Evaluating the Effects of Cooperative Perception on Avoiding Pedestrian Crashes for Connected and Automated Vehicles
Automated-Vehicle (AV) technologies are expected to improve road safety by detecting the surrounding environment with the equipped sensors (e.g., camera, radar) and making necessary driving decisions. It is worth noting that AVs may not be able to detect all the unsafe conditions due to the limitations of the detection range and/or detection accuracy. The on-board sensors might not be able to detect or classify a target object if it is far away from the vehicle or occluded by other road objects, which might lead to a crash. Pedestrian crashes are more likely to fatal or severe injury crashes. Since 2009, the number of pedestrian fatalities has been rising, reaching over 6,200 in 2019 with an increase of 51%. This project aims to explore the effects of cooperative perception on avoiding pedestrian crashes for connected and automated vehicles. A cooperative perception system will be developed based on a co-simulation platform of virtual simulation (e.g., CARLA) and microsimulation (e.g., SUMO). The cooperative perception is programmed in the simulation platform. The safety benefits of cooperative perception will be evaluated and quantified under different conditions. Besides, to mitigate the data transmission load, data analysis will be conducted to develop statistical models or machine learning algorithms, which could help determine when the cooperative perception is needed and how the perception data from roadside sensors should be fused.
Language
- English
Project
- Status: Active
- Funding: $55000
-
Contract Numbers:
69A3551747131
-
Sponsor Organizations:
Office of the Assistant Secretary for Research and Technology
University Transportation Centers Program
Department of Transportation
Washington, DC United States 20590 -
Managing Organizations:
University of Iowa, Iowa City
National Advanced Driving Simulator, 2401 Oakdale Blvd
Iowa City, IA United States 52242-5003 -
Performing Organizations:
University of Central Florida, Orlando
Department of Civil, Environmental & Contruction Engineering
1280 Pegasus Drive, 442B Engineering II
Orlando, FL United States 32816 -
Principal Investigators:
Wu, Yina
Abdel-Aty, Mohamed
- Start Date: 20210601
- Expected Completion Date: 20220531
- Actual Completion Date: 0
- USDOT Program: University Transportation Centers
Subject/Index Terms
- TRT Terms: Autonomous vehicles; Connected vehicles; Data fusion; Machine learning; Pedestrian vehicle crashes; Sensors; Simulation; Statistical analysis
- Subject Areas: Data and Information Technology; Highways; Pedestrians and Bicyclists; Safety and Human Factors; Vehicles and Equipment;
Filing Info
- Accession Number: 01772188
- Record Type: Research project
- Source Agency: Safety Research Using Simulation University Transportation Center (SaferSim)
- Contract Numbers: 69A3551747131
- Files: UTC, RIP
- Created Date: May 24 2021 11:56AM