More Than Following: Introducing Reversing Behavior for Irregular-Aware Traffic Simulation by Interactive Editing

Abstract

Though current traffic simulation methods can produce impressive results, reversing behavior is always ignored, potentially reducing the diversity and plausibility of simulation data. Furthermore, while common traffic behaviors like following-the-leader and lane changing can be easily simulated, efficiently generating irregular cases in a human-in-the-loop manner with specific motions based on user desires is less discussed. To address the gap, we present a novel interactive traffic editing and simulation framework that enables users to regulate vehicles via simple inputs to introduce reversing and generate desired trajectory data with both car-following and irregular driving behaviors. With key states specified, lane-level navigation, including forward/backward directions, is planned through heuristic search. The customized navigation brings the vehicles' new trajectories with both car-following and reversing, and their surrounding neighbors are also adjusted accordingly. To provide smooth and plausible motions after editing, vehicles are updated via the optimization-based simulation method, which takes vehicle kinematics, self-motivation, path keeping, collision avoidance, and special interaction rules into account. We demonstrate that our framework can generate uncommon traffic cases and validate it through extensive experiments.