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Paper FR-LM-T37.4

González González, Juan David (University of the Bundeswehr Munich), Maehlisch, Mirko (University of German Military Forces Munich)

Low-Latency LiDAR Data Quality Monitoring for ADAS under Adverse Weather Conditions

Scheduled for presentation during the Regular Session "S37a-Reliable Perception and Robust Sensing for Intelligent Vehicles" (FR-LM-T37), Friday, November 21, 2025, 11:30−11:50, 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, Lidar-based Mapping and Environmental Perception for ITS Applications, Autonomous Vehicle Safety and Performance Testing

Abstract

In this work, we present a low-latency method for monitoring the quality of data from a rotating LiDAR. Adverse weather conditions such as rain, snow, and fog can significantly degrade LiDAR data due to the presence of airborne particles. Rapidly detecting noisy measurements and identifying the affected regions within the point cloud can provide valuable insights for downstream tasks. By leveraging simple features derived from spatial, temporal, and point-level characteristics, our method efficiently localizes degraded areas in the point cloud. Operating at the packet level, it avoids the need to accumulate a full rotation, thereby maintaining low latency in the data stream. The method achieves performance comparable to more complex deep learning approaches, without requiring large labeled datasets.

 

 

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