Digital Twin Based Multi-Vehicle Cooperative Warning System on Mountain Roads Wuhan University of Technology

Format:

Book

Conference/Event

Author/Creator:

Tian, Liheng, author.

Contributor:

Chen, Xinguo

Yu, Zirui

Conference Name:

WCX SAE World Congress Experience (2024-04-16 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2024

Summary:

Compared with urban areas, the road surface in mountainous areas generally has a larger slope, larger curvature and narrower width, and the vehicle may roll over and other dangers on such a road. In the case of limited driver information, if the two cars on the mountain road approach fast, it is very likely to occur road blockage or even collision. Multi-vehicle cooperative control technology can integrate the driving data of nearby vehicles, expand the perception range of vehicles, assist driving through multi-objective optimization algorithm, and improve the driving safety and traffic system reliability. Most existing studies on cooperative control of multiple vehicles is mainly focused on urban areas with stable environment, while ignoring complex conditions in mountainous areas and the influence of driver status. In this study, a digital twin based multi-vehicle cooperative warning system was proposed to improve the safety of multiple vehicles on mountain roads. First, implement the mapping from reality to the cloud , and establish a multi-vehicle mountain road driving digital twin model based on vehicle dynamics through cloud data and local data. This model focuses on the roll and longitudinal movement of the vehicle. Then, the ground influence factor is introduced to correct the minimum headway on sloping ground. The classification model from the Support Vector Machine is used to identify and classify driver behavior patterns, and adjust the weight of each vehicle in the queue. Next, a multi-vehicle cooperative warning system is used to predict the development mode of the vehicle group, including the rollover predictor, the front predictor and the digital twin prediction model. It provides warnings for specific targets. Finally, a simulation was conducted. The results show that in a short prediction time, the LLTR error of the flip predictor is stable near the actual value within 1.6 seconds, and the prediction results of the distance predictor are consistent with the facts. The system can effectively achieve preliminary warning of the fleet and improve the safety of multi-vehicle driving on mountain roads

Notes:

Vendor supplied data

Publisher Number:

2024-01-1999

Access Restriction:

Restricted for use by site license