Vehicular platoon control can effectively achieve group consensus, improve vehicular running safety and increase road capacity. However, some constraints exist in practical situations due to the limitations of traffic environment in time-varying metrics (time-delay, packet-dropout or interruption) in wireless communication systems. In this work, a distributed model predictive control (MPC) algorithm is proposed for connected vehicle platoon with a focus on switching communication topologies and control strategy under abnormal communications. Firstly, the predecessor-leader following is selected as the basic communication topology, by which the switching communication topology and the desired vehicle spacing policy are established. Secondly, the platoon control algorithm of connected vehicles is established and a set of constraints is analyzed. Thirdly, the ${\mathcal{ L}}_{2} $ -norm string stability criterion and the asymptotic stability criterion are considered within the proposed MPC. Finally, a co-simulation platform for connected vehicle platoon is developed based on Prescan/Matlab/V2X communication simulator. In addition, the platoon control algorithm is tested in three traffic scenarios including normal communication, leading vehicle with abnormal communication and following vehicle with abnormal communication. The experiments demonstrate that the communication topologies in different communication environments can be switched well in real time through the proposed platoon control algorithm. In addition, the string stability, the consistency of vehicle spacing, speed and acceleration are proven to be guaranteed simultaneously.

Turning behavior classification of oncoming vehicles is a critical step towards high level scene understanding and motion planning tasks for autonomous vehicles. In this paper, we propose a new method for classifying an oncoming vehicle's turning behavior at an intersection using a 3D LIDAR which collects rich point clouds from the surrounding environment. First, vehicle pose is measured via a novel rectangle-based multi-frame fitting method applied to the vehicle point cloud extracted by a data pre-processing procedure. Then, a Kalman filter integrates these measured vehicle poses to estimate and update vehicle kinematic parameters. Next, a hidden Markov model and Bayesian filtering are combined to recognize the vehicle's turning behavior using the heading angle and the acceleration components of the kinematic model. Real point clouds from intersection environments are used to train and validate the proposed turning behavior classification algorithm in the experiments. The results of the performance analysis and classification time measurements confirm that the proposed method can recognize the behavior of the oncoming vehicle at intersections correctly at an early stage of entering an intersection.

In order to locate the position of intelligent connected vehicles (ICVs) robustly in urban intersection scenarios, a novel compound positioning method based on the Dempster-Shafer evidence theory and the improved multi-layer unscented Kalman filter (DS-UKF) is proposed in this paper. The Euclidean distance between the heterogeneous data is used to optimize the Dempster-Shafer (D-S) evidence theory, so that the position of ICVs and the distance between ICVs and roadside positioning platform with higher confidence can be obtained. Meanwhile, considering the high complication of urban intersections, the unscented Kalman filter (UKF) algorithm is improved based on the covariance of error sequences so that the influences caused by observation noise anomalies can be reduced. To further improve the accuracy of positional prediction, the position of roadside positioning platform is extended dimensionally into the state vector of ICVs to observe the distance between ICVs and roadside positioning platform. Then the particle filter (PF) theory is introduced to integrate the upper-layer with the lower-layer UKF to reduce the positioning errors brought by signal interferences. Besides, an adaptive factor is adopted to connect D-S theory with the improved multi-layer UKF (IUKF) to dynamically update the weights of output values from sensors. Furthermore, a communication mechanism between the ICVs platform and the roadside positioning platform is established in this project with considering the reliability and low latency of vehicle to everything (V2X) communications. Finally, the feasibility and superiority of the proposed method is validated by experiments with three ICVs at an urban intersection. The experimental results have shown that the proposed method can locate the position of ICVs rapidly and stably in urban intersections and has better positioning performance compared to the current state-of-the-art algorithms.

The technology of Artificial Intelligence (AI) brings tremendous possibilities for autonomous vehicle applications. One of the essential tasks of autonomous vehicle is environment perception using machine learning algorithms. Since the cyclists are the vulnerable road users, cyclist detection and tracking are important perception sub-tasks for autonomous vehicles to avoid vehicle-cyclist collision. In this paper, a robust method for cyclist detection and tracking is presented based on multi-layer laser scanner, i.e., IBEO LUX 4L, which obtains four-layer point cloud from local environment. First, the laser points are partitioned into individual clusters using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method based on subarea. Then, 37-dimensional feature set is optimized by Relief algorithm and Principal Component Analysis (PCA) to produce two new feature sets. Support Vector Machine (SVM) and Decision Tree (DT) classifiers are further combined with three feature sets, respectively. Moreover, Multiple Hypothesis Tracking (MHT) algorithm and Kalman filter based on Current Statistical (CS) model are applied to track moving cyclists and estimate the motion state. The performance of the proposed cyclist detection and tracking method is validated in real road environment.

Moving object classification is essential for autonomous vehicle to complete high-level tasks like scene understanding and motion planning. In this paper, we propose a novel approach for classifying moving objects into four classes of interest using 3D point cloud in urban traffic environment. Unlike most existing work on object recognition which involves dense point cloud, our approach combines extensive feature extraction with the multiframe classification optimization to solve the classification task when partial occlusion occurs. First, the point cloud of moving object is segmented by a data preprocessing procedure. Then, the efficient features are selected via Gini index criterion applied to the extended feature set. Next, Bayes Decision Theory (BDT) is employed to incorporate the preliminary results from posterior probability Support Vector Machine (SVM) classifier at consecutive frames. The point cloud data acquired from our own LIDAR as well as public KITTI dataset is used to validate the proposed moving object classification method in the experiments. The results show that the proposed SVM-BDT classifier based on 18 selected features can effectively recognize the moving objects.

It is agreed that connected vehicle technologies have broad implications to traffic management systems. In order to alleviate urban congestion and improve road capacity, this paper proposes a multilane spatiotemporal trajectory optimization method (MSTTOM) to reach full potential of connected vehicles by considering vehicular safety, traffic capacity, fuel efficiency, and driver comfort. In this MSTTOM, the dynamic characteristics of connected vehicles, the vehicular state vector, the optimized objective function, and the constraints are formulated. The method for solving the trajectory problem is optimized based on Pontryagin’s maximum principle and reinforcement learning (RL). A typical scenario of intersection with a one-way 4-lane section is measured, and the data within 24 hours are collected for tests. The results demonstrate that the proposed method can optimize the traffic flow by enhancing vehicle fuel efficiency by 32% and reducing pollutants emissions by 17% compared with the advanced glidepath prototype application (GPPA) scheme.