Advanced Sensing and Control for Connected and Automated Vehicles

Connected and automated vehicles (CAVs) are a transformative technology that is expected to change and improve the safety and efficiency of mobility. As the main functional components of CAVs, advanced sensing technologies and control algorithms, which gather environmental information, process data, and control vehicle motion, are of great importance. The development of novel sensing technologies for CAVs has become a hotspot in recent years. Thanks to improved sensing technologies, CAVs are able to interpret sensory information to further detect obstacles, localize their positions, navigate themselves, and interact with other surrounding vehicles in the dynamic environment. Furthermore, leveraging computer vision and other sensing methods, in-cabin humans’ body activities, facial emotions, and even mental states can also be recognized. Therefore, the aim of this Special Issue has been to gather contributions that illustrate the interest in the sensing and control of CAVs

A Systematic Survey of Control Techniques and Applications: From Autonomous Vehicles to Connected and Automated Vehicles

Vehicle control is one of the most critical challenges in autonomous vehicles (AVs) and connected and automated vehicles (CAVs), and it is paramount in vehicle safety, passenger comfort, transportation efficiency, and energy saving. This survey attempts to provide a comprehensive and thorough overview of the current state of vehicle control technology, focusing on the evolution from vehicle state estimation and trajectory tracking control in AVs at the microscopic level to collaborative control in CAVs at the macroscopic level. First, this review starts with vehicle key state estimation, specifically vehicle sideslip angle, which is the most pivotal state for vehicle trajectory control, to discuss representative approaches. Then, we present symbolic vehicle trajectory tracking control approaches for AVs. On top of that, we further review the collaborative control frameworks for CAVs and corresponding applications. Finally, this survey concludes with a discussion of future research directions and the challenges. This survey aims to provide a contextualized and in-depth look at state of the art in vehicle control for AVs and CAVs, identifying critical areas of focus and pointing out the potential areas for further exploration

Cooperative Vehicle Tracking in Large Environments

Vehicle position tracking and prediction over large areas is of significant importance in many industrial applications, such as mining operations. In a small area, this can be easily achieved by providing vehicles with a constant communication link to a control centre and having the vehicles broadcast their position. The problem changes dramatically when vehicles operate within a large environment of potentially hundreds of square kilometres and in difficult terrain. This thesis presents algorithms for cooperative tracking of vehicles based on a vehicle motion model that incorporates the properties of the working area, and information collected by infrastructure collection points and other mobile agents. The probabilistic motion prediction approach provides long-term estimates of vehicle positions using motion profiles built for the particular environment and considering the vehicle stopping probability. A limited number of data collection points distributed around the field are used to update the position estimates, with negative information also used to improve the estimation. The thesis introduces the concept of observation harvesting, a process in which peer-to-peer communication between vehicles allows egocentric position updates and inter-vehicle measurements to be relayed among vehicles and finally conveyed to the collection points for an improved position estimate. It uses a store-and-synchronise concept to deal with intermittent communication and aims to disseminate data in an opportunistic manner. A nonparametric filtering algorithm for cooperative tracking is proposed to incorporate the information harvested, including the negative, relative, and time delayed observations. An important contribution of this thesis is to enable the optimisation of fleet scheduling when full coverage networks are not available or feasible. The proposed approaches were validated with comprehensive experimental results using data collected from a large-scale mining operation

Cooperative Vehicle Tracking in Large Environments

Vehicle position tracking and prediction over large areas is of significant importance in many industrial applications, such as mining operations. In a small area, this can be easily achieved by providing vehicles with a constant communication link to a control centre and having the vehicles broadcast their position. The problem changes dramatically when vehicles operate within a large environment of potentially hundreds of square kilometres and in difficult terrain. This thesis presents algorithms for cooperative tracking of vehicles based on a vehicle motion model that incorporates the properties of the working area, and information collected by infrastructure collection points and other mobile agents. The probabilistic motion prediction approach provides long-term estimates of vehicle positions using motion profiles built for the particular environment and considering the vehicle stopping probability. A limited number of data collection points distributed around the field are used to update the position estimates, with negative information also used to improve the estimation. The thesis introduces the concept of observation harvesting, a process in which peer-to-peer communication between vehicles allows egocentric position updates and inter-vehicle measurements to be relayed among vehicles and finally conveyed to the collection points for an improved position estimate. It uses a store-and-synchronise concept to deal with intermittent communication and aims to disseminate data in an opportunistic manner. A nonparametric filtering algorithm for cooperative tracking is proposed to incorporate the information harvested, including the negative, relative, and time delayed observations. An important contribution of this thesis is to enable the optimisation of fleet scheduling when full coverage networks are not available or feasible. The proposed approaches were validated with comprehensive experimental results using data collected from a large-scale mining operation

Actuators for Intelligent Electric Vehicles

This book details the advanced actuators for IEVs and the control algorithm design. In the actuator design, the configuration four-wheel independent drive/steering electric vehicles is reviewed. An in-wheel two-speed AMT with selectable one-way clutch is designed for IEV. Considering uncertainties, the optimization design for the planetary gear train of IEV is conducted. An electric power steering system is designed for IEV. In addition, advanced control algorithms are proposed in favour of active safety improvement. A supervision mechanism is applied to the segment drift control of autonomous driving. Double super-resolution network is used to design the intelligent driving algorithm. Torque distribution control technology and four-wheel steering technology are utilized for path tracking and adaptive cruise control. To advance the control accuracy, advanced estimation algorithms are studied in this book. The tyre-road peak friction coefficient under full slip rate range is identified based on the normalized tyre model. The pressure of the electro-hydraulic brake system is estimated based on signal fusion. Besides, a multi-semantic driver behaviour recognition model of autonomous vehicles is designed using confidence fusion mechanism. Moreover, a mono-vision based lateral localization system of low-cost autonomous vehicles is proposed with deep learning curb detection. To sum up, the discussed advanced actuators, control and estimation algorithms are beneficial to the active safety improvement of IEVs

New Perspectives on Electric Vehicles

Modern transportation systems have adverse effects on the climate, emitting greenhouse gases and polluting the air. As such, new modes of non-polluting transportation, including electric vehicles and plug-in hybrids, are a major focus of current research and development. This book explores the future of transportation. It is divided into four sections: “Electric Vehicles Infrastructures,” “Architectures of the Electric Vehicles,” “Technologies of the Electric Vehicles,” and “Propulsion Systems.” The chapter authors share their research experience regarding the main barriers in electric vehicle implementation, their thoughts on electric vehicle modelling and control, and network communication challenges

Robust hybrid central/self-organising multi-agent systems in intersections without traffic lights

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Vehicle classification in intelligent transport systems: an overview, methods and software perspective

Vehicle Classification (VC) is a key element of Intelligent Transportation Systems (ITS). Diverse ranges of ITS applications like security systems, surveillance frameworks, fleet monitoring, traffic safety, and automated parking are using VC. Basically, in the current VC methods, vehicles are classified locally as a vehicle passes through a monitoring area, by fixed sensors or using a compound method. This paper presents a pervasive study on the state of the art of VC methods. We introduce a detailed VC taxonomy and explore the different kinds of traffic information that can be extracted via each method. Subsequently, traditional and cutting edge VC systems are investigated from different aspects. Specifically, strengths and shortcomings of the existing VC methods are discussed and real-time alternatives like Vehicular Ad-hoc Networks (VANETs) are investigated to convey physical as well as kinematic characteristics of the vehicles. Finally, we review a broad range of soft computing solutions involved in VC in the context of machine learning, neural networks, miscellaneous features, models and other methods

Advanced Control and Estimation Concepts, and New Hardware Topologies for Future Mobility

According to the National Research Council, the use of embedded systems throughout society could well overtake previous milestones in the information revolution. Mechatronics is the synergistic combination of electronic, mechanical engineering, controls, software and systems engineering in the design of processes and products. Mechatronic systems put “intelligence” into physical systems. Embedded sensors/actuators/processors are integral parts of mechatronic systems. The implementation of mechatronic systems is consistently on the rise. However, manufacturers are working hard to reduce the implementation cost of these systems while trying avoid compromising product quality. One way of addressing these conflicting objectives is through new automatic control methods, virtual sensing/estimation, and new innovative hardware topologies

Advances in Intelligent Vehicle Control

This book is a printed edition of the Special Issue Advances in Intelligent Vehicle Control that was published in the journal Sensors. It presents a collection of eleven papers that covers a range of topics, such as the development of intelligent control algorithms for active safety systems, smart sensors, and intelligent and efficient driving. The contributions presented in these papers can serve as useful tools for researchers who are interested in new vehicle technology and in the improvement of vehicle control systems