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Non-Contact Overload Vehicle Recognition by Vehicle to Roadside Unit
Technical Paper
2020-01-0940
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Overloading of vehicles will affect the safety of driving, aggravate the wear of automobile brake system and make the braking distance of overloaded vehicles too long. Secondly, when the vehicle is overloaded, due to the increase of load, the stress will exceed the allowable value, which will cause early wear and damage of the parts, resulting in a decrease in the reliability of the service life of the vehicle. At present, for the detection of overloaded vehicles, most areas still adopt the method of static overload detection station or adopt the monitoring mode of traditional manual watching by installing video monitoring system. It is not efficient at all. To solve these problems, a overload vehicle detection system based on V2R (Vehicle to Roadside Unit) is developed. The system collects engine speed, engine torque and other signals through the CAN (Controller Area Network), and uses DSRC (Dedicated Short Range Communications) to directly transmit the vehicle data information to the roadside detection unit. As the speed signal collected by CAN is detected by wheel speed sensor, and calculated by algorithm. In order to improve the accuracy of the system, the system obtains the instantaneous speed of the car through the roadside video detection equipment. According to the principle of automobile dynamics, the force under the driving state of the vehicle is analyzed, the mathematical model of the vehicle is established, and the load algorithm is designed to calculate the actual weight of the vehicle. In this way, the vehicle can be detected whether it overload. According to the research, the accuracy rate of the system is over 85%. Compared with conventional overload measurement, the system is of great significance to improve the detection efficiency of overloaded vehicles.
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linpeng, C., "Non-Contact Overload Vehicle Recognition by Vehicle to Roadside Unit," SAE Technical Paper 2020-01-0940, 2020, https://doi.org/10.4271/2020-01-0940.Data Sets - Support Documents
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References
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