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TF-Lane: Traffic Flow Module for Robust Lane Perception

Yihan Xie, Han Xia, Zhen Yang

TL;DR

This work tackles the fragility of vision-only lane perception in occluded or cue-sparse environments. It introduces TF-Lane, which embeds a Traffic Flow Module (TFM) that extracts real-time traffic flow cues and fuses them with existing lane detectors via a spatio-temporal dual-encoder. The approach yields consistent gains across multiple baselines and datasets, achieving up to $+4.1\%$ mAP on NuScenes, and demonstrates robustness through ablations and qualitative visualizations. Practically, TF-Lane offers a HD-map-free, low-latency prior that can enhance reliability in real-world autonomous driving systems with broad deployment potential.

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.

TF-Lane: Traffic Flow Module for Robust Lane Perception

TL;DR

This work tackles the fragility of vision-only lane perception in occluded or cue-sparse environments. It introduces TF-Lane, which embeds a Traffic Flow Module (TFM) that extracts real-time traffic flow cues and fuses them with existing lane detectors via a spatio-temporal dual-encoder. The approach yields consistent gains across multiple baselines and datasets, achieving up to mAP on NuScenes, and demonstrates robustness through ablations and qualitative visualizations. Practically, TF-Lane offers a HD-map-free, low-latency prior that can enhance reliability in real-world autonomous driving systems with broad deployment potential.

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.
Paper Structure (24 sections, 3 equations, 7 figures, 7 tables)

This paper contains 24 sections, 3 equations, 7 figures, 7 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Vision-based Approaches
  4. Prior-based Approaches
  5. Method
  6. Overall Framework
  7. Traffic Flow Extraction:
  8. Temporal Feature Encoding:
  9. Spatial Feature Fusion:
  10. Data Preparation
  11. Model Architecture
  12. Temporal Domain Encoder
  13. Spatial Domain Encoder
  14. Experiments
  15. Implementation Details
  16. ...and 9 more sections

Figures (7)

  • Figure 1: Illustration of Traffic Flow. In occluded or complex scenes, traffic flow information enhances the robustness of lane perception by providing real-time prior info.
  • Figure 2: TF-Lane Overall Architecture. The proposed TFM module consists of three key components: Traffic Flow Extraction, Temporal Feature Encoding, and Spatial Feature Fusion. The fused lane features are then processed by the decoder to obtain the final lane perception results in the scene.
  • Figure 3: Visualization of Traffic Flow in Datasets
  • Figure 4: Temporal Mask. This mechanism focuses on the validity of historical frames within the temporal domain of traffic flow instances, effectively tackling real-world challenges such as frame drops and occlusions.
  • Figure 5: Spatial Mask. This mechanism identifies valid instances within current spatial partitions for both traffic flow and lane structures, effectively addressing challenges such as highly complex scenes and sparse instance distribution.
  • ...and 2 more figures