Trajectory planning system for ensuring maneuvers to avoid moving obstacles exist for an autonomous vehicle
A trajectory planning system for an autonomous vehicle includes one or more controllers in electronic communication with one or more external vehicle networks that collect data with respect to one or more moving obstacles located in an environment surrounding the autonomous vehicle. The one or more controllers determine, based on the discrete-time relative vehicle state, a position avoidance set representing relative lateral positions and longitudinal positions that the autonomous vehicle avoids while bypassing the one or more moving obstacles when performing a maneuver. The one or more controllers select a trajectory from the plurality of relative state trajectories for the autonomous vehicle, where the autonomous vehicle follows the trajectory while performing the maneuver.
1. A trajectory planning system for an autonomous vehicle, the trajectory planning system comprising:
one or more controllers in electronic communication with one or more external vehicle networks that collect data with respect to one or more moving obstacles located in an environment surrounding the autonomous vehicle, the one or more controllers executing instructions to:
determine a discrete-time relative vehicle state based on an autonomous vehicle dynamics model;
determine, based on the discrete-time relative vehicle state, a position avoidance set representing relative lateral positions and longitudinal positions that the autonomous vehicle avoids while bypassing the one or more moving obstacles when performing a maneuver;
determine a set of ego states for which the autonomous vehicle is unable to execute maneuvers without entering the position avoidance set for a set of given speed limits and road conditions;
compute a plurality of relative state trajectories for the autonomous vehicle, wherein the plurality of relative state trajectories avoid intersecting an evasion set for the set of ego states, wherein the one or more controllers determine the plurality of relative state trajectories for the autonomous vehicle based on an initial state of the autonomous vehicle, a final state of the autonomous vehicle, and one or more levels of driving aggression;
select a trajectory from the plurality of relative state trajectories for the autonomous vehicle; and
follow, by the autonomous vehicle, the trajectory while performing the maneuver.
2. The trajectory planning system of claim 1 , wherein the one or more controllers select the trajectory by:
assigning a score to each relative state trajectory for the autonomous vehicle based on one or more properties; and
selecting the relative state trajectory having the highest score as the trajectory.
3. The trajectory planning system of claim 2 , wherein the one or more properties include one or more of the following: ride comfort, fuel consumption, timing, and duration.
4. The trajectory planning system of claim 1 , further comprising a plurality of sensors in electronic communication with the one or more controllers, wherein the one or more controllers receive a plurality of dynamic variables as input from the plurality of sensors.
5. The trajectory planning system of claim 4 , wherein the one or more controllers determine the autonomous vehicle dynamics model for the autonomous vehicle based on the plurality of dynamic variables, vehicle chassis configuration information, and a control input vector corresponding to the autonomous vehicle.
6. The trajectory planning system of claim 1 , wherein the position avoidance set is determined by:
={( e s ,e d )∈ R 2 : e s,l ≤e s ≤e s,u ,e d,l ≤e d ≤e d,u }
wherein is the position avoidance set, e s is a discrete-time relative longitudinal state of the autonomous vehicle, e d is a discrete-time relative lateral state of the autonomous vehicle, e s,l is a lower limit of the discrete-time relative longitudinal state e s , e s,u is an upper limit of the discrete-time relative longitudinal state e s , e d,l is a lower limit of the discrete-time relative lateral state e d , and e d,u is a lower limit of the discrete-time relative longitudinal state e s , of the position avoidance set .
7. The trajectory planning system of claim 1 , wherein the evasion set for the set of ego states is saved as one or more look-up tables and are indexed based on a current speed limit of a roadway that autonomous vehicle is presently traveling along and one or more road conditions.
8. The trajectory planning system of claim 1 , wherein the one or more levels of driving aggression include a conservative level, a moderate level, and an aggressive level of aggression.
9. The trajectory planning system of claim 1 , wherein the one or more controllers determine the set of ego states during an offline process based on one of simulated data and experimental data.
10. The trajectory planning system of claim 1 , wherein the one or more moving obstacles include another vehicle located along a roadway that the autonomous vehicle is driving along.
11. The trajectory planning system of claim 1 , wherein the set of ego states represent vehicle states where the autonomous vehicle is unable to execute maneuvers during a time horizon to avoid the one or more moving obstacles.
12. The trajectory planning system of claim 1 , wherein the autonomous vehicle dynamics model includes one or more of the following: a linear tire model and non-linear tire models.
13. The trajectory planning system of claim 1 , wherein the plurality of relative state trajectories are calculated based on a multi-objective optimization between cumulative jerk and the one or more levels of driving aggression.
14. An autonomous vehicle including a trajectory planning system, the autonomous vehicle comprising:
a plurality of sensors that determine a plurality of dynamic variables;
one or more external vehicle networks that collect data with respect to one or more moving obstacles located in an environment surrounding the autonomous vehicle; and
one or more controllers in electronic communication with the one or more external vehicle networks and the plurality of sensors, the one or more controllers executing instructions to:
determine an autonomous vehicle dynamics model for the autonomous vehicle based on the plurality of dynamic variables and vehicle chassis configuration information;
determine a discrete-time relative vehicle state based on the autonomous vehicle dynamics model;
determine, based on the discrete-time relative vehicle state, a position avoidance set representing relative lateral positions and longitudinal positions that the autonomous vehicle avoids while bypassing the one or more moving obstacles when performing a maneuver;
determine a set of ego states for which the autonomous vehicle is unable to execute maneuvers without entering the position avoidance set for a set of given speed limits and road conditions;
compute a plurality of relative state trajectories for the autonomous vehicle, wherein the plurality of relative state trajectories avoid intersecting an evasion set for the set of ego states, wherein the one or more controllers determine the plurality of relative state trajectories for the autonomous vehicle based on an initial state of the autonomous vehicle, a final state of the autonomous vehicle, and one or more levels of driving aggression;
select a trajectory from the plurality of relative state trajectories for the autonomous vehicle; and
follow, by the autonomous vehicle, the trajectory while performing the maneuver.
15. The autonomous vehicle of claim 14 , wherein the one or more controllers select the trajectory by:
assigning a score to each relative state trajectory for the autonomous vehicle based on one or more properties; and
selecting the relative state trajectory having the highest score as the trajectory.
16. The autonomous vehicle of claim 14 , wherein the evasion set for the set of ego states is saved as one or more look-up tables and are indexed based on a current speed limit of a roadway that autonomous vehicle is presently traveling along and one or more road conditions.
17. The autonomous vehicle of claim 14 , wherein the autonomous vehicle dynamics model includes one or more of the following: a linear tire model and non-linear tire models.
18. The autonomous vehicle of claim 14 , wherein the plurality of relative state trajectories are calculated based on a multi-objective optimization between cumulative jerk and the one or more levels of driving aggression.
19. A method for selecting a trajectory for an autonomous vehicle, the method comprising:
determining, by one or more controllers, a discrete-time relative vehicle state based on an autonomous vehicle dynamics model, wherein the one or more controllers are in electronic communication with one or more external vehicle networks that collect data with respect to one or more moving obstacles located in an environment surrounding the autonomous vehicle;
determining, based on the discrete-time relative vehicle state, a position avoidance set representing relative lateral positions and longitudinal positions that the autonomous vehicle avoids while bypassing the one or more moving obstacles when performing a maneuver;
determining a set of ego states for which the autonomous vehicle is unable to execute maneuvers without entering the position avoidance set for a set of given speed limits and road conditions;
computing a plurality of relative state trajectories for the autonomous vehicle, wherein the plurality of relative state trajectories avoid intersecting an evasion set for the set of ego states, wherein the one or more controllers determine the plurality of relative state trajectories for the autonomous vehicle based on an initial state of the autonomous vehicle, a final state of the autonomous vehicle, and one or more levels of driving aggression;
selecting a trajectory from the plurality of relative state trajectories for the autonomous vehicle; and
following, by the autonomous vehicle, the trajectory while performing the maneuver.
20. The method of claim 19 , wherein the trajectory is selected by:
assigning a score to each relative state trajectory for the autonomous vehicle based on one or more properties; and
selecting the relative state trajectory having the highest score as the trajectory.