Authors:
Jason Chemin
;
Eric Lucet
and
Aurélien Mayoue
Affiliation:
Institut LIST, CEA, Université Paris-Saclay, F-91120, Palaiseau, France
Keyword(s):
Path Tracking, Reinforcement Learning, Speed Control.
Abstract:
Path tracking is a critical component of autonomous driving, requiring both safety and efficiency through improved tracking accuracy and appropriate speed control. Traditional model-based controllers like Pure Pursuit (PP) and Model Predictive Control (MPC) may struggle with dynamic uncertainties and high-speed instability if not modeled accurately. While advanced MPC or Reinforcement Learning (RL) can enhance path tracking accuracy via steering control, speed control is another crucial aspect to consider. We explore various RL speed control approaches, including end-to-end acceleration, acceleration correction, and target speed correction, comparing their performance against simplistic model-based methods. Additionally, the impact of sequential versus simultaneous control architectures on their performance is analyzed. Our experiments reveal that RL methods can significantly improve path tracking accuracy by balancing speed and lateral error, particularly for poorly to moderately pe
rforming steering controllers. However, when used with already well-performing steering controllers, they performed similarly or slightly worse than simple model-based ones, raising questions about the utility of RL in such scenarios. Simultaneous RL control of speed and steering is complex to learn compared to sequential approaches, suggesting limited utility in simple path tracking tasks.
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