Safety

Traditional ways of directly measuring visual demand while driving (i.e., extracting eye glance data by manual video tape reduction) are tedious and cumbersome. This paper is a summary of the results on visual demand associated with various telematic tasks. The results are based on the data collected from an empirical study. More specifically, traditional eye glance measures such as Mean Single Glance Time, Number of Glances, Total Glance Time, and Longest Single Eye Glance are compared to other dependent measures such as Static/Dynamic Task Completion Times and the Number of Excursions committed while completing the task. Previous findings indicated that static task completion time is a viable surrogate of dynamic task completion times. Results presented herein provide supportive evidence of previous findings, in addition to the Total Glance Time being a good predictor of other surrogate measures such as lanekeeping performance and task completion times.

Ford Research Laboratory

Virginia Tech Transportation Institute

Presented at the ITS America Annual Conference and Exposition, April 29 –May 2, 2002 Long Beach, California

Emergent Collision Avoidance Systems (CAS's) are beginning to assist drivers in performing specific tasks and extending the limits of driver's perception. The systems face significant hurdles in terms of safety and driver acceptance. In fact, CAS's may actually make the driving environment more dangerous. This paper demonstrates that adaptive capability is necessary to mitigate these concerns. Specifically, CAS's must adapt to a driver's style and limitations. Fortunately, a significant portion of this adaptive capability can be realized without additional sensors and with the inclusion of relatively simple hardware. The requirements of components of a CAS are discussed based on advances found in recent relevant literature. An architecture for an adaptive CAS is proposed.

The George Washington University - GW Transportation Research Institute

Presented at the ITS America Annual Conference and Exposition, April 29 –May 2, 2002 Long Beach, California

The Collision Countermeasure System (CCS) is an ITS traffic control device, consisting of activated warning signs and pavement loop detectors. Its application at a rural unsignalized intersection was intended to enhance driver awareness of cross traffic, thus increasing safety. This three-phase vehicle behavioral field evaluation assessed novelty and longer-term CCS impacts.

Developed Measures of Effectiveness (M.O.E.s) were derived from CCS accident-avoidance operational objectives. Applied M.O.E.s were: (1) drivers’ CCS speed responses in the presence of cross traffic; (2) intersection approach speed reductions; and (3) Projected Times-to-Collision (PTCs), i.e., the elapsed time to which an approaching vehicle would collide with a vehicle in its path in the absence of a timely avoidance response. Human factors (e.g., driver perception-reaction time) accident-avoidance requirements determined critical PTC values that were utilized in the analysis.

Transportation Research Corporation

Presented at the 10th ITS Annual Conference and Exposition, May 1-4, 2000 Boston, MA

An automated warning system has been installed on Interstate Highway 75 in south Georgia at a site that is known for fog problems. The warning system, which was jointly developed by Georgia Tech and the Georgia Department of Transportation (GDOT), continuously monitors visibility and is designed to automatically provide warnings to motorists whenever fog occurs. Visibility is monitored with 19 fog sensors, while speed and headway are monitored with five sets of traffic loops for each direction of traffic. The sensor data will automatically post warnings and speed advisories on two upstream Changeable Message Signs (CMSs), and will automatically notify the GDOT Transportation Management Center (TMC) whenever a hazardous situation occurs. Telephone access for remote monitoring by other transportation officials is also provided. Beta testing has been conducted and has shown the system to perform as designed. A comprehensive evaluation of the system and its effects on driver behavior has not been performed.

Georgia Institute of Technology

Scientific Atlanta

Presented at the ITS America Annual Conference and Exposition, May 19-22, 2003 Minneapolis, Minnesota

This paper will discuss the Automatic Incident Detection (AID) system along the I-95 corridor in the Philadelphia area, the first of its kind in this region. The paper specifically focuses on data errors and error sources, and presents findings of the data analysis. The AID system is expected to greatly enhance the Pennsylvania Department of Transportation, Engineering District 6-0’s (PENNDOT 6-0’s) Traffic Control Center (TCC) day-to-day management and operations. The AID system helps to improve response to incidents by providing rapid information to the proper emergency service providers allowing for shorter incident duration, thereby quicker cleanup operation and bringing the highway back to normal operation. Automatic systems for incident detection are useful in large traffic management systems to detect incidents while operators are busy with other tasks.

Jacobs Edwards & Kelcey, Inc.

Presented at the ITS America Annual Conference and Exposition, May 19-22, 2003 Minneapolis, Minnesota