Mathematical Models of Human Drivers Using Artificial Risk Fields

Jensen, Emily; Luster, Maya; Yoon, Hansol; Pitts, Brandon J.; Sankaranarayanan, Sriram

doi:10.1109/itsc55140.2022.9922389

Cited by 1 publication

(1 citation statement)

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“…Recent research in non-interactive scenarios has shown that a quantification of perceived risk can explain driver adaptations in speed and lateral position and can be used to accurately predict future trajectories [ 4 , 24 , 25 ]. In our framework, we combine this notion of risk-based decision-making in driving with Simon’s ideas of bounded rationality [ 26 ] and satisficing [ 27 ].…”

Section: Communication-enabled-interaction Modellingmentioning

confidence: 99%

Modelling communication-enabled traffic interactions

2023

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A major challenge for autonomous vehicles is handling interactions with human-driven vehicles—for example, in highway merging. A better understanding and computational modelling of human interactive behaviour could help address this challenge. However, existing modelling approaches predominantly neglect communication between drivers and assume that one modelled driver in the interaction responds to the other, but does not actively influence their behaviour. Here, we argue that addressing these two limitations is crucial for the accurate modelling of interactions. We propose a new computational framework addressing these limitations. Similar to game-theoretic approaches, we model a joint interactive system rather than an isolated driver who only responds to their environment. Contrary to game theory, our framework explicitly incorporates communication between two drivers and bounded rationality in each driver’s behaviours. We demonstrate our model’s potential in a simplified merging scenario of two vehicles, illustrating that it generates plausible interactive behaviour (e.g. aggressive and conservative merging). Furthermore, human-like gap-keeping behaviour emerged in a car-following scenario directly from risk perception without the explicit implementation of time or distance gaps in the model’s decision-making. These results suggest that our framework is a promising approach to interaction modelling that can support the development of interaction-aware autonomous vehicles.

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