Personal Mobility

The Federal Motor Carrier Safety Administration (FMCSA) is carrying out a two-phase
project called SmartPark to demonstrate commercial technologies for conveying
information about parking availability in real-time to truck drivers on the road 24 hours
per day, seven days per week. During Phase I, two technologies (i.e., video imaging and
magnetic sensor) will be field tested for their capability to collect data to determine
whether a truck parking area is full. One of the two technologies will be continued for
Phase II, which will be demonstrating how information on parking availability can be
disseminated in real time and used in real time to divert truckers on the road from areas
that are full to areas that have space. This is the first deployment of Intelligent
Transportation Systems (ITS) for truck parking availability in the world.

Federal Motor Carrier Safety Administration

Presented at the 15th World Congress on Intelligent Transport Systems, November 16-20, 2008, New York, New York

Every year, thousands of vehicles are involved in crashes which are attributed to the onset of driver drowsiness. To address this issue, a prototype integrated system was developed that combined machine-vision based drowsy driver monitoring technology and the analysis of operator/vehicle performance parameters to reliably assess driver drowsiness. PERCLOS (a measure of eye closure) is considered to be the “gold standard” of drowsiness detection metrics. Systems have been developed to measure PERCLOS. However, issues including eyewear, ambient illumination, and head movement present hurdles which can be difficult to overcome. Research has investigated driver control metrics associated with drowsiness, and lane position appears to be a key indicator. This paper reports on a project aimed at integrating PERCLOS with other drowsiness metrics to form a new measure, PERCLOS+ (PERCLOS plus other measures), that may prove to be a more robust measure in a real-world, field application as compared to a single metric system.

Virginia Tech Transportation Institute

Federal Motor Carrier Safety Administration

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

Dynamic Traffic Assignment (DTA) is perceived as a major ITS initiative to manage congestion. DTA algorithms seek to guide “smart cars” along paths to their respective destinations so as to disperse traffic demand over a network in a manner that minimizes vehicle hours or trip travel times. DTA algorithms must be tested before deployed. This paper describes a microsimulation model designed to interface with any DTA algorithm, which simulates the operational performance of traffic comprised of a mix of “smart” cars equipped to be guided during their travel, and those not so equipped. Results obtained by the model simulation a DTA traffic environment are presented.

KLD Associates, Inc.

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

We developed the parking space detection using side-view camera image. This algorithm is
very simple and the influence of the noise is excluded as much as possible. It is difficult to
detect the parking space line using the through image of side-view camera, because this image
contains distortion of camera lens. However, we processed from this distorted image to
undistorted image using the method of image synthesis technology. There is an advantage that this technique is composed of the combination of a basic image processing. From this result, in the parking support system, the convenience when the driver sets the target parking position can be improved.

Matsushita Electric Industrial Co., Ltd.

Presented at the 15th World Congress on Intelligent Transport Systems, November 16-20, 2008, New York, New York

With the urgent demand for real-time traffic information in Intelligent Transportation System,
the high-density installation of detectors has been adopted, while the amount of investment
has been sharply increased. So optimum detector location density is necessary. In this paper,
the optimum method based on travel time estimation has been expounded. The major
conclusions could be summarized as follows: considering the appropriate estimation error for
travel time and reasonable investment, the space between detectors has a suitable bound and
the high-density installation of detectors is not always good for ITS system. The method for
selecting suitable bound discussed in the paper could be introduced to confirm detector
density and detector amount in practical applications, then according to this bound the
optimum detector location density can be achieved.

Tongji University

Presented at the 12th World Congress on Intelligent Transport Systems, November 6-10, 2005, San Francisco, California