A Simulation Study on Cooperative Perception for Vulnerable Road Users (VRU) at Signalized Intersections Found That, Under Ideal Conditions, It Could Prevent 98 Percent of VRU crashes.
Four Situations Tested in Cooperative Driving Automation Simulation to Evaluate Vulnerable Road User Detection.
Enhancing Vulnerable Road User Safety at Signalized Intersections Through Cooperative Perception and Driving Automation: Final Report
Summary Information
The fatality rate for Vulnerable Road Users (VRUs) at urban signalized intersections unfortunately continues to rise, highlighting the critical need to enhance VRU safety through emerging technologies such as cooperative driving automation and Cooperative Perception (CP). This study developed a CP VRU safety application for signalized intersections where information about the infrastructure detected VRUs is sent out to vehicles within communication range to increase their situational awareness. This system, which focuses on Data Fusion (DF) and communication capabilities for both infrastructure and vehicles, was evaluated in a traffic simulation.
METHODOLOGY
Four scenarios were tested to assess how a vehicle reacts when either turning left or traveling straight through an intersection, with and without CP. The scenarios were:
• Scenario 1: Left turn without CP
• Scenario 2: Straight-through without CP
• Scenario 3: Left turn with CP
• Scenario 4: Straight-through with CP
In each scenario, a Cooperative Automated Driving System (C-ADS)-equipped vehicle begins from a designated start point and proceeds through the intersection. Meanwhile, a pedestrian enters the crosswalk once the vehicle is within a specific distance from the intersection, creating a potential crash risk.
FINDING
- Simulated results showed that the CP VRU safety application could prevent 98 percent of VRU crashes compared to the base scenario.
