Modeling Commercial Vehicle Drivers’ Acceptance of Forward Collision Warning System

Author(s):  
Yueru Xu ◽  
Zhirui Ye ◽  
Chao Wang ◽  
Kun Gao
Author(s):  
D. V. Zeziulin ◽  
D. Y. Tyugin ◽  
A. V. Tumasov ◽  
A. M. Groshev ◽  
D. M. Porubov

In Russia, 36% [1] of all accidents are a collision with a pedestrian. Vehicle structural elements limit visibility to drivers and do not allow fully assessing the traffic situation, forming blind zones. It especially applies to commercial vehicles with large dimensions. The system allowing to expand visibility of the car due to use of displays in windscreen pillars is presented in this article. It is proposed to use the pedestrian recognition system integrated into the windscreen pillars based on neural networks. The system allows to detect and estimate distance to the objects and to give warning signals about possible collision. It consists of an on-board computer, video cameras and output devices. Interior elements of a commercial vehicle were developed and installed. External side mirrors were replaced with video cameras. The exterior elements were designed and installed. Further there were carried out experimental researches of the system.


2020 ◽  
Vol 38 (2) ◽  
pp. 1519-1530 ◽  
Author(s):  
Chang Wang ◽  
Qinyu Sun ◽  
Zhen Li ◽  
Hongjia Zhang ◽  
Rui Fu

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 5044
Author(s):  
Gerd Christian Krizek ◽  
Rene Hausleitner ◽  
Laura Böhme ◽  
Cristina Olaverri-Monreal

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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