System and method for real world autonomous vehicle trajectory simulation
A system and method for real world autonomous vehicle trajectory simulation may include: receiving training data from a data collection system; obtaining ground truth data corresponding to the training data; performing a training phase to train a plurality of trajectory prediction models; and performing a simulation or operational phase to generate a vicinal scenario for each simulated vehicle in an iteration of a simulation. Vicinal scenarios may correspond to different locations, traffic patterns, or environmental conditions being simulated. Vehicle intention data corresponding to a data representation of various types of simulated vehicle or driver intentions.
1. A system comprising:
a data processor;
a memory device for storage of a plurality of trained trajectory prediction models, the plurality of trained trajectory prediction models having been trained using real world training data and collected driver behavior data;
a trajectory generation module, executable by the data processor, the trajectory generation module being configured to:
generate, by the data processor, a trajectory for an autonomous vehicle, the trajectory corresponding to perception or sensor data and autonomous vehicle intention data;
execute, by the data processor, at least one of the plurality of trained trajectory prediction models to generate a predicted vehicle trajectory for each of a plurality of vehicles proximate to the autonomous vehicle based on the perception or sensor data and the autonomous vehicle intention data; and
modify, by the data processor, the trajectory for the autonomous vehicle to avoid the predicted vehicle trajectory for each proximate vehicle; and
an autonomous vehicle control system for the autonomous vehicle configured to cause the autonomous vehicle to traverse the modified trajectory for the autonomous vehicle.
2. The system of claim 1 further including a data collection system configured to receive the perception or sensor data collected from a plurality of sensors, the perception or sensor data being stored in the memory device.
3. The system of claim 1 wherein the perception or sensor data includes traffic image data, vehicle image data, roadway data, environmental data, distance data from LIDAR devices, and distance data from radar devices.
4. The system of claim 1 further including a trajectory prediction model training system configured to train the plurality of trajectory prediction models with training data to produce the plurality of trained trajectory prediction models, the training data including the real world training data and collected driver behavior data.
5. The system of claim 1 wherein the plurality of trained trajectory prediction models comprise at least one trained trajectory prediction model configured to model a variable level of driver aggressiveness.
6. The system of claim 1 wherein the autonomous vehicle control system is configured to control a direction and speed of the autonomous vehicle to direct the autonomous vehicle to follow the modified trajectory for the autonomous vehicle.
7. A method comprising:
storing, by a memory device, a plurality of trained trajectory prediction models, the plurality of trained trajectory prediction models having been trained using real world training data and collected driver behavior data;
generating, by a data processor, a trajectory for an autonomous vehicle, the trajectory corresponding to perception or sensor data and autonomous vehicle intention data;
executing, by the data processor, at least one of the plurality of trained trajectory prediction models to generate a predicted vehicle trajectory for each of a plurality of vehicles proximate to the autonomous vehicle based on the perception or sensor data and the autonomous vehicle intention data;
modifying, by the data processor, the trajectory for the autonomous vehicle to avoid the predicted vehicle trajectory for each proximate vehicle; and
using an autonomous vehicle control system for the autonomous vehicle to cause the autonomous vehicle to traverse the modified trajectory for the autonomous vehicle.
8. The method of claim 7 further including using a data collection system to receive the perception or sensor data collected from a plurality of sensors and to store the perception or sensor data in the memory device.
9. The method of claim 7 , wherein the perception or sensor data includes traffic image data, vehicle image data, roadway data, environmental data, distance data from LIDAR devices, and distance data from radar devices.
10. The method of claim 7 wherein the plurality of trained trajectory prediction models comprise at least one trained trajectory prediction model configured to model a variable level of driver conservatism.
11. The method of claim 7 wherein the predicted vehicle trajectory for each of a plurality of vehicles proximate to the autonomous vehicle includes data indicative of a degree of likelihood or probability that a particular proximate vehicle will traverse the predicted vehicle trajectory.
12. The method of claim 7 , wherein the plurality of trained trajectory prediction models includes modeling of a transport time delay between a stimulus and a driver's control response.
13. The method of claim 7 wherein the plurality of trained trajectory prediction models includes a speed control model and a cornering aggressiveness model.
14. The method of claim 7 including controlling a direction and speed of the autonomous vehicle to direct the autonomous vehicle to follow the modified trajectory for the autonomous vehicle.
15. A non-transitory machine-readable storage medium embodying instructions which, when executed by a data processor, cause the data processor to:
store in a memory device a plurality of trained trajectory prediction models, the plurality of trained trajectory prediction models having been trained using real world training data and collected driver behavior data;
generate a trajectory for an autonomous vehicle, the trajectory corresponding to perception or sensor data and autonomous vehicle intention data;
execute at least one of the plurality of trained trajectory prediction models to generate a predicted vehicle trajectory for each of a plurality of vehicles proximate to the autonomous vehicle based on the perception or sensor data and the autonomous vehicle intention data; modify the trajectory for the autonomous vehicle to avoid the predicted vehicle trajectory for each proximate vehicle; and
use an autonomous vehicle control system for the autonomous vehicle to cause the autonomous vehicle to traverse the modified trajectory for the autonomous vehicle.
16. The non-transitory machine-readable storage medium of claim 15 wherein the instructions include machine learnable components.
17. The non-transitory machine-readable storage medium of claim 15 wherein the instructions being further configured to train the plurality of trajectory prediction models with training data to produce the plurality of trained trajectory prediction models.
18. The non-transitory machine-readable storage medium of claim 15 wherein the plurality of trained trajectory prediction models comprise at least one trained trajectory prediction model configured to model a variable level of driver aggressiveness.
19. The non-transitory machine-readable storage medium of claim 15 being further configured to use a data collection system to receive the perception or sensor data collected from a plurality of sensors and to store the perception or sensor data in the memory device.
20. The non-transitory machine-readable storage medium of claim 15 wherein the instructions being further configured to control a direction and speed of the autonomous vehicle to direct the autonomous vehicle to follow the modified trajectory for the autonomous vehicle.