🤖 AI Summary
This work addresses the performance degradation of existing lane perception methods under challenging conditions such as occlusion or missing lane markings, where visual cues are insufficient, and highlights the high cost and poor real-time capability of HD map–dependent approaches. To overcome these limitations, the authors propose a Traffic Flow-aware Module (TFM), which, for the first time, leverages real-time, zero-cost traffic flow information as an auxiliary modality for lane perception—requiring neither additional hardware nor HD maps. TFM employs deep learning to extract dynamic traffic flow features and integrates them with mainstream lane detection models through multimodal fusion. Experiments on the NuScenes and OpenLaneV2 datasets demonstrate consistent performance gains across four state-of-the-art models upon incorporating TFM, with mAP improvements of up to 4.1%, significantly enhancing robustness in complex driving scenarios.
📝 Abstract
Autonomous driving systems require robust lane perception capabilities, yet existing vision-based detection methods suffer significant performance degradation when visual sensors provide insufficient cues, such as in occluded or lane-missing scenarios. While some approaches incorporate high-definition maps as supplementary information, these solutions face challenges of high subscription costs and limited real-time performance. To address these limitations, we explore an innovative information source: traffic flow, which offers real-time capabilities without additional costs. This paper proposes a TrafficFlow-aware Lane perception Module (TFM) that effectively extracts real-time traffic flow features and seamlessly integrates them with existing lane perception algorithms. This solution originated from real-world autonomous driving conditions and was subsequently validated on open-source algorithms and datasets. Extensive experiments on four mainstream models and two public datasets (Nuscenes and OpenLaneV2) using standard evaluation metrics show that TFM consistently improves performance, achieving up to +4.1% mAP gain on the Nuscenes dataset.