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

被引:115
作者
Kolekar, Sarvesh [1 ]
de Winter, Joost [1 ]
Abbink, David [1 ]
机构
[1] Delft Univ Technol, Fac Mech Maritime & Mat Engn 3mE, Dept Cognit Robot, Mekelweg 2, NL-2628 CD Delft, Netherlands
关键词
ROAD ENVIRONMENT; VISUAL CONTROL; LANE WIDTH; VEHICLE; SPEED; TRUST; PERFORMANCE; AUTOMATION; PERCEPTION; SIMULATOR;
D O I
10.1038/s41467-020-18353-4
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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-dimensional field that represents the driver's belief about the probability of an event occurring. The DRF, when multiplied with the consequence of the event, provides an estimate of the driver's perceived risk. Through human-in-the-loop and computer simulations, we show that human-like driving behaviour emerges when the DRF is coupled to a controller that maintains the perceived risk below a threshold-level. The DRF model predictions concur with driving behaviour reported in literature for seven different scenarios (curve radii, lane widths, obstacle avoidance, roadside furniture, car-following, overtaking, oncoming traffic). We conclude that our generalizable DRF model is scientifically satisfying and has applications in automated vehicles.
引用
收藏
页数:13
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