MOHA: A Multi-Mode Hybrid Automaton Model for Learning Car-Following Behaviors

This paper proposes a novel hybrid model for learning discrete and continuous dynamics of car-following behaviors. Multiple modes representing driving patterns are identified by partitioning the model into groups of states. The model is visualizable and interpretable for car-following behavior recognition, traffic simulation, and human-like...

Car-following Behavior Model Learning Using Timed Automata

Learning driving behavior is fundamental for autonomous vehicles to “understand” traffic situations. This paper proposes a novel method for learning a behavioral model of car-following using automata learning algorithms. The model is interpretable for car-following behavior analysis. Frequent common state sequences are extracted from the model...

Longitudinal Driving Behavior under Adverse Conditions: A Close Look at Psycho-spacing Models

Adverse weather conditions have been shown to have a substantial impact on traffic flow operations following substantial adaptation effects in driving behavior. In quantifying these effects psycho-spacing models may be used. In this contribution it was examined to what extend adverse weather conditions influence the position of action points in...