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

Sarvesh Kolekar*, Joost de Winter, David Abbink

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

54 Citations (Scopus)
73 Downloads (Pure)


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.

Original languageEnglish
Article number4850
Number of pages13
JournalNature Communications
Issue number1
Publication statusPublished - 2020


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