SafeCoDriver: Post-Intervention Safety with Cooperative Perception

Compared with single-vehicle perception, cooperative perception provides additional information about distant or occluded traffic objects, supporting earlier identification of potential conflicts. To respond to these potential conflicts, safety interventions modify a motion planner's nominal trajectory to avoid hazards. Existing methods guide these modifications through safety rules, safety constraints, and trajectory predictions to address potential hazards. However, the modifications themselves alter traffic interactions, which can introduce new conflicts or cause unnecessary slowing or stagnation. This motivates assessing post-intervention safety, i.e., whether intervention addresses hazards without introducing new collisions or stagnation. To address this need, this paper proposes SafeCoDriver, which combines the ego vehicle's onboard observations and cooperative observations to evaluate proposed interventions before execution. Trajectory Conflict Resolution (TCR) refines the complete trajectory, Multidirectional Threat Assessment (MTA) assesses surrounding threats, and Long-Horizon Feasibility Assessment (LHFA) checks predicted collisions and future lane occupancy. By integrating these components and decoupling geometric correction from the warning output, SafeCoDriver accounts for post-intervention safety. Closed-loop experiments in SUMO and open-loop evaluations on DeepAccident show that SafeCoDriver improves safety and reduces stagnation. It achieves the lowest collision rate in SUMO and the lowest waypoint exposure on DeepAccident among the evaluated safety baselines.