CONTROLLING A VEHICLE TRAJECTORY FOR PRECISE ROUTE TRACKING

Abstract

A method for controlling a vehicle by one or more controllers includes determining an intended path of travel of the vehicle. The method further includes determining positions of virtual attraction nodes on or generally on the intended path of travel. In addition, the method includes determining positions of virtual repulsion nodes along one or more boundaries spaced from the intended path of travel or located at obstacles proximate the intended path of travel. The method additionally includes calculating virtual attraction forces between one or more positions of the vehicle and the virtual attraction nodes. Furthermore, the method includes calculating virtual repulsion forces between one or more positions of the vehicle and the virtual repulsion nodes.The method additionally comprises controlling a trajectory of the vehicle at least partially using the virtual attractive forces and the virtual repulsive forces.

Description

INTRODUCTION

[0001] This description refers to the field of vehicle trajectory control.

[0002] A system for reliable trajectory control in autonomous or semi-autonomous vehicles can be advantageous. DESCRIPTION

[0003] A method for controlling a vehicle by one or more controllers includes determining an intended path of travel of the vehicle. The method further comprises determining positions of virtual attraction nodes on the intended path of travel. In addition, the method includes determining positions of virtual repulsion nodes along one or more boundaries that are spaced from the intended path of travel or located at obstacles proximate the intended path of travel. The method additionally includes calculating virtual attraction forces between one or more positions of the vehicle and the virtual attraction nodes. Furthermore, the method includes calculating virtual repulsion forces between one or more positions of the vehicle and the virtual repulsion nodes.The method additionally comprises controlling a trajectory of the vehicle at least partially using the virtual attractive forces and the virtual repulsive forces.

[0004] The method may also include assigning different weights to different virtual attraction nodes, where the different weights represent different virtual attraction forces generated by the different virtual attraction nodes. Further, the method may include assigning different weights to different virtual repulsion nodes, where the different weights represent different virtual repulsion forces generated by the different virtual repulsion nodes. At least some of the virtual repulsion nodes may be constructed as circles, where the radii of the circles represent the weights of at least some virtual repulsion nodes. At least some of the virtual repulsion nodes constructed as circles may be located at obstacles that the vehicle must avoid.

[0005] The method may further include using the virtual attractive forces and the virtual repulsive forces to determine costs, wherein controlling a trajectory of the vehicle further includes using the costs in a model predictive controller that controls the trajectory of the vehicle.

[0006] The method may also include controlling the vehicle's trajectory in a comfort mode and a precision mode, with both comfort mode and precision mode using control points, with precision mode using a larger number of control points than comfort mode. Precision mode may be used in areas of an upcoming vehicle steering maneuver, an upcoming road intersection, an upcoming road ramp, a construction zone, or during relatively high traffic volumes.

[0007] A second method for controlling a vehicle by one or more controllers includes determining an intended path of travel of the vehicle. The method further includes determining positions of first virtual repulsion nodes along a first boundary spaced from the intended path of travel and determining positions of second virtual repulsion nodes along a second boundary spaced from the intended path of travel. The method further includes using at least some of the first virtual repulsion nodes to generate a first virtual repulsion curve and using at least some of the second virtual repulsion nodes to generate a second virtual repulsion curve.The method further includes calculating first virtual repulsion forces between one or more positions of the vehicle and the first virtual repulsion curve and calculating second virtual repulsion forces between one or more positions of the vehicle and the second virtual repulsion curve. Furthermore, the method includes steering the vehicle at least partially using the first virtual repulsion forces and the second virtual repulsion forces.

[0008] The second method may further include determining positions of virtual attraction nodes located along or generally along the intended travel path, using at least some of the virtual attraction nodes to generate a virtual attraction curve, and generating virtual attraction forces between one or more positions of the vehicle and the virtual attraction curve. The method may also include steering the vehicle at least partially using the virtual attraction forces, the first virtual repulsion forces, and the second virtual repulsion forces. The virtual attraction and repulsion curves may be Bezier curves.

[0009] The second method of controlling a vehicle may include steering the vehicle using a comfort mode and a precision mode, wherein in precision mode, the first virtual repulsion curve and the second virtual repulsion curve are closer together than in comfort mode. The precision mode may be used in areas of an upcoming vehicle steering maneuver, an upcoming road intersection, an upcoming road ramp, a construction zone, or during relatively high traffic volumes.

[0010] A vehicle comprises one or more steered wheels and one or more electronic controls suitable for controlling the steered wheels.The one or more controllers are collectively programmed with the following instructions: determining an intended path of travel of the vehicle; determining positions of virtual attraction nodes along the intended path of travel; determining positions of virtual repulsion nodes along one or more boundaries spaced from the intended path of travel or located at obstacles proximate the intended path of travel; calculating virtual attraction forces between one or more positions of the vehicle and the virtual attraction nodes; calculating virtual repulsion forces between one or more positions of the vehicle and the virtual repulsion nodes; and controlling the steered wheels using at least in part the virtual attraction forces and the virtual repulsion forces.

[0011] The vehicle may also include one or more controllers collectively programmed with an instruction to assign different weights to different virtual attraction nodes, where the different weights represent different virtual attraction forces generated by the different virtual attraction nodes. The vehicle may also include one or more controllers collectively programmed to assign different weights to different virtual repulsion nodes, where the different weights represent different virtual repulsion forces generated by the different virtual repulsion nodes.

[0012] In the vehicle, at least some of the virtual repulsion nodes may be arranged on circles, the radii of the circles representing the weights of the at least some virtual repulsion nodes. The vehicle may also include one or more controllers jointly programmed to use the virtual attractive forces and the virtual repulsive forces to determine costs, wherein the instruction for controlling the steered wheels further includes an instruction for using the costs in a model predictive controller that controls the steered wheels of the vehicle.

[0013] Asymmetric error constraints can be provided (e.g., a comfort trajectory on one side of an intended vehicle path and a precision trajectory on the other side of the intended vehicle path). Path planning commands can be provided that, in addition to a nominal desired trajectory, include asymmetric error constraints, as well as comfort and precision commands and transition commands between the comfort and precision commands. The control system can follow a desired vehicle trajectory and adapt its behavior to account for the additional planner commands.

[0014] The above summary does not represent every embodiment or every aspect of the present description. The above features and advantages of the present disclosure, as well as other possible features and advantages, will be readily apparent from the following detailed description of the embodiments and best modes for carrying out the disclosure, when considered in conjunction with the accompanying drawings and appended claims. Furthermore, this description expressly includes combinations and subcombinations of the elements and features presented above and below. BRIEF DESCRIPTION OF THE DRAWINGS Fig. 1 illustrates a vehicle with a steering system. Fig. 2 shows part of the vehicle’s steering system. Fig. 3 illustrates a method for controlling the trajectory of the vehicle. Fig. Figure 3A shows in detail some boundary conditions that are used in the Fig. The control methods shown in Figure 3 can be used. Fig. Figure 4 illustrates the control of the vehicle trajectory when the vehicle passes through a fork or junction in the roadway. Fig. 5 additionally illustrates the control of the vehicle trajectory when the vehicle passes through a fork or junction on the road. Fig. 6 shows an example of a comfort mode, a transition mode and a precision mode for controlling the trajectory of the vehicle. DETAILED DESCRIPTION

[0015] This description may be embodied in many different forms. Representative examples of the description are illustrated in the drawings and are described in detail herein as non-limiting examples of the disclosed principles. To this end, elements and limitations described in the "Summary," "Introduction," "Description," and "Detailed Description" sections, but not expressly set forth in the claims, should not be incorporated into the claims, individually or collectively, by implication, inference, or otherwise.

[0016] For the purposes of this description, the use of the singular includes the plural and vice versa, unless expressly excluded; the terms "and" and "or" apply both in the subjunctive and disjunctive moods; "each" and "all" mean "each and all," and the words "including," "including," "comprising," "with," and the like mean "including without limitation." Furthermore, words of approximation such as "approximately," "almost," "substantially," "generally," "about," etc., may be used herein to mean "at, near, or almost at," or "within 0-5% of," or "within acceptable manufacturing tolerances," or logical combinations thereof.

[0017] See first Fig. 1. A vehicle 100 is shown. The vehicle 100 may be an autonomous or semi-autonomous vehicle in which steering and route planning are performed at least partially without human intervention or with limited human intervention. Vehicle 100 may be any type of vehicle, such as a car, truck, van, SUV, or other vehicle. As shown in Fig. 2, the vehicle 100 has a steering control system 102.

[0018] Steering control system 102 may include one or more electronic control units (ECUs), such as ECU 110. ECU 110 may be a microprocessor-based controller and should be understood to have the electronic resources (microcontrollers, software, memory, inputs, outputs, circuitry, and the like) to perform the functions of ECU 110 described in this specification. The functions described in this specification may also be distributed among one or more electronic control units in vehicle 100, which may be interconnected by data buses and / or hard wiring and may therefore share data and computational responsibilities.

[0019] Because the ECU 110 and other controllers in the vehicle 100 may be microprocessor-based devices, they may operate based on instructions; such instructions may include one or more programmed software commands. Furthermore, some or all of these instructions may include additional instructions.

[0020] The inputs to ECU 110 may include one or more steering angle sensors 111. The steering angle sensor(s) 111 may provide the angles of the steered wheels of vehicle 100. Vehicle 100 may have one or more steered wheels. Vehicle 100 may have two steered wheels. Vehicle 100 may also have more than two steered wheels, e.g., four steered wheels.

[0021] The inputs of ECU 110 may also include the output of a navigation system 114. Navigation system 114 may include map data to know the configurations and locations of roads, including their boundaries, lane boundaries, and intersections. Navigation system 114 may also include a desired destination or destination for vehicle 100 and one or more desired paths for vehicle 100 to reach the destination.

[0022] Additionally, the inputs to the ECU 110 may include perception sensors such as one or more cameras 112, LIDAR (Light Detection and Ranging) remote sensing 116, radar 118, and GPS sensors 120. Such perception sensors may provide the ECU 110 with information about the environment of the vehicle 100. Such an environment may include the current position of the vehicle 100. The environment may include visual or otherwise perceptible cues of other vehicles or obstacles near the vehicle 100. The environment may also include lane markings, guardrails, roadsides, intersections, obstacles, and other features of the roads on which the vehicle 100 may travel.

[0023] An output of the ECU 110 may include one or more actuators 122 for controlling the steered wheels of the vehicle 100. As such, the ECU 100 is capable of controlling the steered wheels of the vehicle 100 to steer or control the trajectory of the vehicle 100. The vehicle 100 may have one or more steered wheels. The vehicle 100 may have two steered wheels. The vehicle 100 may have four steered wheels.

[0024] Fig. 3 additionally shows a block diagram of a system and method for controlling the trajectory of a vehicle according to this exemplary description. Block 202 is a perception block. There, the position and surroundings of the vehicle 100 may be detected, for example, by perception sensors (camera(s) 112, LIDAR remote sensing 116, radar 118, and GPS 120) and / or by the navigation system 114. The output of block 202 may be a nominal or intended path of the vehicle 100 to reach the destination of the vehicle 100. In block 204, reference trajectories for reaching the destination may be calculated. Multiple reference trajectories may be calculated, such as comfort, transition, and precision trajectories. As described in more detail below, a comfort trajectory may be used when precise control of the steering or trajectory of the vehicle 100 is not deemed necessary.This may be, for example, along a generally straight stretch of road with no nearby intersections, no nearby exit or entry ramps, and no nearby obstacles. (When a comfort trajectory is used, this description may refer to the control system of the vehicle 100 being in comfort mode.) On the other hand, a precision trajectory may be used when precise control is more important. This may be the case, for example, in roadway areas with intersections, exit or entry ramps, lane restrictions, or nearby obstacles. (When a precision trajectory is used, this description may refer to the control system of the vehicle 100 being in precision mode.A transition trajectory may be used for a situation where the roadway transitions between areas where a comfort trajectory and a precision trajectory are desirable. (When a transition trajectory is used, the control system of the vehicle 100 may be referred to in this description as being in transition mode.) In comfort mode, control may be performed with fewer control points, with relaxed limits, and / or with a narrower bandwidth than in precision mode to make the control of the steering of the vehicle 100 less "harsh."

[0025] In block 214, a model of the “system” to be controlled, namely the vehicle 100 and its steering system, is made available to the controller 206.

[0026] A selected trajectory from block 204 may be passed to a model predictive controller 206. The model predictive controller 206 may be located in the ECU 110, in another controller of the vehicle 100, or in multiple controllers that may be networked together. The model predictive controller 206 provides a control input to the actuators 122 so that the vehicle 100 follows the desired trajectory. The controller 206 tests various possible input sequences (in this case, steering inputs provided via the actuators 122) to find the one that results in the output converging with the lowest cost V over a prediction horizon with "p" prediction steps into the future of the reference trajectory. The model predictive controller 206 then commands the first element of the optimal input sequence (u k) to control the vehicle. The model predictive controller 206 then performs the calculation again in the next time interval for the digital controller. The calculation of the cost V is described in more detail below.

[0027] The model predictive controller 206 also utilizes other inputs, the generation of which will now be discussed. First, after providing the planning objective and reference trajectories in block 204, obstacle and roadway geometry, as well as directional error constraints, may be provided from block 204 to block 208. In block 208, planning nodes and Bezier curves may be applied by the ECU 100.

[0028] It will now Fig. 4. The planning nodes applied in block 208 can be either virtual attractive or virtual repulsive nodes. In Fig. 4, it is assumed that the vehicle 100 changes from a straight roadway 300 via a fork 301 to another roadway 302. The roadway 300 may have a first boundary, e.g., the left edge 300a, and a second boundary, e.g., the right edge 300b. The roadway 302 may have a first boundary, e.g., the left edge 302a, and a second boundary, e.g., the right edge 302b. One or more virtual attraction nodes may be placed along the path (e.g., along the center 300c of the roadway 300 and turning into the center 302c of the roadway 302) on which the vehicle 100 is to travel. Such virtual attraction nodes may include: virtual attraction node A1T, virtual attraction node A2T, virtual attraction node A3T, virtual attraction node A4T, virtual attraction node A5T and virtual attraction node A6T. One purpose of the virtual attraction nodes is to create virtual “fields” that are used in the calculation of the cost function V used in the model predictive control 206 of Fig. 3, effectively exert an "attractive" force on the vehicle 100. Such an attractive force contributes to the control of the vehicle 100 following its intended path. The virtual attraction nodes can be located on the intended travel path of the vehicle 100 or generally on it.

[0029] The planning nodes applied in block 208 may also be virtual rejection nodes. Such virtual rejection nodes may include: virtual rejection node R1R, virtual repulsion node R2R, virtual repulsion node R3R and virtual repulsion node R4R, each located at a certain distance from the intended path of the vehicle 100. In this case, they are located to the right of the intended path and can be arranged along the right edges of the respective lanes 300 and 302. Alternatively, the virtual repulsion nodes can also be arranged along a lane boundary, e.g., a lane marking between two lanes.

[0030] Additional virtual repulsion nodes can also be provided, such as virtual repulsion node R1L, virtual repulsion node R2L, virtual repulsion node R5L and virtual repulsion node R6L, which are each located at a certain distance from the intended path of the vehicle 100. In this case, they are located to the left of the intended path and can be arranged along the left edges of the respective lanes 300 and 302. Alternatively, the virtual repulsion nodes can also be arranged along the edge of a lane boundary on the respective lane.

[0031] The ECU 110 may also draw Bezier curves defined by the respective virtual attraction and repulsion nodes. For example, the Bezier curve 312, which generally runs along the left edge 300a of the roadway 300 and merges into the left edge 302a of the roadway 302, may be defined by the Fig. 4 given parameter equation (1) for B LFurthermore, the Bezier curve 310, which generally runs along the right edge 300b of the roadway 300 and merges into the right edge 302b of the roadway 302, can be described by the Fig. 4 given parameter equation (2) for B R It is therefore evident that the Bezier curve 310 and the Bezier curve 312 can be constructed from virtual repulsion nodes.

[0032] The Bezier curve 314 may also be drawn by the ECU 110. The Bezier curve 314 may generally run along the center 300c of the roadway 300 (which may be on or generally on the target trajectory for the vehicle 100) and merge into the center 302c of the roadway 302 (which may also be on or generally on the target trajectory for the vehicle 100 as the vehicle 100 negotiates the fork 301 between the roadway 300 and the roadway 302). The Bezier curve 314 may be described by the parametric equation (3) for B T in Fig. 4 are described.

[0033] The perception sensors on the vehicle 100 can be in block 202 ( Fig. 3) also detect that an obstacle or a potential obstacle (here the second vehicle 340) is located in front of the vehicle 100. ECU 110 can create a virtual repulsion node R3L on or near the second vehicle 340, namely in this example near the right rear corner of the second vehicle 340. The virtual repulsion node R3L can be constructed as a circle 342. The virtual repulsion node R3L may face the vehicle 100. The circle 342 may have a radius that represents the strength of the repelling node, which may reflect the importance of the potential obstacle in question. That is, if an obstacle or potential obstacle is considered more significant, the virtual repelling node R3L placed farther away (in this case, on a larger circle 342) from the obstacle to help the vehicle 100 maintain a greater distance from it. The radius of the circle may also reflect the uncertainty about the obstacle's position; if the obstacle is a moving vehicle, a larger radius may be assigned due to the uncertainty about the obstacle's current position. A smaller radius may be used if the obstacle is stationary, as the obstacle's position is more certain.

[0034] A virtual repulsion node R4L can be placed at the vertex 344 of the intersection between lanes 300 and 302. Here, too, the vertex 344 may be a particularly important point that the vehicle 100 wants to avoid. The virtual repulsion node R4L can be constructed as a circle 346 with a radius representing the strength of the repulsion node, which may be proportional to the importance of the location the vehicle 100 wishes to avoid or otherwise directly related to it. The virtual repulsion node R4L may face the vehicle 100 or be generally directed toward it.

[0035] In addition, Fig. 5. Illustrated therein are a virtual repulsion node 410a, a virtual repulsion node 410b, a virtual repulsion node 410c, a virtual repulsion node 410d, a virtual repulsion node 410e, and a virtual repulsion node 410f. (These virtual repulsion nodes may be collectively referred to as virtual repulsion nodes 410a-410f.) Also depicted are the virtual repulsion node 412a, the virtual repulsion node 412b, the virtual repulsion node 412c, and the virtual repulsion node 412d. (These virtual repulsion nodes may be collectively referred to as virtual repulsion nodes 412a-412d.) Additionally, the virtual attraction node 414a, the virtual attraction node 414b, the virtual attraction node 414c, the virtual attraction node 414d, the virtual attraction node 414e, and the virtual attraction node 414f are shown.(These virtual attraction nodes may collectively be referred to as virtual attraction nodes 414a-414f.) The virtual attractive and virtual repulsive nodes may be weighted differently, depending at least in part on the importance of their positions along the planned path of vehicle 100. For example, virtual attraction node 414c is located immediately at the intersection of roadway 300 and roadway 302, which may be a particularly important point for the guidance and control of vehicle 100. Virtual attraction node 414c is shown in the figure with a larger radius, indicating that it has a greater relative weight in calculating the virtual attraction force exerted on vehicle 100. Virtual attraction node 410b may have a greater weight because it is located at the beginning of the intersection between roadway 300 and roadway 302.The virtual repulsion node 410d may also have a greater weight because it is located at the apex 344; by maintaining an appropriate distance from the apex 344, crossing the roadway may be avoided when the vehicle 100 travels from the roadway 300 to the roadway 302.

[0036] The virtual repulsion nodes 410a-410f and the Bezier curve 411 formed by them can generate virtual repulsion fields, as shown by the arrows emanating from the virtual repulsion nodes 410a-410f and the Bezier curve 411 formed by them. Virtual repulsion nodes 412a-412d and the Bezier curve 413 formed by them can also generate virtual repulsion fields, as illustrated by the arrows emanating from the virtual repulsion nodes 412a-412d and the Bezier curve 413 formed by them. On the other hand, virtual attraction nodes 414a-414f and the Bezier curve 415 formed by them can generate virtual attraction fields, as shown by the arrows pointing to the virtual attraction nodes 414a-414f and the Bezier curve 415 formed by them.

[0037] The issue of Block 208 ( Fig. 3) may be a net "field" (the "net potential field") representing the attractive or repulsive "potential" of the attractive and repulsive nodes and the attractive and repulsive Bezier curves. The potential is a measure of the virtual force generated by the attractive and repulsive nodes on the vehicle 100. The weighted "cost" related to this potential (the "potential field cost") is calculated in block 210 for use in the model predictive controller 206.

[0038] The model predictive controller 206 may be a discrete digital controller operating with a control interval or period "k." The controller has a curve 250 representing the reference trajectory for the vehicle 100. The model predictive controller 206 calculates a cost function V as the summation of a number of cost terms, such as: V=12∑k=1p{(y−yref)2Wy+(u−uref)2Wu+(uk−uk−1)2WΔu+ε2Wε+γ2Wγ}, where y is / are variables based on the movement of the vehicle 100 (i.e. the “outputs”); y ref Reference states is / are related to the target path of vehicle 100; u is / are the inputs to the vehicle trajectory control system; u ref is / are the pre-calculated input setpoint(s) for the model predictive control, i.e. the input that would result in outputs following the setpoints based on feed-forward calculations; ε is a step function indicating whether “soft” constraints would be violated by a given control action; γ is the virtual net strength of the virtual attraction nodes, the virtual repulsion nodes and the Bezier curves; W y is the weight assigned to the outputs; W u is the weight assigned to the inputs; W Δuis the weight assigned to the rate at which the inputs change; W ε is the weight assigned to the violation of “soft” constraints; Wγ is the weight assigned to the potential field of the virtual attraction nodes, the virtual repulsion nodes and the Bezier curves; and p is the forecast horizon.

[0039] In the equation for the cost function V, y and y ref 4 × 1 matrices, where the variables in the matrices are the lateral position, lateral velocity, turning position, and turning rate of the vehicle 100. u can be a scalar, namely the steering angle input command supplied to the actuator(s) 122.

[0040] The term (u k - u) k-1 2reflects the rate at which the steering angle changes. This term is included in the cost function V to emphasize that less "twitching" in the steering of the vehicle 100 is desirable.

[0041] "Soft" constraints can be constraints whose violation can be detrimental, such as approaching a predetermined distance from a road edge or lane boundary without actually crossing the road edge or boundary. "Soft" conditions are conditions that should preferably, but not necessarily, not be violated and are therefore included in the calculation of the cost function V. If no control solution can be found that does not violate a "hard" constraint, the system can revert the steering from autonomous to manual. Another example of the relationship between a soft and a hard constraint can be that a hard constraint states that the vehicle 100 cannot approach a certain obstacle within a predetermined distance (e.g.five feet); a related soft constraint may be that the vehicle 100 may not approach the obstacle to a greater predetermined distance (e.g., ten feet).

[0042] The various W terms in the above equation are weighting factors, which can be constants. They can be assigned based on the system's calibration to achieve the desired system performance.

[0043] In the model predictive controller 206, curve 250 shows the reference trajectory of vehicle 100. Curve 252 is the actual output, which reflects the trajectory of vehicle 100 up to time "k," and curve 254 is the predicted output, which reflects the predicted trajectory of vehicle 100. (Arrow 251 and arrow 253 denote the time before and after time "k," respectively.) Curve 256 is the past control input, and curve 258 is the predicted control input that causes vehicle 100 to converge to the reference trajectory. After calculating the minimum cost V over the prediction horizon and the inputs (curve 258) that, according to the prediction, would result in the minimum cost, the model predictive controller 206 applies the first input u k+1accordingly. The model predictive controller 206 then recalculates the minimum cost V over an incremental prediction horizon and reapplies the first input from that calculation, and the process continues.

[0044] The model predictive control operates with constraints. The boundary conditions 212 within which the model predictive control 206 can operate are defined in Fig. 3A is shown in detail. There, stands n u for the length of the tax horizon; stands n p for the length of the forecast horizon; place and min and u max the minimum or maximum input restriction; stand y min and y max for the minimum and maximum performance requirements; ε stands for the step function mentioned above with respect to soft constraints; and place A min and A max programmable parameters.

[0045] The calculation of the total field strength based on virtually attractive Bezier curves (i.e., Bezier curves constructed on the basis of virtually attractive nodes) and virtually repulsive Bezier curves (i.e., Bezier curves constructed on the basis of virtually repulsive nodes) can be done as follows. A virtual attractive force γ A can be calculated as y A = y BT - y where y BTis the position of the virtual attractive Bezier curve and y is the position of the vehicle 100 in the xy coordinate set (where y is a lateral direction relative to the vehicle 100 and x is a longitudinal direction defined by the vehicle 100). That is, the virtual attractive force is greater when the vehicle 100 is farther from the virtual attractive Bezier curve. (The force can be scaled based on the strength of the respective attraction nodes that form the basis of the Bezier curve.) A virtual repulsive force γ R can be against: γR={1yBL−y,y<01yBR−y,y≥0

[0046] That is, the virtual repulsion force may be greater when the vehicle 100 is closer to a virtual repulsive Bezier curve. The force may be scaled based on the strength of the respective repulsion nodes that form the base of the Bezier curve.

[0047] Thus, the total cost based on the potential fields for any longitudinal position of the vehicle 100 may be as follows: γ=γAγ={yBT−yyBL−y,y<0yBT−yyBR−y,y≥0

[0048] The costs y attributable to the virtual attraction and repulsion nodes can be calculated similarly according to the lateral distance y of the vehicle 100 from the virtual attraction and repulsion nodes. (The virtual attraction and repulsion forces can be scaled based on the strength of the respective virtual attraction and repulsion nodes.) These costs can be summed in a similar manner. All costs y can then be summed to determine the total costs attributable to the virtual attraction and repulsion nodes and the virtual attraction and repulsion Bezier curves used in the calculation of V by the model predictive controller 206.

[0049] It will now Fig.6. There, the use of a comfort mode, a transition mode, and a precision mode is illustrated. In this example, the vehicle 100 may be on a roadway 500 and intend to remain on the roadway 500 rather than turning onto a roadway 502 via a fork 501. While the vehicle 100 is traveling on a section 504 of the roadway 500 prior to the fork 501, the vehicle 100 may be controlled in the comfort mode. The location of the virtual repulsion nodes and the resulting Bezier curves 506 and 508 may be relatively far apart, since the precision control and the associated comparatively hard gait may not be as advantageous on a section of road without intersections and obstacles. However, as the vehicle 100 approaches the fork 501, it may become more important for the vehicle 100 not to deviate onto the roadway 502.Therefore, for road segment 509, a transition mode may be activated in which the Bezier curve 510 on the right (i.e., toward the fork) is more closely aligned with the intended path of vehicle 100 than the Bezier curve 512 on the left. Therefore, the controller (i.e., the path planner) may prescribe asymmetric error constraints or asymmetric error limits. In this case, the controller prescribes a comfort trajectory on the left and a precision trajectory on the right. Furthermore, in precision mode, the Bezier curve 516 and the Bezier curve 518 may be relatively closely spaced (i.e., closer to each other) in road segment 514. Eventually, the behavior may revert to the default comfort mode.At this point, the Bezier curve 520 and the Bezier curve 522 may again be relatively further apart and transition to the comfort mode because the vehicle 100 is no longer on a section of road where particularly precise control could be advantageous.

[0050] Additionally or alternatively, a precision mode can be represented by increasing the number of control points to which the trajectory of the vehicle 100 can be controlled. In the case of using model predictive control, this can be achieved by increasing the bandwidth of the relevant digital controller. Greater precision in maneuvers or locations that would benefit from the additional precision may come at the expense of greater harshness resulting from the more precise control. A comfort mode can be represented by using fewer control points, and a transition mode can be represented by a number of control points between precision mode and comfort mode.

[0051] For example, a precision mode or trajectory may be used in areas where an upcoming vehicle steering maneuver, an upcoming road intersection, an upcoming road ramp, nearby obstacles (such as other vehicles), or a construction zone may benefit from precise control of the trajectory of the vehicle 100.

[0052] Embodiments of the present specification are described herein. It should be understood, however, that the disclosed embodiments are merely examples, and other embodiments may take various and alternative forms. The figures are not necessarily to scale; some features may be exaggerated or reduced to show details of particular components. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a representative basis for showing one skilled in the art how to variously employ the present specification.

[0053] Furthermore, the embodiments illustrated in the drawings or the features of various embodiments mentioned in this description are not necessarily to be understood as independent embodiments. Rather, it is possible that each of the features described in one of the embodiments can be combined with one or more other desired features of other embodiments, resulting in other embodiments that are not described in words or by reference to the drawings. Accordingly, such other embodiments fall within the scope of the appended claims. Furthermore, this description expressly includes combinations and sub-combinations of the elements and features presented above and below.

Claims

[1] A method for controlling a vehicle, comprising: by one or more controls: Determining an intended path of the vehicle; Determining locations of virtual attraction nodes on the intended route; Determining locations of virtual repulsion nodes along one or more boundaries spaced from the intended travel path; Calculating virtual attraction forces between one or more positions of the vehicle and the virtual attraction nodes; Calculating virtual repulsion forces between one or more positions of the vehicle and the virtual repulsion nodes; and Controlling a trajectory of the vehicle at least partially using the virtual attractive forces and the virtual repulsive forces. [2] The method of claim 1, further comprising assigning different weights to different virtual attraction nodes, wherein the different weights represent different virtual attraction forces generated by the different virtual attraction nodes. [3] The method of claim 2, further comprising assigning different weights to different virtual repulsion nodes, wherein the different weights represent different virtual repulsion forces generated by the different virtual repulsion nodes. [4] The method of claim 3, wherein at least some of the virtual repulsion nodes are constructed as circles, the radii of the circles representing the weights of at least some of the virtual repulsion nodes. [5] The method of claim 4, wherein at least some of the virtual repulsion nodes constructed as circles are located at obstacles that the vehicle is to avoid. [6] The method of claim 1, further comprising: Using the virtual attractive forces and the virtual repulsive forces to determine costs; and wherein controlling a trajectory of the vehicle further comprises using the cost in a model predictive controller that controls the trajectory of the vehicle. [7] The method of claim 1, further comprising: Controlling the trajectory of the vehicle using a comfort mode and a precision mode, wherein both the comfort mode and the precision mode use control points, with a greater number of control points being used in the precision mode than in the comfort mode. [8] The method of claim 7, further comprising using the precision mode in areas of an upcoming steering maneuver of the vehicle, an upcoming road intersection, an upcoming road ramp, a construction zone, or an area of ​​relatively high traffic volume.

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