Safety

A new method and its application are presented for the evaluation of CMS safety benefits. We focus on an automatic CMS applying a 1g braking deceleration at TTC=0.5s, during 0.5s. We assume three different CMS sensor detection capabilities that generate then three CMS types. A global model of the CMS  is  assembled,  from  technical  specification  to  market  penetration,  with  appropriate  hypotheses. This model enables a discussion of safety benefits and a sensitivity analysis with respect to the main parameters, as well as of error causes and margins.

LCPC-LIVIC

LEMCO-INRETS

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

The Georgia Department of Transportation (GDOT) is currently using cell phone probe data to
provide real time speed information along the SR 400 corridor.  Preliminary results from this
system are promising, showing that there is no significant difference between the measured
speeds obtained from traditional point detector data and cellular probe data between 20 mph
and 70 mph.  This deployment has great potential for expansion in terms of both geographical
scope and applications, including those related to highway safety.  Traditionally, highway
safety issues are constrained by the limitations of crash data, timeliness of that data, and the
subjective nature of data on crash reports.  Traffic probe data has the potential to provide a
valuable new and robust data source for various highway safety applications including more
nuanced Vehicle Miles Traveled (VMT) estimates on corridors, speed detection, and evaluation
metrics.

The Georgia Department of Transportation (GDOT) is currently using cell phone probe data to
provide real time speed information along the SR 400 corridor.  Preliminary results from this
system are promising, showing that there is no significant difference between the measured
speeds obtained from traditional point detector data and cellular probe data between 20 mph
and 70 mph.  This deployment has great potential for expansion in terms of both geographical
scope and applications, including those related to highway safety.  Traditionally, highway
safety issues are constrained by the limitations of crash data, timeliness of that data, and the
subjective nature of data on crash reports.  Traffic probe data has the potential to provide a
valuable new and robust data source for various highway safety applications including more
nuanced Vehicle Miles Traveled (VMT) estimates on corridors, speed detection, and evaluation
metrics.

Governor’s Office of Highway Safety

Georgia Department of Transportation

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

Middleware has emerged as an important architectural component in supporting distributed
applications. The role of middleware is to present a unified programming model to
application writers and to mask out problems of heterogeneity and distribution. This paper is
motivated by the convergence of the embedded sensor and mobile communication
revolutions in the automobile. The national vehicle fleet is morphing into a vast mobile
sensor fleet. In this paper, we provide a middleware architecture and implementation that
addresses the needs of a distributed system of mobile sensors comprised of vehicles and
intersections producing traffic related data for traffic safety and operations. We conclude our
paper with some performance measures that relate to the cost of overhead incurred from
using the middleware. The measurements show the middleware is efficient enough for certain
road safety and congestion relief applications.

University of California, Berkeley

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

We describe a computational architecture of a collision early warning system for ve-
hicles and other principals. Early warnings allow drivers to make good judgments and
to avoid emergency stopping or dangerous maneuvering. With many principals (vehicles,
pedestrians, bicyclists, etc) coexisting in a dense intersection, it is difficult to predict even
a few seconds in advance, since there are an enormous number of possible scenarios. It is a
major challenge to manage computational resources and human resources so that only the
more plausible collisions are tracked and of those, only the most critical collisions prompt
warnings to drivers. In this paper, we propose a two-stage collision risk assessment process,
including (1) a preliminary assessment via simple efficient geometric computations which
throughly considers surrounding principals and identifies likely potential accidents, and (2)
a specialized assessment which computes more accurate collision probabilities via sophis-
ticated statistical inference. The whole process delivers an expected utility assessment to
available user-interfaces, allowing the user interfaces make discriminating choices of when
to warn drivers or other principals.

Palo Alto Research Center

Fujitsu Limited

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