Towards Robust and Interpretable Event–Frame Fusion for Autonomous Driving

Abstract

Autonomous driving must handle motion blur, low light, and fast-changing scenes, where RGB frames and event cameras provide complementary strengths. This thesis explores how to fuse them across the perception–reasoning–planning pipeline. It introduces FlexEvent, a frequency-robust detector with adaptive fusion and label-efficient training; Talk2Event, the first benchmark for event–language grounding with attribute-aware modeling; and the EventDrive, an event–frame VLM covering the full driving loop. Together, these contributions advance robust perception, interpretable reasoning, and reliable planning for safety-critical driving through event–frame fusion.