Abstract
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 DRF-based risk estimate (rˆ) could predict manual driving behaviour and the driver’s perceived risk during automated driving. To ensure that the participants perceived realistic levels of risk, the experiment was conducted in a test vehicle. Eight participants drove five laps manually and experienced 12 different laps of automated driving on a test track. The test track consisted of three sections (which were sub-divided into 12 sectors): curve driving (9 sectors), parked car (1 sector), and 90-degree intersections (2 sectors). If the driver verbally expressed risk or performed a takeover, that particular sector was labelled as risky. The results show that the DRF risk estimate (rˆ) predicted manual driving behaviour (ρsteering=0.69, ρspeed=0.64), as well as correlated with the driver’s perceived risk in curve driving (r2 = 0.98) and while negotiating a car parked outside the lane boundary (r2=0.59). In conclusion, the DRF-based risk estimate (rˆ) is predictive of manual driving behaviour and perceived risk in automated driving. Future research should include tactical and strategic components to the driving task.
Highlights
Recent years have seen a surge in the popularity of automated vehicles (AVs)
The results show that the Driver’s Risk Field’ (DRF) risk estimate (r) predicted manual driving behaviour, as well as correlated with the driver’s perceived risk in curve driving (r2 = 0.98) and while negotiating a car parked outside the lane boundary (r2 = 0.59)
To test if the DRF model could predict the driver’s behaviour during manual driving we compared the predictions of the model to the steering and speed control actions performed by the driver
Summary
AVs may solve several problems, they introduce new ones. What is acceptable driving behaviour for an AV, and when do humans takeover the AV’s control? It may be useful to have an estimate of the driver’s perceived risk in the AV (Zhang et al, 2019a). AVs will be used by drivers and passengers, and how these drivers and passengers perceive their system while driving is hardly known, yet essential to know. If drivers perceive high levels of risk, and the AV does not react appropriately and reliably, they could lose trust (Azevedo-Sa et al, 2021) and take over control, or reject the use of the AV altogether. It can be argued that new AVs need an assessment of how end users perceive their behaviour
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