aiqbench analysis
This research addresses a critical bottleneck in autonomous driving by demonstrating that specialized machine learning models trained on specific fog-density levels significantly outperform unified general-purpose networks in low-visibility scenarios. The study's core contribution lies in its methodology of generating synthetic depth images to simulate five distinct weather states, achieving a substantial recall improvement for vehicles navigating very heavy fog conditions. By shifting from a single model architecture to density-specific training strategies, this work offers a tangible path toward more robust computer vision systems capable of handling adverse environmental factors.
Enhancing Visual Perception in Foggy Conditions via Multiclass Fog Density Modeling
arXiv:2608.01572v1 Announce Type: cross Abstract: Autonomous driving (AD) systems have advanced rapidly over the past decade; however, robust perception under adverse weather conditions remains a major challenge, particularly in dense fog. In this work, we investigate fog-aware perception using synthetically generated fog data derived from the Waymo dataset. To support fog simulation, depth images are generated using an iterative learning approach. We consider five fog-density levels: clear, light fog, moderate fog, heavy fog, and very heavy fog. Instead of training a single unified model across all conditions, we train separate perception models for each fog-density level. Experimental results show that density-specific training improves performance in severe fog conditions. In particular, for the very heavy fog class, recall improves from 0.076 to 0.232, corresponding to an absolute gain of 15.6 percentage points. These findings suggest that deploying multiple specialized models, rather than a single general-purpose model, can improve perception robustness for autonomous vehicles under challenging visibility conditions. Future work will extend this strategy to additional sensing modalities, including LiDAR and radar, and evaluate generalization across diverse weather scenarios.
Abstract:Autonomous driving (AD) systems have advanced rapidly over the past decade; however, robust perception under adverse weather conditions remains a major challenge, particularly in dense fog. In this work, we investigate fog-aware perception using synthetically generated fog data derived from the Waymo dataset. To support fog simulation, depth images are generated using an iterative learning approach. We consider five fog-density levels: clear, light fog, moderate fog, heavy fog, and very heavy fog. Instead of training a single unified model across all conditions, we train separate perception models for each fog-density level. Experimental results show that density-specific training improves performance in severe fog conditions. In particular, for the very heavy fog class, recall improves from 0.076 to 0.232, corresponding to an absolute gain of 15.6 percentage points. These findings suggest that deploying multiple specialized models, rather than a single general-purpose model, can improve perception robustness for autonomous vehicles under challenging visibility conditions. Future work will extend this strategy to additional sensing modalities, including LiDAR and radar, and evaluate generalization across diverse weather scenarios.
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
arXiv:2608.01572 [cs.CV]
(or arXiv:2608.01572v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2608.01572 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
https://doi.org/10.5220/0014996800004091 Focus to learn more DOI(s) linking to related resources
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