Autonomous Vehicle Motion Planning With Iterative Trajectory Feedback
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Solution Overview
Problem
Traditional motion planning methods in autonomous driving systems face challenges such as limited accuracy in tracking complex trajectories, lack of feedback to correct tracking errors, and inability to predict deviations, leading to potential collisions.
Innovation Solution
Establish a feedback connection between motion planning and motion control by iteratively updating the motion trajectory using a motion control simulator and a motion control operator, which simulates and adjusts the trajectory offset to improve tracking performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional trajectory generation methods are used to reduce computation cost, then computational efficiency is improved, but trajectory tracking accuracy deteriorates
Solution Approach 1:
The system performs preliminary simulation of trajectory tracking using a simplified vehicle model before actual execution. This preliminary action identifies potential tracking errors in advance, allowing the motion planner to pre-compensate by adjusting the trajectory waypoints. The simulation results are used to calculate offset signals that are added to the original trajectory, ensuring accurate tracking without requiring complex real-time computations.
Solution Approach 2:
The system implements a feedback mechanism where simulation results of trajectory tracking are fed back to the motion planner. The simulation compares the generated trajectory with the actual vehicle response, identifies tracking errors, and returns offset signals to correct the trajectory. This closed-loop feedback ensures high tracking accuracy while maintaining computational efficiency through iterative refinement.
2Device complexity
If traditional motion planning without feedback is used, then system complexity is reduced, but ability to predict and correct tracking deviations deteriorates
Solution Approach 1:
The system introduces a motion control simulator as an intermediary component between the motion planner and motion controller. This simulator acts as a virtual testbed that predicts tracking behavior without requiring complex real-time models. It generates offset signals that compensate for anticipated tracking errors, enabling reliable deviation prediction while keeping the overall system architecture relatively simple and modular.
3Measurement precision
If high-resolution motion trajectories are generated, then trajectory accuracy is improved, but computation cost increases
Solution Approach 1:
The system performs preliminary simulation using a simplified vehicle model to evaluate trajectory tracking performance before final execution. This preliminary action identifies which trajectory segments require high resolution and which can use lower resolution, allowing selective refinement of waypoints. The simulation results guide the generation of offset signals that maintain accuracy without requiring uniformly high-resolution trajectories throughout, thus reducing overall computation cost.
Data Source
AI summary
Establishing a feedback connection between a motion planner and a motion controller in an autonomous driving system wherein feedback is iteratively updated and able to improve motion controller performance.