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\"This book contains our research advances in the past decade in the analysis and synthesis of CAVs systems from all aspects of trajectory planning, cooperative control and communication. The focuses of this book are on the development of mathematical models and methodologies for trajectory optimization and tracking control, communications conflict resolution, cooperative control subject to communication constraints and sensor/actuator failures/faults for CAVs from different perspectives. This book is composed of fourteen Chapters. The contents are divided into three parts, with Chapter 1 - Chapter 5 as Part I, Chapter 6 - Chapter 9 as Part II, and Chapter 10 - Chapter 14 as Part III, respectively, concerned with cooperative vehicular communication and control, performance guarantee under actuator limitations, and speed trajectory planning and tracking control of CAVs.\"-- Provided by publisher.

Path planning is one of the key technologies for unmanned driving of underground intelligent vehicles. Due to the complexity of the drift environment and the vehicle structure, some improvements should be made to adapt to underground mining conditions. This paper proposes a path planning method based on an improved RRT* (Rapidly-Exploring Random Tree Star) algorithm for solving the problem of path planning for underground intelligent vehicles based on articulated structure and drift environment conditions. The kinematics of underground intelligent vehicles are realized by vectorized map and dynamic constraints. The RRT* algorithm is selected for improvement, including dynamic step size, steering angle constraints, and optimal tree reconnection. The simulation case study proves the effectiveness of the algorithm, with a lower path length, lower node count, and 100% steering angle efficiency.

With the development and progress of information technology, especially V2X technology, the research focus of intelligent vehicles gradually shifted from single-vehicle control to multi-vehicle control, and the cooperative control system of intelligent connected vehicles became an important topic of development. In order to track the research progress of intelligent connected vehicle cooperative driving systems in recent years, this paper discusses the current research of intelligent connected vehicle cooperative driving systems with vehicles, infrastructure, and test sites, and analyzes the current development status, development trend, and development limitations of each object. Based on the analysis results of relevant references of the cooperative control algorithm, this paper expounds on vehicle collaborative queue control, vehicle collaborative decision making, and vehicle collaborative positioning. In the case of taking the infrastructure as the object, this paper expounds the communication security, communication delay, and communication optimization algorithm of the vehicle terminal and the road terminal of intelligent connected vehicles. In the case of taking the test site as the object, this paper expounds the development process and research status of the real vehicle road test platform, virtual test platform, test method, and evaluation mechanism, and analyzes the problems existing in the intelligent connected vehicle test environment. Finally, the future development trend and limitations of intelligent networked vehicle collaborative control system are discussed. This paper summarizes the intelligent connected car collaborative control system, and puts forward the next problems to be solved and the direction of further exploration. The research results can provide a reference for the cooperative driving of intelligent vehicles.

Aiming at the problem of vehicle model tracking error and overdependence in traditional path planning of intelligent driving vehicles, a path planning method of intelligent driving vehicles based on deep reinforcement learning is proposed. Firstly, the abstract model of real environment is extracted. The model uses deep reinforcement learning end-to-end strategy (DRL-ETE) and vehicle dynamics model to train the reinforcement learning model which approaches the optimal intelligent driving. Secondly, the real scene problem is transferred to the virtual abstract model through the model transfer strategy, and the control and trajectory sequences are calculated according to the trained deep reinforcement learning model in the environment. Finally, the optimal trajectory sequence is selected according to the evaluation function in real environment. Because the storage mode of experience playback mechanism of Deep Q-Network algorithm is FIFO, and the sampling mode of later playback training is average sampling, the efficiency of experience playback is low. These two problems lead to the slow process of intelligent driving vehicle to target and route finding. And because of greedy strategy, the information of exploration map is incomplete, and IDQNPER algorithm model is proposed. When storing samples, the samples are given weight, and sent to the network in priority order for sample training. Meanwhile, the importance data sequence is retained in the experience playback cache area, and the sequence with high similarity is removed. The total reward value is about 10% higher than the reward value of original Deep Q-network, which proves that the accuracy of intelligent driving vehicles tends to target points is higher. In order to further realize the autonomous decision-making of intelligent driving vehicles and solve the problem of relying too much on map information in the traditional human planning framework, an end-to-end path planning method is proposed based on the depth reinforcement learning theory, which maps the action instructions directly from the sensor information and then issues them to the intelligent driving vehicles. Firstly, CNN and LSTM are used to process radar and camera information. By comparing the advantages of DQ, Double DQN, Dueling DQN and PER algorithm, IDQNPER algorithm is used to train the automatic path planning of intelligent driving vehicles. Finally, the simulation and verification experiments are carried out in the static obstacle environment. The test results show that IDQNPER algorithm is adaptable to intelligent vehicles in different environments. The method can deal with the continuous input state and generate the continuous control sequence of the corner control, which can reduce the lateral tracking error. At the same time, the generalization performance of the model can be improved and the overdependence problem can be reduced by experience playback.

Autonomous driving is becoming a pivotal technology that can realize intelligent transportation and revolutionize the future of mobility. Various types of sensors, including perception sensors and localization sensors, are essential for high-level autonomous and intelligent vehicles (AIV). In this paper, the characteristics of different sensors are compared, and the application characteristics and requirements of AIV are analyzed in depth. These analyses indicate that: GNSS, as the unique localization sensor that can obtain an absolute position, can not only provide all-weather position and time information for internal multi-sensor fusion but also act as a standard spatiotemporal reference for all autonomous systems; Furthermore, AIVs aim to provide safety for a mass user base ranging from tens to hundreds of millions; for this, AIVs require a global wide-area and instantaneous precise positioning service with location privacy protection. Based on a “geometry-bound” description of road grade and vehicle size, it has been found that GNSS requirements in autonomous vehicles include decimeter-level positioning with the assurance of high integrity. Combined with high-integrity GNSS implementation in the civil aviation field, GNSS different technology routes, and commercial solutions, a state space representation (SSR)-based GNSS high-precision augmentation positioning solution for AIV is summarized and introduced. The solution can achieve instantaneous, precise positioning with high integrity in a wide area by utilizing passive positioning mode with location privacy protection. In addition, the research progress on key technologies in the solution and existing challenges is investigated in detail by reviewing a series of publications.

Vision-based techniques for intelligent vehicles in heterogeneous road environments are gaining significant attention from researchers and industrialists. Unfortunately, the mechanisms in this domain suffer from limited performance due to scene complexity, varying road structure, and improper illumination conditions. These challenging situations may lead an intelligent vehicle into dangerous situations such as collisions or road accidents and may cause higher mortality. The application of intelligent methods and other machine learning techniques for road surface classification is little explored in the existing literature. Thus, we propose a convolutional neural network-based road classification network (RCNet) for the accurate classification of road surfaces. This procedure includes the classification of five major categories of road surfaces: curvy, dry, ice, rough, and wet roads. The experimental results reveal the behavior of the proposed RCNet under various optimizer techniques. The standard performance evaluation measures have been used to test and validate the proposed method on the Oxford RobotCar dataset. RCNet achieves classification accuracy, precision, and sensitivity of 99.90%, and 99.97% of specificity. Results of implemented work are significantly higher than available state-of-the-art techniques and show accurate and effective performance in the complex road environment.

In order to research and develop flight system technologies for next-generation urban air mobility (UAM) such as drones, as well as to foster young human resources in related fields, the Center for Aerial Intelligent Vehicle (CAIV) was established on October 1, 2019 (https://caiv.chiba-u.jp/index.html).

Trajectory planning is one of the key technologies for autonomous driving. A* algorithm is a classical trajectory planning algorithm that has good results in the field of robot path planning. However, there are still some practical problems to be solved when the algorithm is applied to vehicles, such as the algorithm fails to consider the vehicle contours, the planned path is not smooth, and it lacks speed planning. In order to solve these problems, this paper proposes a path processing method and a path tracking method for the A* algorithm. First, the method of configuring safe redundancy space is given considering the vehicle contour, then, the path is generated based on A* algorithm and smoothed using Bessel curve, and the speed is planned based on the curvature of the path. The trajectory tracking algorithm in this paper is based on an expert system and pure tracking theory. In terms of speed tracking, an expert system for the acceleration characteristics of the vehicle is constructed and used as a priori information for speed control, and good results are obtained. In terms of path tracking, the required steering wheel angle is calculated based on pure tracking theory, and the influence factor of speed on steering is obtained from test data, based on which the steering wheel angle is corrected and the accuracy of path tracking is improved. In addition, this paper proposes a target point selection method for the pure tracking algorithm to improve the stability of vehicle directional control. Finally, a simulation analysis of the proposed method is performed. The results show that the method can improve the applicability of the A* algorithm in automated vehicle planning.

High-precision positioning and navigation are necessary for autonomous driving. GNSS RTK and INS integrated system is commonly used in vehicular navigation. But it suffers from severe signal reflections and blockages of GNSS signals and error accumulation of INS with MEMS-IMU in GNSS-challenging environment. In addition, high-definition (HD) maps in vector format and light detection and ranging (LiDAR) are two common options for intelligent vehicles. A tightly coupled localization method with vector HD map, LiDAR, GNSS RTK, and INS is proposed to take advantage of their complementary characteristics to accurately navigate a vehicle in a GNSS-challenging environment. The method is based on a particle filter (PF) framework. Lateral positions of particles are constrained by LiDAR measurements and lane information in the vector HD map. A constrained and damped LAMBDA searching method is proposed to update particle weights, aiming to find accurate longitudinal localization. Experimental results prove that our method can maintain sub-meter level horizontal positioning accuracy in GNSS-challenging environment, with improvements of (77%, 75%, 64% and 65%), regarding to three-dimensional position and yaw, compared to traditional GNSS-RTK/INS integration, while the improvement of the popular GNSS-RTK/INS/LiDAR integrated framework LIO-SAM is (16%, 53%, 48% and 51%).

With the increasing emergence of intelligent vehicles, novel accident patterns have gradually emerged. In human–machine cooperative driving (HMCD) states, despite driving automation systems being capable of controlling lateral and longitudinal vehicle motions over extended periods, functional limitations persist in specific scenarios due to insufficient expected functionalities. When combined with risk factors, such as driver distraction, these limitations significantly elevate accident risks. This study investigated takeover safety through real vehicle testing in two typical accident scenarios: large-curvature curves and static obstacles. The key findings include the following: (1) in scenarios involving large curvature curves and static obstacles, vehicles are prone to lane departure and missed target detection, which are typical dangerous scenarios; (2) during the human–machine cooperative driving phase, the design of the driving automation system should focus on enhancing driver engagement in driving tasks, while in the autonomous driving phase, the vehicle’s early warning capabilities should be strengthened; (3) the takeover request for longitudinal control requires at least 4.12 s of driver reaction time, while the takeover request for lateral control requires at least 1.87 s. This study provides important theoretical and practical references for safety in designing assisted driving systems and the testing of hazardous scenarios.