Autonomous vehicle (AV) technology can provide a safe and convenient transportation solution for the public, but the complex and various environments in the real world make it difficult to operate safely and reliably. A connected autonomous vehicle (CAV) is an AV with vehicle connectivity capability, which enhances the situational awareness of the AV and enables the cooperation between AVs. Hence, CAV technology can enhance the capabilities and robustness of AV to be a promising transportation solution in the future. This paper introduces a representative architecture of CAVs and surveys the latest research advances, methods, and algorithms for sensing, perception, planning, and control of CAVs. It reviews the state-of-the-art and state-of-the-practice (when applicable) of a multi-layer Perception-Planning-Control architecture including on-board sensors and vehicular communications, the methods of sensor fusion and localization and mapping in the perception layer, the algorithms of decision making and trajectory planning in the planning layer, and the control strategies of trajectory tracking in the control layer. Furthermore, the implementations and impact of vehicle connectivity and the corresponding consequential challenges of cooperative perception, complex connected decision making, and multi-vehicle controls are summarized and their significant research issues enumerated. Most importantly, the critical review in this paper provides a list and discussion of the remaining challenges and unsolved problems of CAVs in each Section which would be helpful to researchers in the field. The comprehensive coverage of this paper makes it particularly useful to academic researchers, practitioners, and students alike.

Research Advances and Challenges of Autonomous and Connected Ground Vehicles

Electronic stability control (ESC) is an evolution of antilock brake technology designed to help drivers maintain control of their vehicles in high-speed or sudden maneuvers and on slippery roads. Manufacturers first began equipping vehicles with ESC in the mid 1990s in Europe, and the technology appeared in other markets several years later. The wider proliferation of ESC across the vehicle fleet has allowed evaluation of its effects in real world crashes in many countries, including Japan, Germany, Sweden, France, Great Britain, and the United States. Studies have examined crash effects on different roadways, using differing analytic methods, different crash severities, and different make/model vehicles including both cars and SUVs. This paper provides a summary of those findings. Most studies find that ESC is highly effective in reducing single-vehicle crashes in cars and SUVs. Fatal single-vehicle crashes involving cars are reduced by about 30-50 percent and SUVs by 50-70 percent. Fatal rollover crashes are estimated to be about 70-90 percent lower with ESC regardless of vehicle type. A number of studies find improved effectiveness in reducing crashes when road conditions are slippery. There is little or no effect of ESC in all multi-vehicle crashes; however, there is a 17-38 percent reduction in more serious, fatal multi-vehicle crashes. Given the extraordinary benefits of ESC in preventing crashes, the implementation of ESC should be accelerated to include the full range of vehicles in both developed and developing markets.

The effectivness of electronic stability control in reducing real world crashes: a literature review

Induction motor (IM) drives, specifically the three-phase IMs, are a nonlinear system that are difficult to explain theoretically because of their sudden changes in load or speed conditions. Thus, an advanced controller is needed to enhance IM performance. Among numerous control techniques, fuzzy logic controller (FLC) has increasing popularity in designing complex IM control system due to their simplicity and adaptability. However, the performance of FLCs depends on rules and membership functions (MFs), which are determined by a trial-and-error procedure. The main objective of this paper is to present a critical review on the control and optimization techniques for solving the problems and enhancing the performance of IM drives. A detailed study on the control of variable speed drive, such as scalar and vector, is investigated. The scalar control functions of speed and V/f control are explained in an open- and closed-loop IM drive. The operation, advantages, and limitations of the direct and indirect field-oriented controls of vector control are also demonstrated in controlling the IM drive. A comprehensive review of the different types of optimization techniques for IM drive applications is highlighted. The rigorous review indicates that existing optimization algorithms in conventional controller and FLC can be used for IM drive. However, some problems still exist in achieving the best MF and suitable parameters for IM drive control. The objective of this review also highlights several factors, challenges, and problems of the conventional controller and FLC of the IM drive. Accordingly, the review provides some suggestions on the optimized control for the research and development of future IM drives. All the highlighted insights and recommendations of this review will hopefully lead to increasing efforts toward the development of advanced IM drive controllers for future applications.

Optimization techniques to enhance the performance of induction motor drives: A review

In much the same way as one may sit enjoying a glass of claret musing with one's friends over the composition of the world's greatest international soccer team, Charles Poser would probably figure in most Neurologists' forward line-up when considering contributions to multiple sclerosis over the last 30 years. The late Dale McFarlin and the recently retired Ian McDonald would be wearing jerseys on the same team. Charles Poser has contributed significantly in a number of areas and is justly renowned not only for establishing definitive criteria for diagnosing multiple sclerosis, but also for his work on epidemiology and his thoughts on the pathogenesis of multiple sclerosis. He is to be further congratulated on assembling a magnificent collection of illustrations in his current Atlas of Multiple Sclerosis , which admirably convey reflections of the history of multiple sclerosis as well as clinical aspects, pathology and imaging.

The book is divided into two sections. …

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An atlas of multiple sclerosis.

A 6DOF passive vibration isolator using X-shape supporting structures

This paper presents a path following application for vehicles based on a simple linear and time invariant single-track model, which is calculated based on a constant nominal longitudinal speed. In order to consider the differences in vehicle dynamics between the real vehicle and this constant nominal model, a tube-based robust Model Predictive Control (MPC) approach is introduced. The proposed control algorithm is designed in order to guarantee proper path tracking, not only considering lateral error, but also orientation error to the target trajectory. Additionally, strict constraints are considered in the control signal and the lateral path following error. The control approach is designed in order to guarantee certain comfort standards for a wide range of velocities, with guaranteed stability.

Robust Tube-Based Model Predictive Control for Lateral Path Tracking

A recently growing literature discusses the topics of direct yaw moment control based on model predictive control (MPC), and energy-efficient torque-vectoring (TV) for electric vehicles with multiple powertrains. To reduce energy consumption, the available TV studies focus on the control allocation layer, which calculates the individual wheel torque levels to generate the total reference longitudinal force and direct yaw moment, specified by higher level algorithms to provide the desired longitudinal and lateral vehicle dynamics. In fact, with a system of redundant actuators, the vehicle-level objectives can be achieved by distributing the individual control actions to minimize an optimality criterion, e.g., based on the reduction of different power loss contributions. However, preliminary simulation and experimental studies – not using MPC – show that further important energy savings are possible through the appropriate design of the reference yaw rate. This paper presents a nonlinear model predictive control (NMPC) implementation for energy-efficient TV, which is based on the concurrent optimization of the reference yaw rate and wheel torque allocation. The NMPC cost function weights are varied through a fuzzy logic algorithm to adaptively prioritize vehicle dynamics or energy efficiency, depending on the driving conditions. The results show that the adaptive NMPC configuration allows stable cornering performance with lower energy consumption than a benchmarking fuzzy logic TV controller using an energy-efficient control allocation layer.

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Asier Zubizarreta

Papers

Robust Tube-Based Model Predictive Control for Lateral Path Tracking

On Nonlinear Model Predictive Control for Energy-Efficient Torque-Vectoring

Real time direct kinematic problem computation of the 3PRS robot using neural networks

A procedure to evaluate Extended Computed Torque Control configurations in the Stewart–Gough platform

Redundant sensor based control of the 3RRR parallel robot