ITSC 2025 Paper Abstract

Paper FR-EA-T37.6

Cho, Sungeun (Ajou University), Ryoo, Seonghoon (Ajou University), Roh, Chiwoo (Korea Transport Institute(KOTI)), Lee, Soomok (Ajou University), So, Jaehyun (Ajou University)

Monitoring and Measurement Estimation Using LiDAR Sensors on Autonomous Vehicles

Scheduled for presentation during the Regular Session "S37b-Reliable Perception and Robust Sensing for Intelligent Vehicles" (FR-EA-T37), Friday, November 21, 2025, 14:50−15:30, Coolangata 1

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 Verification of Autonomous Vehicle Sensor Systems in Real-world Scenarios, Traffic Management for Autonomous Multi-vehicle Operations, Real-time Motion Planning and Control for Autonomous Vehicles in ITS Networks

Abstract

Traffic monitoring is crucial for effective traffic management, planning, and safety assessment. Conventional monitoring methods depend heavily on fixed infrastructure such as loop detectors and stationary video cameras, which, despite their accuracy, have inherent limitations including restricted spatial coverage, susceptibility to adverse weather conditions. In contrast, the rise of autonomous vehicles (AVs) equipped with advanced sensors such as LiDAR presents a unique opportunity. Although primarily intended for vehicle navigation and safety, these sensors continuously collect rich datasets that inherently provide comprehensive traffic information. This study proposes a proof-of-concept framework to demonstrate the feasibility of extracting detailed traffic metrics—such as lane-level speed, density, traffic flow, and time-to-collision (TTC)—from vehicle-mounted LiDAR sensors. Using the publicly available Waymo Open Dataset and the MCTrack algorithm, the research highlights how individual AV sensor data can be leveraged to derive meaningful traffic indicators without requiring additional public sector infrastructure investment. Furthermore, this study considers a scenario in which aggregating multiple AVs’ sensor data significantly enhances spatial coverage and improves the reliability and comprehensiveness of macro-level traffic metrics as AV market penetration increases.