Enhancing LiDAR Performance under Fog with Adaptive Multi-Echo and Feature-Based Filtering

LiDAR (Light Detection and Ranging) systems are essential for autonomous driving (AD) and advanced driver-assistance systems (ADAS), providing accurate 3D perception of the surrounding environment. However, their performance significantly deteriorates under adverse weather conditions such as fog, where laser pulses are scattered by airborne particles, resulting in substantial noise and reduced ranging accuracy. This scattering effect makes it difficult to detect objects within or behind particulate matter, posing a serious challenge for reliable perception in real-world driving scenarios. To address this issue, we propose an algorithm that combines adaptive multi-echo signal processing with a feature-integrated, rule-based denoising framework to enhance LiDAR performance in noisy environments. The multi-echo approach selectively utilizes meaningful signal returns by evaluating both intensity and relative echo positions. Based on predefined rules, the algorithm identifies the echo most likely to represent a real object. The rule-based denoising algorithm dynamically adjusts thresholds by integrating multiple features, including point clouds density, intensity, and echo width. These features are evaluated in conjunction with measured distance to adaptively suppress fog noise and improve signal reliability. This synergistic method enables robust detection of real objects even in low-visibility conditions. Experimental evaluations demonstrate that the proposed algorithm significantly improves effective ranging distance under adverse conditions compared to conventional methods. Furthermore, it eliminates up to approximately 99% of noise induced by airborne particles in foggy scenarios. These results highlight the potential of our approach to enhance LiDAR reliability and safety in real-world automotive applications, contributing to the advancement of autonomous driving technologies under all-weather conditions.