An End-to-end Deep Reinforcement Learning Approach for the Long-term Short-term Planning on the Frenet Space

TL;DR

This paper introduces an end-to-end deep reinforcement learning method for autonomous highway driving that operates in the Frenet space, improving decision-making and motion planning by leveraging continuous trajectories and deep learning.

Contribution

It is the first to define states and actions in the Frenet space for reinforcement learning in autonomous driving, enhancing robustness to road curvature and traffic interactions.

Findings

Outperforms baseline methods in high-fidelity CARLA simulations.

Demonstrates robustness across 1000 diverse traffic scenarios.

Provides continuous spatiotemporal trajectories for effective control.

Abstract

Tactical decision making and strategic motion planning for autonomous highway driving are challenging due to the complication of predicting other road users' behaviors, diversity of environments, and complexity of the traffic interactions. This paper presents a novel end-to-end continuous deep reinforcement learning approach towards autonomous cars' decision-making and motion planning. For the first time, we define both states and action spaces on the Frenet space to make the driving behavior less variant to the road curvatures than the surrounding actors' dynamics and traffic interactions. The agent receives time-series data of past trajectories of the surrounding vehicles and applies convolutional neural networks along the time channels to extract features in the backbone. The algorithm generates continuous spatiotemporal trajectories on the Frenet frame for the feedback controller to track. Extensive high-fidelity highway simulations on CARLA show the superiority of the presented approach compared with commonly used baselines and discrete reinforcement learning on various traffic scenarios. Furthermore, the proposed method's advantage is confirmed with a more comprehensive performance evaluation against 1000 randomly generated test scenarios.