A Non-Contact Overload Identification Method Based on Vehicle Dynamics Wuhan University of Technology

Format:

Book

Conference/Event

Author/Creator:

Zhou, Zhou, author.

Contributor:

Ding, Yiran

Ma, Xiaofei

Tan, Gangfeng

Wang, Shuai

Wang, Zhenyu

Yu, Shimin

Conference Name:

WCX SAE World Congress Experience (2019-04-09 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2019

Summary:

AbstractThe vehicle overload seriously jeopardizes traffic safety and affects traffic efficiency. At present, the static weighing station and weigh-in-motion station are both relatively fixed, so the detection efficiency is not high and the traffic efficiency is affected; the on-board dynamic weighing equipment is difficult to be popularized because of the problem of being deliberately damaged or not accepted by the purchaser. This paper proposes an efficient, accurate, non-contact vehicle overload identification method which can keep the road unimpeded. The method can detect the vehicle overload by the relative distance (as the characteristic distance) between the dynamic vehicle's marking line and the road surface. First, the dynamics model of the vehicle suspension is set up. Then, the dynamic characteristic distance of the traffic vehicle is detected from the image acquired by the calibrated camera based on computer vision and image recognition technology. The data error caused by the vehicle vibration can be reduced by the filter set up in this paper. Finally, the actual axle load of the vehicle can be obtained combined with the established model, which can be compared with the recorded standard data to detect overload vehicles. In this paper, the real vehicle test was carried out with Dong Feng Aeolus S30. The results show that the characteristic distance identification absolute error and relative error can respectively be controlled within 42.2mm and 3.18%, and the vehicle load identification precision can be 96.0%. The method above can effectively improve the efficiency of the overload identification and has certain guiding significance for maintaining the safety of intelligent transportation

Notes:

Vendor supplied data

Publisher Number:

2019-01-0490

Access Restriction:

Restricted for use by site license