A risk field-based metric correlates with driver's perceived risk in manual and automated driving: A test-track study

Quantifying drivers’ perceived risk is important in the design and evaluation of the behaviour of automated vehicles (AVs) and in predicting takeovers by the driver. A ‘Driver's Risk Field’ (DRF) function has been previously shown to be able to predict manual driving behaviour in several simulated scenarios. In this paper, we tested if the...

Which parts of the road guide obstacle avoidance? Quantifying the driver's risk field

Gibson and Crooks (1938) argued that a ‘field of safe travel’ could qualitatively explain drivers' steering behavior on straights, curved roads, and while avoiding obstacles. This study aims to quantitatively explain driver behavior while avoiding obstacles on a straight road, and quantify the ‘Driver's Risk Field’ (DRF). In a fixed-based...

Human-like driving behaviour emerges from a risk-based driver model

Current driving behaviour models are designed for specific scenarios, such as curve driving, obstacle avoidance, car-following, or overtaking. However, humans can drive in diverse scenarios. Can we find an underlying principle from which driving behaviour in different scenarios emerges? We propose the Driver’s Risk Field (DRF), a two...

A human-like steering model: Sensitive to uncertainty in the environment

The interaction between a human driver and an automated driving system may improve when the automation is designed in such a way that it behaves in a human-like manner. This paper introduces a human-like steering model, in which the driver adapts to the risk due to uncertainty in the environment. Current steering models take a risk-neutral...