A Probabilistic Framework for Microscopic Traffic Propagation

Probabilistic microscopic traffic models provide a statistical representation of interactive behavior between traffic participants. They are crucial for the validation of automotive safety systems that make decisions based on surrounding traffic. The construction of such models by hand is error-prone and difficult to extend to the complete diversity of human behavior. This paper describes a methodology for microscopic traffic model construction based on a Bayesian statistical framework connected to real-world data and applies it to learning models for free-flow, car following, and lane-change behaviors on highways. The evolution of traffic scenes is represented by a generative model learned for individual vehicles that captures their response to other traffic participants as well as the road structure. Our evaluation shows realistic behaviors over a four second horizon. A complete implementation is available online.