Intersection crashes are among the most frequent and lethal crash modes in the United States. Intersection Advanced Driver Assistance Systems (I-ADAS) are an emerging active safety technology which aims to help drivers safely navigate through intersections. One primary function of I-ADAS is to detect oncoming vehicles and in the event of an imminent collision can (a) alert the driver and/or (b) autonomously evade the crash. Another function of I-ADAS may be to detect and prevent imminent traffic signal violations (i.e. running a red light or stop sign) earlier in the intersection approach, while the driver still has time to yield for the traffic control device.

This dissertation evaluated the capacity of I-ADAS to prevent U.S. intersection crashes and mitigate associated injuries. I-ADAS was estimated to have the potential to prevent up to 64% of crashes and 79% of vehicles with a seriously injured driver. However, I-ADAS effectiveness was found to be highly dependent on driver behavior, system design, and intersection/roadway characteristics. To generate this result, several studies were performed. First, driver behavior at intersections was examined, including typical, non-crash intersection approach and traversal patterns, the acceleration patterns of drivers prior to real-world crashes, and the frequency, timing, and magnitude of any crash avoidance actions. Second, two large simulation case sets of intersection crashes were generated from U.S. national crash databases. Third, the developed simulation case sets were used to examine I-ADAS performance in real-world crash scenarios. This included examining the capacity of a stop sign violation detection algorithm, investigating the sensor detection needs of I-ADAS technology, and quantifying the proportion of crashes and seriously injuries that are potentially preventable by this crash avoidance technology.