A Benchmark for Spray from Nearby Cutting Vehicles

TL;DR

This paper introduces a benchmarking methodology for assessing spray disturbances from nearby vehicles on perception systems in autonomous driving, highlighting the need for robust testing in adverse weather conditions.

Contribution

It presents a novel lightweight spray setup and evaluation scheme to quantify spray effects on cameras and LiDAR, enabling reproducible benchmarking for autonomous vehicle perception.

Findings

Spray significantly distorts perception systems up to four seconds.

Distortions severely impact RGB cameras and LiDAR sensors in close vehicle scenarios.

Benchmarking methods are essential for developing robust autonomous driving systems.

Abstract

Current driver assistance systems and autonomous driving stacks are limited to well-defined environment conditions and geo fenced areas. To increase driving safety in adverse weather conditions, broadening the application spectrum of autonomous driving and driver assistance systems is necessary. In order to enable this development, reproducible benchmarking methods are required to quantify the expected distortions. In this publication, a testing methodology for disturbances from spray is presented. It introduces a novel lightweight and configurable spray setup alongside an evaluation scheme to assess the disturbances caused by spray. The analysis covers an automotive RGB camera and two different LiDAR systems, as well as downstream detection algorithms based on YOLOv3 and PV-RCNN. In a common scenario of a closely cutting vehicle, it is visible that the distortions are severely affecting the perception stack up to four seconds showing the necessity of benchmarking the influences of spray.