Neurocognitive and traffic based handover strategies

TL;DR

This paper proposes a novel approach combining neurocognitive and traffic data to determine optimal takeover points in automated vehicles, enhancing safety and decision-making accuracy.

Contribution

It introduces an integrated method that fuses neurocognitive and traffic information to improve takeover timing in automated driving systems.

Findings

Demonstrates improved accuracy in identifying takeover points

Shows potential for reducing driver reaction time

Provides a framework for real-time situation analysis

Abstract

The level of automation in vehicles will significantly increase over the next decade. As automation will become more and more common, vehicles will not be able to master all traffic related situations for a long time by themselves. In such situations, the driver must take over and steer the vehicle through the situation. One of the important questions is when the takeover should be performed. Many decisive factors must be considered. On the one hand, the current traffic situation including roads, traffic light and other road users, especially vulnerable road users, and on the other hand, the state of the driver must be considered. The goal is to combine neurocognitive measurement of the drivers state and the static and dynamic traffic related data to develop an interpretation of the current situation. This situation analysis should be the basis for the determination of the best takeover point.