Predictive Risk-Aware Reinforcement Learning for Autonomous Vehicles Using Safety Potential

Safety remains a central challenge in autonomous driving: overly rigid safeguards can cause unnecessary stops and erode efficiency. Addressing this safety–efficiency trade-off requires specifying what behaviors to incentivize. In reinforcement learning, the reward provides that specification. Conventional reward surrogates—such as distance gaps and time-to-collision (TTC)—depend on instantaneous geometry and often miss unfolding multi-vehicle interactions, whereas sparse terminal rewards provide no intermediate guidance. Accordingly, we adapt Safety Potential (SP)—a short-horizon, time-weighted path-overlap forecast—into a dense reward-shaping term that provides a predictive risk-aware signal for anticipatory throttle/brake control. In the CARLA v0.9.14 roundabout environment, SP attains 94% success with 3% collisions; in percentage points, this is 16.00, 13.00, and 5.75 higher success and 18.75, 9.50, and 7.25 lower collisions than No-Safe, Distance, and TTC, respectively. Adding a lightweight reactive guard at inference further reduces collisions to below 1% without sacrificing success. These results indicate that injecting a predictive, overlap-based risk measure directly into the reward supplies temporally consistent safety cues and clarifies the trade-off between progress and risk in reinforcement-learning-based vehicle control.