Drivers with limited perception: model and application to traffic simulation

We propose a model of the driver perception suited for microscopic, agent-based traffic simulations. The model includes both top-down and bottom-up perception, and takes into account the limited amount of perceptive resource which gain access to short-term memory. The driving task is split into sub-tasks, which can be activated in parallel (e.g. car following and crossroads passing). Perceived entities (percepts) as well as subtasks are ranked with respect to their subjective value, and due to the bounded perception, only the more valuable percepts are sent to the decision module of the cognitive model. The competition among percepts to gain access to the short-term memory simulates attentional processes. A computational implementation of the model is proposed for the driver, using agent-based modeling. It is implemented in a traffic simulation environment and allows the driver-agent to manage the conflicts and the longitudinal space in the middle of the crossroads. This way, we improve the realism of the simulation. Furthermore, this model can lead to a new way of identifying and explaining near accidents. We illustrate some benefits for a microscopic traffic simulation at crossroads in two situations. The first scenario explores the traffic parameters at a crossroads, simulating various distributions of the driver's age. The second one demonstrates the adaptive behavior of simulated drivers facing a dangerous (e.g. hypo-vigilant) driver.