The operational safety of automated driving system (ADS)-equipped vehicles (AVs) needs to be quantified for an understanding of risk, requiring the measurement of parameters as they relate to AVs and human driven vehicles alike. In prior work by the Institute of Automated Mobility (IAM), operational safety metrics were introduced as part of an operational safety assessment (OSA) methodology that provide quantification of behavioral safety of AVs and human-driven vehicles as they interact with each other and other road users. To calculate OSA metrics, the data capture system must accurately and precisely determine position, velocity, acceleration, and geometrical relationships between various safety-critical traffic participants. The design of an infrastructure-based system that is intended to capture the data required for calculation of OSA metrics is addressed in this paper. The designed multi-modal sensor system includes a combination of traffic video cameras, vehicle-to-infrastructure (V2I) roadside units (RSUs), National Transportation Communications for Intelligent Transportation System Protocol (NTCIP)-compliant signal controllers streaming Signal Phase and Timing (SPAT) data, and Light Detection and Ranging (LIDAR) sensors. The system is contrasted with other design options to evaluate trade-offs between capability and cost. The designed data capture system was deployed at a SMARTDrive ProgramSM Test Bed intersection in Anthem, AZ that has been developed by the University of Arizona Transportation Research Institute (TRI) in cooperation with the Maricopa County Department of Transportation (MCDOT). The intersection is equipped with a sensor system that includes a fiber optic data transfer backbone to support the data transfer to a server at the MCDOT Traffic Management Center. A measurement uncertainty (MU) analysis has been conducted using experimental data to better understand the performance and reliability of the proposed sensor system design. The data capture system will enable the development and validation of a methodology to continuously measure OSA metrics by gaining rich information through fusion of multimodal data collected from available sources for safety assessment of the transportation system that includes AVs.
- Data capture
- Traffic safety assessment
ASJC Scopus subject areas
- Automotive Engineering
- Safety, Risk, Reliability and Quality
- Industrial and Manufacturing Engineering