Chang'an University

Xia Wu

Xiangmo Zhao

Siyuan Gong

Kang Sun

Yukun Ding

Zhen Wang

Zhigang Xu

Runmin Wang

Yu Zhu

Pengpeng Sun

Zhanwen Liu

Haigen Min

Licheng Zhang

Yalong Wu

Fenglin Liu

Yukun Fang

Wuqi Wang

Chaoyi Cheng

Yuande Jiang

Yihan Sun

Ze Li

Guohui Zheng

Wenjing Wang

Congli Zhang

Jialin Liu

Jiakai Yin

Hongda Chang

Xinyi Li

Xinxin Wang

Lu Zeng

Lu Zhao

Yuqian Ma

A smooth cooperative lane change control method for multi-connected and autonomous vehicles (CAVs), including: acquiring vehicle information of a lane-changing vehicle M and four surrounding vehicles A, B, C and D; constructing uncontrolled-vehicle and controlled-vehicle motion state prediction models; according to the motion state prediction models, predicting motion states of the lane-changing vehicle M, and the vehicles A, B, C and D; constructing an upper-layer optimization model to calculate an optimal control value and an optimized motion state of the lane-changing vehicle M and an optimal control value of the vehicle A; constructing a lower-layer optimization model to calculate an optimal control value of the vehicle D; and controlling the lane-changing vehicle M, the vehicle A and the vehicle D to run according to a corresponding optimal control value.

Smooth cooperative lane change control method for multi-connected and autonomous vehicle (CAV)

A model of a system of an intelligent vehicle is trained and optimized using system operation data of the intelligent vehicle in a normal running state. The system operation data of the intelligent vehicle in a running state is collected in real time. Sensor data of the system operation data is de-noised, and feature extraction and screening are performed for a fatal sensor fault to reconstruct the system operation data. The reconstructed system operation data is inputted into the trained model to output system state data of the intelligent vehicle in the running state. The system state data is compared with a set threshold. If the system state data exceeds the set threshold, an actuator corresponding to the system state data is determined to have a fault. In addition, a system for a fault diagnosis of the intelligent vehicle is further provided.

Method and system for fault diagnoses of intelligent vehicles

Provided is a method for berth allocation of a multiline bus station and speed guidance of buses. The intelligent control center sorts all buses between the current station and the previous station to form the BusNumber table, and performs speed guidance for all buses in the BusNumber table. Whether the guided bus can pass through the signalized intersection is determined. The buses that cannot pass through the signalized intersection are guided by acceleration, and those that cannot pass through the signalized intersection by acceleration guidance method update their speeds based on the phase state of signal light, so as to pass through the signalized intersection without stopping in most cases. All buses are conducted through speed guidance methods, and the berths are allocated for all the buses under different conditions.

Method for berth allocation of a multiline bus station and speed guidance of buses

Growing community of inventors

Shaanxi, China

Xia Wu