Integrating Neurophysiological Sensors and Driver Models for Safe and Performant Automated Vehicle Control in Mixed Traffic

TL;DR

This paper explores combining neurophysiological sensors and driver models to enhance the safety and performance of highly automated vehicles interacting with human drivers in mixed traffic environments.

Contribution

It introduces an integrated approach using neurophysiological data and driver models to improve HAV decision-making in complex traffic scenarios.

Findings

Neurophysiological sensors provide valuable data on human driver states.

Driver models can predict human behavior to inform HAV actions.

Integration improves safety and efficiency in mixed traffic.

Abstract

In future mixed traffic Highly Automated Vehicles (HAV) will have to resolve interactions with human operated traffic. A particular problem for HAVs is detection of human states influencing safety critical decisions and driving behavior of humans. We demonstrate the value proposition of neurophysiological sensors and driver models for optimizing performance of HAVs under safety constraints in mixed traffic applications.