Normal and Hard Braking Behavior at Stop Signs and Traffic Signals

2005 ◽  
Vol 49 (22) ◽  
pp. 1897-1901 ◽  
Author(s):  
Suzanne E. Lee ◽  
Sarah B. Brown ◽  
Miguel A. Perez ◽  
Zachary R. Doerzaph ◽  
Vicki L. Neale
Keyword(s):  
Collision Avoidance ◽  
Warning System ◽  
Traffic Signals ◽  
Performance Tests ◽  
Algorithm Development ◽  
Intersection Problem ◽  
Collision Warning ◽  
And Performance ◽  
Forward Collision Warning

A testbed intersection violation warning system was developed to address the problem of intersection crashes. The effectiveness of such systems is fundamentally dependent on the driver-braking model used to decide if a warning should be issued to the driver. If the model is unrealistic, drivers can either be annoyed due to assumed braking levels that are too low, or can be warned too late if braking expectations are too high. Initial algorithm development relied on data from the Collision Avoidance Metrics Partnership (CAMP) Forward Collision Warning (FCW) project. However, it was unknown whether the CAMP data (collected in the presence of stopped lead vehicles) would be applicable to the intersection problem (e.g., will drivers respond similarly to red traffic signals and stopped lead vehicles). Braking profile and performance tests were thus conducted to determine the applicability of the CAMP FCW results to the intersection violation warning.

Benefit Evaluation of Crash Avoidance Systems

1998 ◽  
Vol 1621 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Datta N. Godbole ◽  
Raja Sengupta ◽  
James Misener ◽  
Natasha Kourjanskaia ◽  
James B. Michael
Keyword(s):  
Collision Avoidance ◽  
Large Scale ◽  
Warning System ◽  
Operating Conditions ◽  
Real World Data ◽  
Collision Warning ◽  
General Terms ◽  
Crash Avoidance ◽  
Forward Collision Warning ◽  
Hierarchical Assessment

A five-layer hierarchy to integrate models, data, and tools is proposed for benefits assessment and requirements development for crash avoidance systems. The framework is known as HARTCAS: Hierarchical Assessment and Requirements Tools for Crash Avoidance Systems. The analysis problem is multifaceted and large-scale. The driving environment is diverse and uncertain, driver behavior and performance are not uniform, and the range of applicable collision avoidance technologies is wide. Considerable real-world data are becoming available on certain aspects of this environment, although the collection of experimental data on other aspects is constrained by technological and institutional issues. Therefore, analyses of collision avoidance systems are to be conducted by collecting data on nominal operating conditions to the greatest extent possible and by using such data to build models for analysis of the rare, abnormal conditions. HARTCAS provides a framework within which to structure the collection and use of such knowledge. It is described in general terms, and its use is illustrated by analysis of a forward collision warning system. How to quantify the relationships between the effectiveness of a warning and the probability that the warning is a nuisance is shown. System benefits are also quantified.

A forward collision warning system based on self-learning algorithm of driver characteristics

2020 ◽  
Vol 38 (2) ◽  
pp. 1519-1530 ◽  
Author(s):  
Chang Wang ◽  
Qinyu Sun ◽  
Zhen Li ◽  
Hongjia Zhang ◽  
Rui Fu
Keyword(s):  
Learning Algorithm ◽  
Warning System ◽  
Collision Warning ◽  
Forward Collision Warning ◽  
Self Learning

scholarly journals Empirical Analysis of Safe Distance Calculation by the Stereoscopic Capturing and Processing of Images Through the Tailigator System

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 5044
Author(s):  
Gerd Christian Krizek ◽  
Rene Hausleitner ◽  
Laura Böhme ◽  
Cristina Olaverri-Monreal
Keyword(s):  
Low Cost ◽  
Field Tests ◽  
Warning System ◽  
Collision Warning ◽  
The Road ◽  
Cost System ◽  
Straight Line ◽  
Real Value ◽  
On The Road ◽  
Forward Collision Warning

Driver disregard for the minimum safety distance increases the probability of rear-end collisions. In order to contribute to active safety on the road, we propose in this work a low-cost Forward Collision Warning system that captures and processes images. Using cameras located in the rear section of a leading vehicle, this system serves the purpose of discouraging tailgating behavior from the vehicle driving behind. We perform in this paper the pertinent field tests to assess system performance, focusing on the calculated distance from the processing of images and the error margins in a straight line, as well as in a curve. Based on the evaluation results, the current version of the Tailigator can be used at speeds up to 50 km per hour without any restrictions. The measurements showed similar characteristics both on the straight line and in the curve. At close distances, between 3 and 5 m, the values deviated from the real value. At average distances, around 10 to 15 m, the Tailigator achieved the best results. From distances higher than 20 m, the deviations increased steadily with the distance. We contribute to the state of the art with an innovative low-cost system to identify tailgating behavior and raise awareness, which works independently of the rear vehicle’s communication capabilities or equipment.

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