Evaluating the Potential of an Intersection Driver Assistance System to Prevent U.S. Intersection Crashes
| dc.contributor.author | Scanlon, John Michael | en |
| dc.contributor.committeechair | Gabler, Hampton Clay | en |
| dc.contributor.committeemember | Weaver, Ashley A. | en |
| dc.contributor.committeemember | Kemper, Andrew R. | en |
| dc.contributor.committeemember | Doerzaph, Zachary R. | en |
| dc.contributor.committeemember | Rowson, Steven | en |
| dc.contributor.department | Biomedical Engineering | en |
| dc.date.accessioned | 2018-10-25T06:01:01Z | en |
| dc.date.available | 2018-10-25T06:01:01Z | en |
| dc.date.issued | 2017-05-02 | en |
| dc.description.abstract | 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. | en |
| dc.description.abstractgeneral | Intersection crashes account for over 5,000 fatalities each year in the U.S., which places them among the most lethal crash modes. Highly automated vehicles are a rapidly emerging technology, which has the potential to greatly reduce all traffic fatalities. This work evaluated the capacity of intersection advanced driver assistance systems (I-ADAS) to prevent U.S. intersection crashes and mitigate associated injuries. I-ADAS is an emerging technology used by highly automated vehicles to help drivers safely navigate intersections. This technology utilizes onboard sensors to detect oncoming vehicles. If an imminent crash is detected, I-ADAS can respond by (a) warning the driver and/or (b) autonomously braking. Another function of I-ADAS may be to prevent intersection violations altogether, such as running a red light or a stop sign. Preventing and/or mitigating crashes and injuries that occur in intersection crashes are among the highest priority for designers, evaluators, and regulatory agencies. This dissertation has three main components. The first aim of this research was to describe how individuals drive through intersections. This included examining how drivers approach, traverse, and take crash avoidance actions at intersections. The second aim was to develop a dataset of intersection crashes that could be used to examine I-ADAS effectiveness. This was completed by extracting crashes that occurred throughout the U.S., and reconstructing vehicle positions before and after impact. The third aim was to use the extracted dataset of intersection crashes, and consider a scenario where one of the vehicles had been equipped with I-ADAS. Estimates of IADAS effectiveness were then generated based on these results. | en |
| dc.description.degree | Ph. D. | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:9797 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/85505 | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Intersection Advanced Driver Assistance Systems | en |
| dc.subject | Active Safety | en |
| dc.subject | Benefits Estimates | en |
| dc.subject | Driver Behavior | en |
| dc.subject | Event Data Recorders | en |
| dc.subject | Naturalistic Driving | en |
| dc.subject | Crash | en |
| dc.subject | Injury Biomechanics | en |
| dc.title | Evaluating the Potential of an Intersection Driver Assistance System to Prevent U.S. Intersection Crashes | en |
| dc.type | Dissertation | en |
| thesis.degree.discipline | Biomedical Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | Ph. D. | en |
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