Live Test
Mcity 2.0 will enable U.S.-based researchers to send their test algorithms and programs to Mcity, plug them into the test facility’s Mcity OS operating system, request specific conditions for testing and participate remotely as those parameters play out in Mcity’s combined real/virtual setting.
An example of the kind of AV testing that could be conducted using this approach is the Mcity Safety Assessment Program outlined below, which was developed by Mcity and the Center for Connected and Automated Transportation (CCAT), a regional transportation research center funded by the U.S. Department of Transportation. CCAT is based at U-M.
Mcity Safety Assessment Program
- Scenario-based behavior competency test: Mcity ABC Test
- Driving environment-based safety performance test: CCAT SAFE TEST
Both Mcity ABC Test and CCAT SAFE TEST are developed based on the large-scale naturalistic driving data collected by UMTRI and Mcity.
Mcity ABC Test
The Mcity ABC Test consists of three parts: Accelerated evaluation, Behavior competence, and Corner cases. Taken together, they are random, valid, fair, and comprehensive.
Read the Mcity ABC Test white paper here
CCAT SAFE TEST
The Safe AI Framework for Trustworthy Edge Scenario Tests, or SAFE TEST, combines two pieces of technology: the Augmented Reality Test Environment and the Naturalistic and Adversarial Driving Environment (NADE). The background vehicles and other traffic participants in the NADE are trained to execute adversarial maneuvers at selected moments to maximize testing efficiency while ensuring testing accuracy.
Read the CCAT NADE paper here