Search Results

This paper deals with an integrated motor-transmission (IMT) speed tracking control of the connected vehicle when there are controller area network (CAN)-induced delays and denial of service (DOS)-induced delays. A connected vehicle equipped with an IMT system may be attacked through the external network. Therefore, there are two delays on the CAN of the connected vehicle, which are CAN-induced and cyber-attack delays. A DOS attack generates huge delays in CAN and even makes the control system invalid. To address this problem, a robust dynamic output-feedback controller of the IMT speed tracking system considering event-triggered detectors resisting CAN-induced delays and DOS-induced delays is designed. The event-triggered detector is used to reduce the CAN-induced network congestion with appropriate event trigger conditions on the controller input and output channels. CAN-induced delays and DOS-induced delays are modeled by polytopic inclusions using the Taylor series expansion.

As an important component of the Intelligent Transportation System (ITS), short-term traffic flow prediction is a key step to assess the traffic situation. It provides suggestions for travellers and helps the administrators manage the traffic effectively. Due to the availability of massive traffic data with various features, the data-driven methods have been applied widely to improve the accuracy of traffic flow prediction. However, few previous studies try to capture the information of traffic flows from multi entry stations to forecast the overall tendency of traffic flow. In this paper, we collect data at a highway exit station in Shanghai, split the data according to originating entry stations and predict the corresponding exit station traffic flow from that of the multi entry stations. Firstly, the original records are collected, preprocessed, aggregated and normalized.

As a basic link of driving behavior in urban roads, vehicle lane changing has a significant impact on traffic flow characteristics and traffic safety, and the automation of lane change is also a key issue to be solved in the field of intelligent driving. In this paper, the research on the automatic lane change control for intelligent vehicles is carried out. The main work is to build the overall structure of the vehicle's automatic lane change behavior, of which the planning and tracking are focused. The strategy of Constant Time Headway (CTH) is used in the lane change decision. The lane change trajectory adopts the model of constant velocity offset plus sine function, and the longitudinal displacement is determined by the vehicle speed when changing lanes. Model Predictive Control (MPC) theory is used to track the trajectory, which optimizes tracking accuracy and vehicle stability and constrains the range and rate of change of vehicle speed and steering angle.

Urban intersection is the key element to determine the traffic operation of road network. Under the CAVs environment, the roadside control equipment of intersection can communicate with CAVs in real time, collect vehicle state data and optimize traffic control schemes. This paper presents a method for intersection traffic signal control based on deep learning of CAVs data. In addition, intelligent control agent of traffic signal (ICATS) is designed to simulate CAVs. ICATS can perceive real-time changes of traffic flow, model different conditions of intersection and generate the corresponding traffic signal scheme. ICATS used double Q-learning method combination with deep neural network, which is an effective model-independent deep learning algorithm. Moreover, the real traffic data is collected and tested in this paper for evaluating the experiment performance, including vehicle delay, number of passing vehicles, total stop times and passing time.