Safe Decision-making for Lane-change of Autonomous Vehicles via Human Demonstration-aided Reinforcement Learning

TL;DR

This paper introduces a reinforcement learning approach enhanced with human demonstrations to improve safety in autonomous vehicle lane-change decisions, demonstrating superior performance in simulation.

Contribution

It proposes a novel method integrating human demonstrations into RL to enhance safety and effectiveness in autonomous lane-change decision-making.

Findings

Human demonstrations improve RL safety in lane change tasks.

The method outperforms existing learning-based decision strategies.

Simulation results validate the approach's effectiveness.

Abstract

Decision-making is critical for lane change in autonomous driving. Reinforcement learning (RL) algorithms aim to identify the values of behaviors in various situations and thus they become a promising pathway to address the decision-making problem. However, poor runtime safety hinders RL-based decision-making strategies from complex driving tasks in practice. To address this problem, human demonstrations are incorporated into the RL-based decision-making strategy in this paper. Decisions made by human subjects in a driving simulator are treated as safe demonstrations, which are stored into the replay buffer and then utilized to enhance the training process of RL. A complex lane change task in an off-ramp scenario is established to examine the performance of the developed strategy. Simulation results suggest that human demonstrations can effectively improve the safety of decisions of RL. And the proposed strategy surpasses other existing learning-based decision-making strategies with respect to multiple driving performances.