e., the first value of the adaptive parameters). base The basic utility function J base It is based on the adaptive parameters of planning module 208 (i.e., planning parameters θ). PL The utility function and the adaptive parameters based on the control module 210 (i.e., the control parameters θ) K The linear combination of the utility functions of ) is shown in formula (1):

[0043]

[0044] J cont To control the utility function, J plan Let be the utility function for planning, where θ∈{θ PL θ K} represents the planning parameter θ PL and control parameter θ K At least one of the parameters α1 and α2 can be selected for sensitivity analysis of equation (1), applicable to planning module 208, control module 210, or a combination thereof. In block 404, disturbance analysis is performed using the disturbance adaptive parameter θ, as shown in equation (2):

[0045] θ d =θ(1+β) Formula (2)

[0046] Where θd is the perturbation value of the adaptive parameter, and the perturbation parameter β∈{β PL ,β K} is applied to the planning parameter θPL Planning disturbance β PL and applied to control parameter θ K Control perturbation β K At least one of them.

[0047] In box 406, the perturbation value θ is based on the adaptive parameters. d Calculate the disturbance utility function J dist As shown in formula (3):

[0048]

[0049] It should be noted that since the output of the planning module 208 is used as the input of the control module 210, the planning parameter θ PL The impact of disturbances on the planning utility function J plan and control utility function J cont Control parameter θ K The disturbance in the control only affects the control utility function J. cont .

[0050] In box 408, the Jacobian matrix is ​​determined based on the basic utility function and the perturbation utility function, as shown in formula (4):

[0051]

[0052] In box 410, the Jacobian matrix is ​​compared with a selected threshold. If the Jacobian is not less than or equal to the threshold, the method proceeds to box 412, and degradation is observed by planning module 208 and control module 210. If the Jacobian matrix is ​​less than or equal to the threshold, the method proceeds to box 414, and degradation is not observed by planning module 208 and control module 210.

[0053] The process of adjusting adaptive parameters (i.e., Figure 3 The process in box 324 is performed using an optimization equation for driver comfort to reduce the cost function, as shown in equation (5).

[0054]

[0055] Where X is the current state of the vehicle, X ref It is the vehicle's reference state, Θ PL It is a set of adaptive programming parameters, θ PL，ref It is a set of reference (original) planning parameters, θ K It is a set of adaptive control parameters, θ K，ref This is a set of reference (original) control parameters. Parameters Q1, Q2, and Q3 are adaptive weighting factors that can be adjusted based on degradation or faults detected in the system. X jerkThis is the jerk parameter. Formula (5) is subject to the following constraints:

[0056] X ref =P(X, x) obst θ PL ) Formula (6)

[0057] U = K(X, Y, θ) K ) Formula (7)

[0058] Y = h(X, ω) Formula (8)

[0059]

[0060] Where P is the planning strategy, K is the control strategy, and Y is a function of the vehicle's state, measurements, and uncertainty ω. It is a function of system dynamics based on vehicle state X, where X is... obst This is the predicted location of the obstacle. The predicted location of the vehicle is X. veh and the predicted location X of the obstacle obst Constrained by the safety limits of formula (10):

[0061]

[0062] It indicates that their positions do not overlap.

[0063] Figure 5 The process of adjusting the planning parameters of planning module 208 for offline and online calculations is shown (i.e., Figure 3 The process is described in flowchart 500 (see box 328). Planning parameters can be behavioral parameters or trajectory parameters. Behavioral parameters adjust the behavior planning procedure operating at planning module 208, which generates high-level objectives for the driving scenario, such as the target speed or target lane the vehicle needs to reach. Trajectory parameters affect the trajectory planning procedure of planning module 208, which uses the high-level objectives from the behavior planning procedure and generates a detailed trajectory of the vehicle's future motion to be used by control module 210. The process begins at box 502. In box 504, health metrics are obtained. Health metrics are sent to an adaptation table to modify various parameters. The adaptation table is pre-calibrated using the results of offline testing.

[0064] In box 506, health metrics are used in the behavior parameter table to identify adjustments to the behavior parameters. In box 508, the behavior parameters are adjusted. Behavior parameters define a set of permissible vehicle maneuvers, or ways in which the vehicle plans or follows a trajectory, such as permitted lanes, lane speed limits, maximum acceleration, maximum direction of travel, etc. In box 510, the adjusted behavior parameters are used to execute an updated behavior planning procedure.

[0065] In box 512, health metrics are used in the trajectory parameter table to identify adjustments to the planning parameters. In box 514, a new trajectory is planned based on the adjusted trajectory planning parameters. In box 516, the new trajectory is executed using an updated behavior planner. In box 518, threat analysis is performed using the new trajectory plan, for example, using the constraints of formula (10). When the threat analysis in box 518 is performed, various sensed uncertainties and estimated uncertainties are provided from box 520. Box 522 is the decision box based on the threat analysis in box 518. If a threat is detected, the method proceeds to box 524, where a feedback adaptation strategy is implemented. Feedback is sent to the adaptation tables in boxes 506 and 512 to obtain a new set of updated behavior parameters and trajectory planning strategies. If no threat is detected in box 522, the method proceeds to box 526, where updated parameters are used to plan the trajectory. In box 528, the trajectory is sent to the control module for execution.

[0066] Figure 6 A flowchart 600 illustrating the control module's adaptation strategy is shown. The route module 204 provides a route plan to the planning module 208, and the perception / localization module 206 provides information extracted from sensing data to the planning module 208, providing a perception of the surrounding environment. The planning module 208 plans the trajectory of the autonomous vehicle 10 and provides the trajectory to the control module 210. The control module 210 adjusts the control parameters based on the trajectory. The process of adjusting the control parameters (i.e., Figure 3 The process in box 332 includes performing an optimization process to reduce the control cost function involved in formula (11):

[0067]

[0068] Subject to the following restrictions:

[0069] X(k+1)=f(X(k),U(k)) Formula (12)

[0070] D min ≤x1-x 1，lead ≤D max Formula (13)

[0071] A long，min ≤x5≤A long，max Formula (14)

[0072] A lat，min ≤x6≤A lat，max Formula (15)

[0073] Where U is a set of control actions, such as torque or steering angle:

[0074] U = [T, U] steer ] Formula (16)

[0075] and

[0076] X=[x1,x2,x3,x4,x5,x6] T Formula (17)

[0077] Where x1 is the longitudinal position, x2 is the lateral position, x3 is the longitudinal velocity, x4 is the lateral velocity, x5 is the longitudinal acceleration, and x6 is the lateral acceleration. The adjusted control parameters are provided to vehicle 10. The adjusted control parameters are also provided back to planning module 208. Planning module 208 is thus informed of changes in vehicle 10's behavior, such as changes in response time. Vehicle 10 provides data feedback to control module 210. This feedback can be combined with the planned trajectory parameters at combiner 602. The dynamic data of vehicle 10 is also sent to monitoring module 212, which determines constraint 604, which can be used at control module 210 during subsequent iterations where the control parameters are adjusted. In one embodiment, the controller gain can be adjusted based on diagnostic system information. In another embodiment, the controller constraint / reference is adapted to ensure that the actuation signal keeps the system within a safe and acceptable range.

[0078] Figure 7 Figure 700 is shown, illustrating the impact of adjusting the adaptation parameters of the planning module 208 and the control module 210 on the final trajectory of the vehicle. Figure 700 is merely an illustration of one possible operation of the method disclosed herein and is not intended to limit the invention. Figure 700 is discussed in relation to lateral movement occurring during lane changes. For each figure, time is displayed in seconds along the horizontal axis, and lateral position is displayed in meters along the vertical axis.

[0079] Graph 702 shows the planned trajectory 704, which includes a lateral movement of about 2 meters within about 1 second that occurs at t = 5 seconds.

[0080] Graph 706 shows the achieved trajectory of a vehicle attempting to replicate the planned trajectory 704 of graph 702. A first achieved trajectory 708 appears when the coefficient of friction between the vehicle tires and the road is approximately 0.83. A second achieved trajectory 710 appears when the coefficient of friction is approximately 0.33. Both achieved trajectories exhibit an overshoot (e.g., 2 meters) to the target lateral position. Furthermore, the second achieved trajectory 710 has a higher overshoot and requires additional time (at least 5 seconds) to finally reach the target lateral position. This indicates that vehicle behavior is sensitive to uncertainties in the input information (in this case, road friction).

[0081] Graph 712 shows the replanned trajectory 714 of the lateral movement generated using the adjusted planning parameters. The planned trajectory 704 is copied in graph 712 for comparison. The replanned trajectory 714 allows for additional time for the lateral movement (approximately 2 seconds).

[0082] Graph 716 shows the achieved trajectory of a vehicle attempting to replicate the replanned trajectory 714 of graph 712. A third achieved trajectory 718 appears when the coefficient of friction between the vehicle tires and the road is approximately 0.83. A fourth achieved trajectory 720 appears when the coefficient of friction is approximately 0.33. Compared to the first achieved trajectory 708 of graph 706, the third achieved trajectory 718 exhibits little or no lateral overshoot. Similarly, compared to the second achieved trajectory 710 of graph 706, the fourth achieved trajectory 720 exhibits some overshoot, but is able to recover to the target lateral position in a relatively short time.

[0083] Graph 722 shows the trajectory achieved when both the planning and control parameters are adjusted. Regardless of the accuracy of the friction coefficient estimation, the achieved trajectory 728 conforms to the replanned trajectory 714 without significantly deviating from the target lateral position.

[0084] Figure 8A top view of a road segment 800 with a left lane 802 or overtaking lane and a right lane 804 is shown in an illustrative embodiment. A primary vehicle 806 and an obstacle vehicle 808 are traveling in the right lane 804, with the primary vehicle 806 behind but approaching the obstacle vehicle 808. The primary vehicle 806 plans a trajectory to pass the obstacle vehicle 808 using the left lane 802, which has a degraded scenario in which the obstacle vehicle is detected later than normal (i.e., the vehicle has a shorter distance). Two trajectories are shown. The first trajectory 810 shows a constant-speed maneuver in which the primary vehicle 806 passes the obstacle vehicle 808 without changing its speed. The second trajectory 812 shows a variable-speed maneuver in which the primary vehicle 806 changes its speed to overtake the obstacle vehicle 808. In the variable-speed maneuver, the primary vehicle 806 approaches the obstacle vehicle 808 at a selected speed, decelerates to match the planned passing speed, and changes lanes at an appropriate speed. The primary vehicle 806 then accelerates to overtake the obstacle vehicle 808, and once it has fully passed the obstacle vehicle 808, it changes back to the right lane 804. The second trajectory 812 allows for a larger gap between the primary vehicle 806 and the obstacle vehicle 808 compared to the first trajectory 810. The first trajectory 810 can be executed safely, but requires faster lateral acceleration designed by the adaptive planning module during lane changes because a shorter gap exists when the obstacle vehicle is detected due to sensing degradation. However, the adaptive parameters of the planning module 208 can be altered to produce the second trajectory 812, where the maneuver has a variable speed but lower lateral acceleration. Driver preference can be used to select one of the aforementioned trajectories.

[0085] Figure 9 It shows Figure 8 The graph 900 shows the lateral position changes of the first trajectory 810 and the second trajectory 812 over time. Time is displayed in seconds along the horizontal axis, and the lateral position (y) is displayed in meters along the vertical axis. As shown in curve 902, the first trajectory 810 begins to change from the right lane 804 to the left lane 802 at approximately t = 9 seconds, and reaches the left lane 802 at approximately t = 12 seconds. The first trajectory 810 leaves the left lane 802 at approximately t = 19 seconds and returns to the right lane 804 at approximately t = 25 seconds.

[0086] As shown by curve 904, the second trajectory 812 begins to change from the right lane 804 to the left lane 802 at approximately t = 8 seconds, and reaches the left lane 802 at approximately t = 12 seconds. The second trajectory 812 then leaves the left lane 802 at approximately t = 25 seconds and returns to the right lane 804 after t = 30 seconds.

[0087] Figure 10 It shows Figure 8Graph 1000 shows the lateral acceleration values ​​of the first trajectory 810 and the second trajectory 812 as a function of time. Time is displayed in seconds on the horizontal axis, and lateral acceleration (ay) is displayed in meters per second² on the vertical axis. As shown in graph 1002, when the vehicle changes from the right lane 804 to the left lane 802 (e.g., from approximately t = 9 seconds to approximately t = 15 seconds), the lateral acceleration of the first trajectory 810 shows a large and abrupt change. When the vehicle changes back from the left lane 802 to the right lane 804 (e.g., from approximately t = 19 seconds to approximately t = 25 seconds), the lateral acceleration of the first trajectory 810 also shows a large and abrupt change.

[0088] As shown in curve 1004, compared to the lateral acceleration of the first trajectory 810 within the same time period, the lateral acceleration of the second trajectory 812 shows a smaller, less abrupt change when the vehicle changes from the right lane 804 to the left lane 802 (e.g., from approximately t = 8 seconds to approximately t = 15 seconds). Similarly, compared to the lateral acceleration of the first trajectory 810 within the same time period, the lateral acceleration of the second trajectory 812 also shows a less abrupt change when the vehicle changes from the left lane 802 to the right lane 840 (e.g., from approximately 19 seconds to approximately 25 seconds). Figure 8 The trajectory shows that different mitigation strategies can be safe, while resulting in different driving performance.

[0089] Figure 11 Graphs illustrating the effect of adjusting only the control parameters are shown. For each graph, time is displayed in seconds along the horizontal axis, and lateral distance in meters along the vertical axis. Graph 1100 shows the planned and achieved trajectories with first values ​​of the control parameters. The planned trajectory 1102 provides approximately 1.8 meters of lateral movement at t = 5 seconds and returns to its original lateral position at approximately t = 25 seconds. The first achieved trajectory 1104 is an achieved trajectory with approximately 0.83 road friction. During the first lateral movement (t = 5 seconds), the first achieved trajectory 1104 exceeds the planned trajectory 1102 by approximately 0.2 meters for approximately 5 seconds, and is then able to align with the planned trajectory. Similarly, during the second lateral movement (t = 25 seconds), the first achieved trajectory 1104 exceeds the planned trajectory 1102 by approximately 0.2 meters and eventually aligns with the planned trajectory after approximately 5 seconds.

[0090] The second achieved trajectory 1106 shows an achieved trajectory with a road friction force of approximately 0.33. During the first lateral movement, the second achieved trajectory 1106 exceeds the planned trajectory 1102 by approximately 0.4 meters for approximately 10 seconds before being able to align with the planned trajectory. Similarly, during the second lateral movement, the second achieved trajectory 1106 exceeds the planned trajectory 1102 by approximately 0.4 meters, eventually aligning with the planned trajectory after approximately 10 seconds.

[0091] Graph 1110 shows the planned and achieved trajectories with a second value of control parameters, which are adjusted to address degradation of the operating parameters of motion system 33. A planned trajectory 1102 from graph 1100 is shown for reference. The third achieved trajectory 1112 is an achieved trajectory with a road friction of approximately 0.83. The third achieved trajectory 1112 gradually approaches the planned trajectory 1102 over a time interval of approximately 10 seconds during the first lateral movement, and similarly approaches the planned trajectory over a time interval of approximately 10 seconds during the second lateral movement. The fourth achieved trajectory 1114 is an achieved trajectory with a road friction of approximately 0.33. The fourth achieved trajectory 1114 follows or substantially follows the same trajectory as the third achieved trajectory 1112.

[0092] Using the second value of the control parameters, the lateral movements of the third and fourth realized trajectories 1112 and 1114 are slower than those of the first and second realized trajectories 1104 and 1106. However, the third and fourth realized trajectories 1112 and 1104 are identical or substantially identical, independent of road conditions (i.e., independent of the accuracy of the estimated road friction). Therefore, by adjusting the control parameters to avoid unsafe driving, the effects of road condition uncertainty can be mitigated.

[0093] While the foregoing disclosure has been described with reference to exemplary embodiments, those skilled in the art will understand that various changes can be made and equivalents can replace its elements without departing from its scope. Furthermore, many modifications can be made to adapt particular situations or materials to the teachings of this disclosure without departing from its essential scope. Therefore, it is intended that this disclosure be limited to the specific embodiments disclosed, but will include all embodiments falling within its scope.

Claims

1. A method of operating a vehicle, comprising: The degradation of vehicle operating parameters is measured, and the vehicle operates in a first state based on the first value of the adaptive parameters; Determine the threat posed to the vehicle due to operating it in the first state under conditions of degraded operating parameters; The adaptive parameter is adjusted from the first value to a second value that mitigates the threat to the vehicle; The second state is simulated by simulating the perturbation of the adaptive parameters from the first value to the second value. Identify degradation in the vehicle's planning or control modules; and The vehicle is operated in the second state based on the second value.

2. The method of claim 1, wherein, Measuring the degradation of vehicle operating parameters also includes determining a health metric for at least one of the following: (i) the vehicle’s motion system; and (ii) the vehicle’s sensing system.

3. The method according to claim 1, wherein adjusting the adaptive parameters further includes adjusting at least one of the following: (i) the planning parameters of the planning module; and (ii) the control parameters of the control module.

4. The method of claim 3, further comprising operating the vehicle in the second state when the observability of the degradation is analyzed due to adjusting the adaptive parameter from the first value to the second value.

5. The method of claim 1, further comprising taking preventative measures when adjusting the adaptive parameters fails to mitigate the threat.

6. The method of claim 3, further comprising reducing the cost function to determine a second value for the adaptive parameter.

7. A system for operating a vehicle, comprising: a monitoring module running on the processor, the monitoring module being configured to measure a degradation of an operating parameter of the vehicle, the vehicle operating in a first state based on a first value of the adaptive parameter; and A mitigation module running on the processor is configured to: Determine the threat posed to the vehicle due to operating it in the first state under conditions of degraded operating parameters; The adaptive parameter is adjusted from the first value to a second value that mitigates the threat to the vehicle; The second state is simulated by simulating the perturbation of the adaptive parameters from the first value to the second value. Identify degradation in the vehicle's planning or control modules; and The processor operates the vehicle in the second state based on a second value.

8. The system of claim 7, wherein, The monitoring module is also configured to determine a health metric of at least one of the following: (i) the vehicle’s motion system; and (ii) the vehicle’s sensing system.

9. The system according to claim 7, wherein the adaptive parameters further include at least one of the following: (i) planning parameters of the planning module; and (ii) control parameters of the control module.

10. The system of claim 9, wherein, When the adaptive parameter is adjusted from the first value to the second value at the mitigation module, the processor operates the vehicle in the second state, so that the observability of the degradation is analyzed.

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