CAV Adverse Weather Simulation – Snow

The CAVH group started to work on this project during the 2020 summer. It completed by the end of 2020. Seven group members worked on this project, and the leaders are Han Cao, Lolita Schmitt, Emily Xu.

Current Issue

The arrival of winter brings challenging driving conditions for various kinds of vehicles. Snow in all its forms potentially increases travel times and the number of delays and accidents. The current issue for most automated vehicles is that their automation highly relies on cameras. The autonomous vehicles could only obtain information through this method, severely hindered during the snow. For instance, if the snow were not removed on time, the lane markings would become invisible for the vehicles.

Project Overview

This project built a geometric design of the connected automated vehicle and highway system (CAVH) under the snow scenario to resolve such obstacles. The connected automated vehicles (CAV) are designed to drive on dedicated lanes. Intelligent roadside units will send location information of different areas of the dedicated CAV lanes to CAVs. Therefore, in the eyes of an automated vehicle, the snow-covered lane marking will be lightened and shining. Thus automated driving would be achievable on the dedicated lane. The geometric design uses VISSIM 11 and includes simulated lanes from Madison to Chicago O’Hare Airport via Beltline and I-90. In addition to animating how the CAVH system functions under the snow scenario, we ran multiple simulations and compared the performances of human-driven vehicles and CAV.

Simulation Animation