Robust Perception for Autonomous Driving Under Low-Visibility Conditions: A Review of Low-Light Enhancement, Multimodal Fusion, and Task-Oriented Detection

Abstract

Nighttime driving and adverse weather expose persistent weaknesses in autonomous-driving perception pipelines. Low illumination, fog, rain, snow, glare, wet-road reflections, and motion blur degrade camera, LiDAR, radar, and event-camera inputs in modality-specific ways. This review examines robust perception under low-visibility conditions as a pipeline-level problem requiring joint consideration of image enhancement, sensor fusion, detection, and evaluation. Rather than treating enhancement as an isolated restoration task, it analyzes whether recent methods preserve detector-relevant structures, exploit cross-sensor complementarity, and improve downstream 2D and 3D perception. A taxonomy-driven narrative approach compares representative studies along five axes, from input modality and supervision strategy to evaluation protocol and deployment feasibility, supported by a structured verification search of literature published between January 2020 and July 2026, with the search strategy, eligibility criteria, and corpus composition documented. Recent work indicates a shift from image-quality-oriented restoration toward perception-driven optimization, in which enhancement and fusion modules are evaluated by their effect on object detection and 3D perception. Remaining challenges include generalization to compound degradations, cross-sensor misalignment, scene-dependent sensor reliability, latency on in-vehicle edge platforms, and inconsistent benchmark protocols. Future systems should therefore jointly model degradation severity, sensor reliability, downstream task performance, and real-time constraints rather than optimizing restoration, fusion, and detection modules in isolation.