Enhancing High-Speed Cruising Performance of Autonomous Vehicles through Integrated Deep Reinforcement Learning Framework

TL;DR

This paper presents an integrated deep reinforcement learning framework that improves the safety and efficiency of autonomous vehicles during high-speed cruising in mixed traffic by combining decision-making, path planning, and motion control.

Contribution

It introduces a novel integrated framework utilizing deep RL and IRL to enhance AV decision-making, interpretability, and high-speed performance in complex traffic scenarios.

Findings

The framework enables safe high-speed cruising in simulations.

IRL helps AVs mimic human-like lane-changing behavior.

Deep RL improves decision-making under complex traffic conditions.

Abstract

High-speed cruising scenarios with mixed traffic greatly challenge the road safety of autonomous vehicles (AVs). Unlike existing works that only look at fundamental modules in isolation, this work enhances AV safety in mixed-traffic high-speed cruising scenarios by proposing an integrated framework that synthesizes three fundamental modules, i.e., behavioral decision-making, path-planning, and motion-control modules. Considering that the integrated framework would increase the system complexity, a bootstrapped deep Q-Network (DQN) is employed to enhance the deep exploration of the reinforcement learning method and achieve adaptive decision making of AVs. Moreover, to make AV behavior understandable by surrounding HDVs to prevent unexpected operations caused by misinterpretations, we derive an inverse reinforcement learning (IRL) approach to learn the reward function of skilled drivers for the path planning of lane-changing maneuvers. Such a design enables AVs to achieve a human-like tradeoff between multi-performance requirements. Simulations demonstrate that the proposed integrated framework can guide AVs to take safe actions while guaranteeing high-speed cruising performance.