One Perception, All Maneuvers: Directional Traffic Signal Understanding for Maneuver-Level Signal Intent Prediction

2026-09-26

AI Summary

This study addresses the limitation of existing autonomous driving traffic signal perception, which merely recognizes colors without semantically interpreting right-of-way for specific driving maneuvers. To bridge this gap, this work proposes a directional traffic signal understanding task that predicts structured signal states and passing permissions—such as going straight or turning—directly from images. Accordingly, a direction-level benchmark is constructed based on OpenLane-V2, alongside a direction-aware baseline model designed to integrate global context, local evidence, and action-specific representations. By closing the semantic gap between perception and planning, the proposed approach provides an interpretable signal interface. Experimental results demonstrate that it significantly improves passing permission prediction accuracy in complex intersection scenarios compared to conventional detection pipelines, thereby effectively supporting downstream path planning.

Abstract

Traffic lights are a key regulatory signal for autonomous driving at urban intersections, yet existing traffic signal perception is still predominantly formulated as instance-level detection or color recognition. Such formulations identify where traffic lights are and what colors they display, but leave a critical semantic gap before downstream planning: which ego maneuver is controlled by each visible signal and what dynamic permission the signal expresses for that maneuver. In this paper, we formulate Directional Traffic Signal Understanding, a decision-oriented task that predicts structured signal states for straight, left-turn, right-turn, and U-turn maneuvers from a front-view image. Each state contains the associated signal color and signal-implied passability. Based on OpenLane-V2, we provide a direction-level benchmark with maneuver-level supervision and metrics for color recognition, passability, full-frame consistency, and safety-critical errors. A direction-aware baseline combines global context, localized traffic-light evidence, and maneuver-specific representations. Experiments show that direction-level modeling improves passability prediction over image-level classifiers and detection-oriented pipelines, particularly at complex multi-signal intersections. The resulting representation provides a direct and interpretable traffic-signal interface for downstream planning together with topology, route, and surrounding-agent information.

Problem

• Directional Traffic Signal Understanding
• Maneuver-Level Prediction
• Passability
• Autonomous Driving
• Semantic Gap

Innovation

• Directional Traffic Signal Understanding
• Maneuver-Level Prediction
• Passability Prediction
• Direction-Aware Baseline
• Autonomous Driving