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Paper FR-LM-T43.3

Belotti, Ottavia (Politecnico di Milano), De Luca, Alessandro (Politecnico di Milano), Pieroni, Riccardo (Politecnico di Milano), Corno, Matteo (Politecnico di Milano), Savaresi, Sergio M. (Politecnico di Milano)

Camera-LiDAR Traffic Light Relevance Estimation As Graph-Matching for Autonomous Vehicles

Scheduled for presentation during the Regular Session "S43a-Multi-Sensor Fusion and Perception for Robust Autonomous Driving" (FR-LM-T43), Friday, November 21, 2025, 11:10−11:30, Stradbroke

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 18, 2025

Keywords Data Analytics and Real-time Decision Making for Autonomous Traffic Management, Advanced Sensor Fusion for Robust Autonomous Vehicle Perception

Abstract

This paper addresses the challenge of identifying which traffic light in an intersection regulates the motion of the ego vehicle. We propose a modular pipeline that combines standard traffic light recognition with a novel relevance estimation module based on human-like logic. The core contribution is a graph-based reasoning method that encodes semantic cues from camera images and geometric information from LiDAR into a structured scene graph. This graph is matched against an offline topological map using inexact graph matching to determine the most relevant traffic light. We evaluate our approach on a proprietary dataset collected on Italian roads, demonstrating promising performance in identifying relevant signals and ensuring appropriate behavior at intersections. Our results highlight the potential of the proposed method as a reliable and efficient component for traffic light understanding in autonomous driving systems.

 

 

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