Integrating Neurophysiological Sensors and Driver Models for Safe and Performant Automated Vehicle Control in Mixed Traffic
This addresses safety and performance challenges for automated vehicles in mixed traffic, but appears incremental as it builds on existing sensor and modeling approaches.
The paper tackles the problem of Highly Automated Vehicles (HAVs) interacting with human drivers in mixed traffic by using neurophysiological sensors and driver models to detect human states affecting safety-critical decisions, aiming to optimize HAV performance under safety constraints.
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.