# A Benchmark for Spray from Nearby Cutting Vehicles

arXiv: https://arxiv.org/abs/2108.10800 · HTML: https://mlsanity.com/paper/2108.10800/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 39 references that are in the map.
- First posted on arXiv 24 Aug 2021.

## Builds on (most cited first, 5 of 5)

- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection](https://mlsanity.com/paper/1912.13192/) (2019)
- [A Benchmark for Lidar Sensors in Fog: Is Detection Breaking Down?](https://mlsanity.com/paper/1912.03251/) (2019)
- [Benchmarking Image Sensors Under Adverse Weather Conditions for Autonomous Driving](https://mlsanity.com/paper/1912.03238/) (2019)

## Built on by (most cited first, 5 of 5)

- [Robust 3D Object Detection in Cold Weather Conditions](https://mlsanity.com/paper/2205.11925/) (2022)
- [Energy-based Detection of Adverse Weather Effects in LiDAR Data](https://mlsanity.com/paper/2305.16129/) (2023)
- [Detection of Condensed Vehicle Gas Exhaust in LiDAR Point Clouds](https://mlsanity.com/paper/2207.04908/) (2022)
- [Simulating Road Spray Effects in Automotive Lidar Sensor Models](https://mlsanity.com/paper/2212.08558/) (2022)
- [Towards Robust 3D Object Detection In Rainy Conditions](https://mlsanity.com/paper/2310.00944/) (2023)

## Abstract (arXiv)

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.