Safety

Minnesota has supported rural intersection research regarding crashes and their causality for
the past several years. The Minnesota Department of Transportation initiated research
through the University of Minnesota and CH2MHill to identify the most prevalent type and
most likely causes of crashes at rural intersections. Right angle crashes at rural thru-stop
intersections were identified as most common, and the greatest cause of these right angle
crashes was failure of drivers to recognize unsafe gaps in the traffic stream they were hoping
to enter or cross. As additional data has been collected in Minnesota and eight other states,
the methodology for identifying problem intersections, collecting macroscopic driver gap
rejection behavior, and conclusions about gap acceptance and rejection have been validated
and support the need for Cooperative Intersection Collision Avoidance Systems.

Minnesota Department of Transportation

University of Minnesota – ITS Institute

Presented at the ITS America Annual Conference and Exposition, November 16-20, 2008, New York, New York

This paper discusses the design and development process for an Overheight Vehicle
Detection System (OVDS) intended for deployment at a busy interchange in central Virginia.
The primary goal of the system was to reduce the likelihood of a collision of an overheight
vehicle with the supporting girders of the overpass. This particular structure had suffered a
number of collisions, resulting in so many repairs that structures and bridge staff had
concluded that the structure could not be repaired again and would have to be replaced if
severely damaged again. The subject overpass carries a high volume of traffic on I-95 and
replacement would result in severe disruption to traffic operations and costs estimated in the
tens of millions of dollars. Getting a system in place rapidly was a critical requirement for
the Virginia Department of Transportation (VDOT).

Iteris, Inc.

Presented at the ITS America Annual Conference and Exposition, November 16-20, 2008, New York, New York

We are developing a traffic simulator called STREET which is intended to enable us to evaluate
safety systems. To evaluate safety systems, however, we have to be able to faithfully reproduce
accidents that are caused by interactive events between vehicles and pedestrians. To this end, it
is necessary to construct a driver model that incorporates cognition, decision-making, and operation
behaviors. In this paper, we focus on a driver’s visual behavior, which is the first phase
of the cognition process, with the goal of solving some assignments in STREET. We propose
a novel behavior model by employing a model-based approach. First, we construct a hypothetical
model whereby the driver estimates the risks in his/her surroundings through a recording
process. Second, we measure the unknown parameters within the model by way of experiment.
Finally, we confirm that the proposed model allows STREET to faithfully reproduce the driver’s
visual behaviors.

Toyota Central R&D Labs., Inc. Vehicle Safety Research Center

Presented at the ITS America Annual Conference and Exposition, November 16-20, 2008, New York, New York

We are developing a traffic simulator called STREET, which is intended to enable us to
evaluate safety systems. In this paper, we propose a new structure for the pedestrian model used
by STREET, which will give us the means to evaluate vehicle-pedestrian accidents and safety
systems. To simulate the sequence of a pedestrian’s behavior leading up to an accident, we
assume that they determine their next behavior from their cognition of objects such as vehicles.
Accidents occur as a result of pedestrians engaging in behaviors unrelated to walking such as,
in our model, using a mobile phone and generally being inattentive. The model also
incorporates behaviors resulting from pedestrian-unique accident factors (such as a “priority
sense”) which were identified by analyzing actual accident data. Finally, we confirm that
STREET not only lets us evaluate how safety systems reduce the occurrence of accidents, but
also analyze the factors contributing to accidents and the effectiveness of some safety systems.

TOYOTA CENTRAL R&D LABS., INC.

Presented at the ITS America Annual Conference and Exposition, November 16-20, 2008, New York, New York

Forward collision warning systems target a major crash type : rear-end crashes with a moving
or parked vehicle which covers 30% of all accidents in Japan. The development of this system
contributes to the reduction of road accidents. Many collision warning algorithms have
recently been proposed and developed in automobile markets but they are based on Pre-Crash
Safety system concept which activates in critical driving situation. In future, the collision
warning device should be designed to be activated earlier than the current pre-crash safety
system in order to induce driver’s own braking maneuver to prevent collision as well as near-
miss incident. This paper focuses on a forward collision warning algorithm based on road
friction coefficient and driver characteristics. The effectiveness of the algorithm is verified by
using driving simulator experiments and experimental car in urban area.

Dept. of Mechanical Systems Engineering, Tokyo Univ.of Agriculture and Technology

Presented at the ITS America Annual Conference and Exposition, November 16-20, 2008, New York, New York