Models of Human Decision-Making as Tools for Estimating and Optimizing Impacts of Vehicle Automation

Abstract

With the development of increasingly automated vehicles (AVs) comes the increasingly difficult challenge of comprehensively validating these for acceptable, and ideally beneficial, impacts on the transport system. There is a growing consensus that virtual testing, where simulated AVs are deployed in simulated traffic, will be key for cost-effective testing and optimization. The least mature model components in such simulations are those generating the behavior of human agents in or around the AVs. In this paper, human models and virtual testing applications are presented for two example scenarios: (i) a human pedestrian deciding whether to cross a street in front of an approaching automated vehicle, with or without external human–machine interface elements, and (ii) an AV handing over control to a human driver in a critical rear-end situation. These scenarios have received much recent research attention, yet simulation-ready human behavior models are lacking. They are discussed here in the context of existing models of perceptual decision-making, situational awareness, and traffic interactions. It is argued that the human behavior in question might be usefully conceptualized as a number of interrelated decision processes, not all of which are necessarily directly associated with externally observable behavior. The results show that models based on this type of framework can reproduce qualitative patterns of behavior reported in the literature for the two addressed scenarios, and it is demonstrated how computer simulations based on the models, once these have been properly validated, could allow prediction and optimization of AV impacts on traffic flow and traffic safety.