Validating DVS Application in Autonomous Driving with Various AEB Scenarios in CARLA Simulator

Predicting potential collisions with leading vehicles is a fundamental capability of autonomous and assisted driving systems. In particular, automatic emergency braking (AEB) demands reaction times on the order of microseconds. A key limitation of existing approaches lies in their update rate, which is constrained by the sampling speed of conventional sensors. Event-based Dynamic Vision Sensors (DVSs), with their microsecond temporal resolution and high dynamic range, offer a promising alternative to frame-based cameras in challenging driving environments. In this work, we investigate the integration of DVS into autonomous driving pipelines, focusing specifically on AEB scenarios. Building on our earlier work, where a YOLO-based detection model was trained on real-world DVS data, we extend the approach to CARLA’s simulated DVS environment. We publish a CARLA-compatible 2-channel DVS dataset aligned with our detection model, bridging the gap between real-world recordings and simulation. Through a series of simulated AEB scenarios, we demonstrate how DVS enables earlier and more reliable detection compared to RGB cameras, resulting in improved braking performance.