TF-Lane: Traffic Flow Module for Robust Lane Perception

• URL: http://arxiv.org/abs/2602.01277v1
• Date: Sun, 01 Feb 2026 15:18:48 GMT
• Title: TF-Lane: Traffic Flow Module for Robust Lane Perception
• Authors: Yihan Xie, Han Xia, Zhen Yang,
• Abstract summary: This paper proposes a TrafficFlow-aware Lane perception Module (TFM)<n>It extracts real-time traffic flow features and seamlessly integrates them with existing lane perception algorithms.<n>It consistently improves performance, achieving up to +4.1% mAP gain on the Nuscenes dataset.
• Score: 8.229324385163801
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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