Driver's risk field: A step towards a unified driver model

The National Highway Transportation Safety Administration (NHTSA) reports that 94-96% of the road accidents involve human error. These statistics make it seem as if humans are terrible drivers. However, a different set of numbers paint a completely different picture. According to the United States Bureau of Transportation Statistics, the failure...

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...

How road narrowing impacts the trade-off between two adaptation strategies: Reducing speed and increasing neuromuscular stiffness

When drivers encounter a road narrowing two potential adaptation strategies come into play that may increase safety margins: decreasing speed and increasing neuromuscular stiffness of the arms. These two adaption strategies have so far been studied in isolation. We expect that there is a trade-off between these two strategies, and that risk...

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...

Design of time-to-lane-crossing based haptic steering guidance

Current haptic control systems provide feedback torques based on a lateral deviation with respect to a reference trajectory (i.e., centre of the lane), which do not capture the satisficing behaviour human beings typically adopt during a lane keeping task. As such, a novel time-to-lane-crossing-based controller is proposed, which is expected to...

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...