CAV Adverse Weather Simulation – Heavy Rain

This study aims to learn the impact of rain on automated driving and how connected automated vehicle and highway system (CAVH) can improve the driving performance of CAV in rain scenarios. The CAVH group began to work on this project during the 2021 spring and it was completed by the end of summer in the same year. A total of seven graduate students* participated in this project, along with the help of multiple undergraduate students from the 579-4 class.

Current Issue

Unlike extreme weather conditions that might only appear in certain areas, rain occurs frequently worldwide. The characteristics of rain are quantified by its intensity, duration, and frequency; wind and fog are other sub-characteristics that are of great influence on the driver. All the main and sub characteristics of rain impact the visibility of the drivers. On automated vehicles, rain will cause disruptions for the vehicle’s sensor, such as visibility degradation on the camera, or reflectivity degradation on the LiDAR.  Splash of water caused by the rain can also cause problems on LiDAR’s sensor. Overall, rain heavily influences the driving behavior of both human-driven and automated vehicles.

Project Overview

In order to resolve such obstacles, CAVH is proposed for a more accurate routing decision. CAVH integrates new technologies that aid the car with the required geometrical and local weather knowledge of the transportation system they are using. The goal of this simulation project focuses on the analysis of the advantages of CAVH implementation. In the project, a geometric design of the CAVH is built under the heavy rain scenario using PTV Vissim from Cherry Valley to Gilbert I90. The connected automated vehicles are designed to drive on dedicated lanes. The infrastructure and vehicle are both set to be level 3, in which the CAVH will be equipped with communication devices such as computer servers, intelligent roadside units with cameras, LiDAR, 5G network, and reference markers.

During the rain, the roadside infrastructure and the reference markers can enhance the sensing capability of the system. They provide additional sensing functions and identify roadway markings and signs, therefore they work as alternate eyes for blind vehicles and provide virtual lane lighting-up signals for localization. Therefore, under heavy rain conditions, Vissim showed better performance on a dedicated CAVH lane than regular human-driven lanes. CAVH system increases the flow and speed of vehicles while decreasing delay and the probability of an accident by providing accurate and necessary information to the CAV. The proposed CAVH system has diminished the risk of the driver in rainy conditions and increased the overall highway safety.

* Kentin Brummett, Hanif Nazaroedin, Lolita Schmitt, Erynn Schroeder, Haotian Shi, Emily Xu and Jingwen Zhu together participated in this project.

For more details, please check the following documents

Simulation Animation