Microscopic Modeling of Abnormal Driving Behavior: A Two-Dimensional Stochastic Formulation with Customizable Safety Levels

Microscopic traffic models serve as indispensable tools in tasks such as constructing test scenarios for autonomous vehicles (AVs), predicting trajectories, and analyzing traffic flow dynamics. However, a significant proportion of these models rely on assumptions of normal behaviors. Yet, the validity of these assumptions is dubious given the heterogeneous nature of traffic flow and existence of abnormal driving behaviors. These limitations impede the efficacy of conventional microscopic models in crucial tasks like constructing AV test scenarios with specified risk levels, analyzing abnormal behaviors, etc. To address these challenges, this study contributes by proposing a model tailored to accommodate two-dimensional abnormal driving behaviors in microscopic traffic framework. The proposed approach have the following innovations: 1) it incorporates assumptions concerning abnormal behaviors in both the longitudinal and lateral dimensions; 2) abnormality at each dimension is captured by a combination of certain terms; 3) stochastic control barrier method is applied to customize the risk levels of the resulting traffic flow dynamics. Additionally, we present a method for retrieving vehicular maneuver information, enabling the extraction of detailed vehicle body gestures and driver control inputs, which would benefit the analysis of abnormal behavior. Our findings demonstrate that the proposed model yields longitudinal and lateral dynamics consistent with empirical observations, and various abnormal behavior patterns can be simulated.