Brain-Inspired Modelling and Decision-making for Human-Like Autonomous Driving in Mixed Traffic Environment

TL;DR

This paper presents a brain-inspired, human-like decision-making framework for autonomous vehicles to better integrate into human traffic, using models of emotional learning, aggressiveness estimation, and dynamic game theory, validated through simulator experiments.

Contribution

It introduces a novel human-like driving model combining BELCM and preview models, along with a decision-making algorithm considering safety and efficiency, validated in simulation.

Findings

The framework effectively mimics human driving behavior.

The decision-making algorithm improves lane-change safety.

Simulation results confirm the approach's feasibility.

Abstract

In this paper, a human-like driving framework is designed for autonomous vehicles (AVs), which aims to make AVs better integrate into the transportation ecology of human driving and eliminate the misunderstanding and incompatibility of human drivers to autonomous driving. Based on the analysis of the real world INTERACTION dataset, a driving aggressiveness estimation model is established with the fuzzy inference approach. Then, a human-like driving model, which integrates the brain emotional learning circuit model (BELCM) with the two-point preview model, is designed. In the human-like lane-change decision-making algorithm, the cost function is designed comprehensively considering driving safety and travel efficiency. Based on the cost function and multi-constraint, the dynamic game algorithm is applied to modelling the interaction and decision making between AV and human driver. Additionally, to guarantee the lane-change safety of AVs, an artificial potential field model is built for collision risk assessment. Finally, the proposed algorithm is evaluated through human-in-the-loop experiments on a driving simulator, and the results demonstrated the feasibility and effectiveness of the proposed method.