ITSC 2025 Paper Abstract

Paper WE-LA-T13.2

Zhao, Runxin (Shanghai Jiao Tong University), Zhuang, Hanyang (Shanghai Jiao Tong University), Wang, Chunxiang (Shanghai Jiao Tong University), Yang, Ming (Shanghai Jiao Tong University)

Bench-RNR: Dataset for Benchmarking Repetitive and Non-Repetitive Scanning LiDAR for Infrastructure-Based Vehicle Localization

Scheduled for presentation during the Regular Session "S13c-Localization, Mapping, and Sensing for Robust Navigation in ITS" (WE-LA-T13), Wednesday, November 19, 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 19, 2025

Keywords Infrastructure Requirements for Connected and Automated Vehicles, Validation of Cooperative Driving and Connected Vehicle Systems, Real-time Object Detection and Tracking for Dynamic Traffic Environments

Abstract

Vehicle localization using roadside LiDARs can provide centimeter-level accuracy for cloud-controlled vehicles while simultaneously serving multiple vehicles, enhanc-ing safety and efficiency. While most existing studies rely on repetitive scanning LiDARs, non-repetitive scanning LiDAR offers advantages such as eliminating blind zones and being more cost-effective. However, its application in roadside perception and localization remains limited. To address this, we present a dataset for infrastruc-ture-based vehicle localization, with data collected from both repetitive and non-repetitive scanning LiDARs, in order to benchmark the performance of different LiDAR scanning patterns. The dataset contains 5,445 frames of point clouds across eight vehicle trajectory sequences, with diverse trajectory types. Our experiments establish baselines for infrastructure-based vehicle localization and compare the performance of these methods using both non-repetitive and repetitive scanning LiDARs. This work offers valuable insights for selecting the most suitable LiDAR scanning pattern for infrastruc-ture-based vehicle localization. Our dataset is a signifi-cant contribution to the scientific community, supporting advancements in infrastructure-based perception and vehicle localization. The dataset and source code are publicly available at: https://github.com/sjtu-cyberc3/BenchRNR.