利用实时车流信息提升复杂路况下的车道检测精度。
TF-Lane: Traffic Flow Module for Robust Lane Perception
- 引入车流数据作为辅助信号，增强视觉感知能力
- 在Nuscenes上实现最高4.1%的检测准确率提升
- 无需额外成本，适合实车部署的自动驾驶系统
自动驾驶系统需要鲁棒的车道感知能力，但现有基于视觉的检测方法在遮挡或无车道线等场景下性能显著下降。虽然部分方法借助高精地图作为补充，但面临订阅成本高、实时性差的问题。本文提出一种交通流感知车道感知模块（TFM），利用实时车流信息作为新信息源，无需额外成本即可提升感知能力。该模块可无缝集成至现有车道检测算法中，基于真实自动驾驶场景设计，并在四个主流模型及两个公开数据集（Nuscenes、OpenLaneV2）上验证。实验表明，使用标准评估指标时，TFM在多个模型上持续提升性能，尤其在Nuscenes上最高实现+4.1% mAP增益。
原文摘要 · Abstract (English)
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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