Predicting the Influence of Adverse Weather on Pedestrian Detection with Automotive Radar and Lidar Sensors

TL;DR

This paper investigates how rain and fog affect pedestrian detection using automotive radar and lidar sensors, introducing a Weather Filter model that improves prediction accuracy with minimal testing.

Contribution

It provides comprehensive empirical data on weather effects and presents a novel Weather Filter model that enhances prediction of sensor performance under adverse conditions.

Findings

Measurement results align with existing literature.

The Weather Filter outperforms the baseline model.

Minimal testing effort required for accurate predictions.

Abstract

Pedestrians are among the most endangered traffic participants in road traffic. While pedestrian detection in nominal conditions is well established, the sensor and, therefore, the pedestrian detection performance degrades under adverse weather conditions. Understanding the influences of rain and fog on a specific radar and lidar sensor requires extensive testing, and if the sensors' specifications are altered, a retesting effort is required. These challenges are addressed in this paper, firstly by conducting comprehensive measurements collecting empirical data of pedestrian detection performance under varying rain and fog intensities in a controlled environment, and secondly, by introducing a dedicated Weather Filter (WF) model that predicts the effects of rain and fog on a user-specified radar and lidar on pedestrian detection performance. We use a state-of-the-art baseline model representing the physical relation of sensor specifications, which, however, lacks the representation of secondary weather effects, e.g., changes in pedestrian reflectivity or droplets on a sensor, and adjust it with empirical data to account for such. We find that our measurement results are in agreement with existent literature related to weather degredation and our WF outperforms the baseline model in predicting weather effects on pedestrian detection while only requiring a minimal testing effort.