Relative to other roadway segments, intersections occupy a small portion of the overall infrastructure; however, they represent the location for nearly 41 % of the annual automotive crashes in the United States. Thus, intersections are an inherently dangerous roadway element and a prime location for vehicle conflicts. Traditional safety treatments are effective at addressing certain types of intersection safety deficiencies; however, cumulative traffic data suggests these treatments do not address a large portion of the crashes that occur each year.
Intersection Collision Avoidance Systems (ICAS) represent a new breed of countermeasures that focus on the types of crashes that have not been reduced with the application of traditional methods. Incursion systems, a subset of ICAS, are designed to specifically undertake crashes that are a result of the violation of a traffic control device. Intersection Collision Avoidance Systems to address Violations (ICAS-V) monitor traffic as it approaches the intersection through a network of in-vehicle sensors, infrastructure- mounted sensors, and communication equipment. A threat-assessment algorithm performs computations to predict the driver's intended intersection maneuver, based on these sensor inputs. If the system predicts a violation, it delivers a timely warning to the driver with the aim of compelling the driver to stop. This warning helps the driver to avoid a potential crash with adjacent traffic.
The following dissertation describes an investigation of intersection approach behavior aimed at developing a threat assessment algorithm for stop-sign intersections. Data were collected at live intersections to gather infrastructure-based naturalistic vehicle approach trajectories. This data were compiled and analyzed with the goal of understanding how drivers approach intersections under various speeds and environmental conditions. Six stop-controlled intersection approaches across five intersections in the New River Valley, Virginia area were selected as the test sites. Data were collected from each site for at least two months, resulting in over sixteen total months of data.
A series of statistical analysis techniques were applied to construct a set of threat assessment algorithms for stop-controlled intersections. These analyses identified characteristics of intersection approaches that suggested driver intent at the stop sign. Models were constructed to predict driver stopping intent based on measured vehicle kinematics. These models were thoroughly tested using simulation and evaluated with signal detection theory. The overall output of this work is a set of algorithms that may be integrated into an ICAS-V for on-road testing. / Ph. D.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/30013 |
| Date | 11 February 2008 |
| Creators | Doerzaph, Zachary R. |
| Contributors | Industrial and Systems Engineering, Dingus, Thomas A., Neale, Vicki L., Nussbaum, Maury A., Kleiner, Brian M., Smith-Jackson, Tonya L. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Dissertation |
| Format | application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
| Relation | Doerzaph_Dissertation.pdf |
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