Fusion techniques of frame and event cameras in autonomous driving: A review

The rapid advancement of autonomous driving technology demands robust environmental perception systems capable of operating under extreme illumination variations, high-speed motion, and adverse weather conditions. While conventional frame-based cameras offer rich spatial and textural information, they suffer from fixed frame rates and limited dynamic range. Event cameras, as neuromorphic vision sensors, provide unique advantages in temporal resolution, dynamic range, and power efficiency through asynchronous pixel-level brightness change detection. This paper presents the first comprehensive review of frame-event camera fusion technology for autonomous driving applications. This paper establishes a fusion framework tailored to autonomous driving perception requirements and analyzes the complementary characteristics of both sensor modalities in addressing critical perception challenges. This paper proposes the first systematic classification of frame-event fusion architectures, covering multi-level approaches from data-level to decision-level integration, while tracing the technical evolution of fusion strategies. Additionally, this paper constructs a dataset evaluation framework for autonomous driving tasks, providing systematic benchmark selection guidance. Through detailed analysis of deployment challenges, this paper identifies key technical barriers including temporal synchronization, computational efficiency, and cross-modal calibration, alongside corresponding solutions. Finally, this paper presents perspectives on emerging paradigms and future directions, providing essential references for advancing practical frame-event fusion applications in autonomous driving.