What Road Elements are More Important than Others for Safe Driving on Urban Roads?

Road elements are increasingly digitalized to provide drivers advanced assistance especially in the emergent or adverse conditions. It is challenging and expensive to accurately digitalize all the road elements especially on the urban roads with many infrastructures and complex designs, where we may focus on the most important ones at the first stage. This research designs a questionnaire to ask the drivers to rank the importance of the road elements in various driving conditions. Driver characteristics are also collected, including age, driving style, accident experience, and accumulated driving distance, to explore their effect on drivers’ cognition of road elements importance. It is found that driving is a complex activity, and the moving elements (e.g. surrounding cars) are more important than the non-moving ones. Attention should be paid to the road elements even distant from the ego car, to get prepared to the potential driving risk or penalty. Statistical difference between the experienced and non-experienced drivers recommends that driver assistance system should be sufficiently trained in various conditions, to build up autonomous driving tactics and skills. This research promotes the understanding of driving cognition pattern to provide insights into the development of road digitalization

Applicability of the Future State Maximization Paradigm to Agent-Based Modeling: A Case Study on the Emergence of Socially Sub-Optimal Mobility Behavior

Novel developments in artificial intelligence excel in regard to the abilities of rule-based agent-based models (ABMs), but are still limited in their representation of bounded rationality. The future state maximization (FSX) paradigm presents a promising methodology for describing the intelligent behavior of agents. FSX agents explore their future state space using “walkers” as virtual entities probing for a maximization of possible states. Recent studies have demonstrated the applicability of FSX to modeling the cooperative behavior of individuals. Applied to ABMs, the FSX principle should also represent non-cooperative behavior: for example, in microscopic traffic modeling, there is a need to model agents that do not fully adhere to the traffic rules. To examine non-cooperative behavior arising from FSX, we developed a road section model populated by agent-cars endowed with an augmented FSX decision making algorithm. Simulation experiments were conducted in four scenarios modeling various traffic settings. A sensitivity analysis showed that cooperation among the agents was the result of a balance between exploration and exploitation. We showed that our model reproduced several patterns observed in rule-based traffic models. We also demonstrated that agents acting according to FSX can stop cooperating. We concluded that FSX can be useful for studying irrational behavior in certain traffic settings, and that it is suitable for ABMs in general