ITSC 2025 Paper Abstract

Paper TH-LA-T28.2

OUBOUABDELLAH, Selma Nourhane (LS2N - Laboratoire des sciences du numérique de Nantes - Ecole C), Dao, Minh Quan (INRIA), Malis, Ezio (INRIA), Héry, Elwan (LS2N (UMR CNRS 6004) École Centrale de Nantes), Moreau, Julien (University of technology of Compiègne (UTC)), FREMONT, Vincent (Ecole Centrale de Nantes, CNRS, LS2N, UMR 6004)

Improving Vulnerable Road-Users Detection through Hybrid Collaborative Perception and Detection Refinement

Scheduled for presentation during the Regular Session "S28c-Multi-Sensor Fusion and Perception for Robust Autonomous Driving" (TH-LA-T28), Thursday, November 20, 2025, 16:20−16:40, 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 Advanced Sensor Fusion for Robust Autonomous Vehicle Perception, Lidar-based Mapping and Environmental Perception for ITS Applications, Deep Learning for Scene Understanding and Semantic Segmentation in Autonomous Vehicles

Abstract

Ensuring the safety of autonomous vehicles in complex urban environments critically depends on accurate 3D object detection. While LiDAR sensors provide reliable depth information, their effectiveness is limited by sparsity at long distances and occlusions, particularly in intersection scenarios. Collaborative perception addresses these challenges by enabling information sharing among vehicles and infrastructure sensors, with intermediate fusion offering a balance between communication efficiency and detection accuracy. However, existing collaborative perception frameworks exhibit a notable performance gap between detecting vehicles and vulnerable road users such as cyclists and pedestrians. In this work, we propose a novel hybrid collaboration framework designed to reduce this gap. Our method leverages late-stage information from communicating agents to augment the ego agent's point cloud, then applies a standard intermediate fusion strategy, followed by a refinement stage that further improves the detection accuracy of various objects. Experiments on the Mixed Signals dataset demonstrate that our approach sets a new state-of-the-art in the detection of vulnerable road users in urban V2X scenarios.