Brain-in-the-Loop Learning for Intelligent Vehicle Decision-Making

The inflexible human-autonomy relationship within autonomous driving scenarios still has not realized deep intelligent synergy, therefore unable to provide adaptive and context-sensitive decision-making and sometimes leading to violation of human preferences or even hazards. In this paper, we utilize functional near-infrared spectroscopy (fNIRS) signals as real-time human risk-perception feedback to establish a brain-in-the-loop (BiTL) trained artificial intelligence algorithm for decision-making. The proposed algorithm uses the result of driving risk reasoning as one input of reinforcement learning combining fNIRS-based risk and driving safety field model-based risk, realizing integrating human brain activity into the reinforcement learning scheme, then overcoming the disadvantage of machine-oriented intelligence that could violate human intentions. To achieve policy learning within limited BiTL training periods, we add two modification features to the proposed algorithm based on TD3. The experiment involving twenty participants has been conducted, and the results show that in continuously high-risk driving scenarios, compared to traditional reinforcement learning algorithms without human participation, the proposed algorithm can maintain a cautious driving policy and avoid potential collisions, validated with both proximal surrogate indicators and success rates.

This repository contains the experimental dataset and Python code to reproduce the experimental results used in our research on 'Brain-in-the-Loop Learning for Intelligent Vehicle Decision-Making'. Both human subject studies, control groups and ablation studies data are included in this repository. Detailed description of file organization, data structures, requirements could be found in the README.md document.