To reduce fuel consumption and emissions in plug-in hybrid electric vehicles (PHEVs), it is equally important to select an appropriate drive train topology as it is to develop a suitable power flow control strategy While there are many control strategies that have been developed and presented, most are expansions of hybrid electric vehicle (HEV) control strategies and do not maximize the true potential the PHEV offers as a result of its ability to operate in electric-only mode over a significant distance In this paper, state-of-the-art control strategies are reviewed and classified in detail PHEV controllers mostly operate on either a rule-based or an optimization-based algorithm, each having its own advantages and disadvantages An overview of the controllers is given, and an analysis on which strategy is more suitable to maximize PHEV performance in different drive cycle conditions is provided Finally, a new classification for PHEV control strategies based on the operation of the vehicle is presented and verified through simulation results

Classification and Review of Control Strategies for Plug-In Hybrid Electric Vehicles

Vehicles on the road with some common interests can cooperatively form a platoon-based driving pattern, in which a vehicle follows another vehicle and maintains a small and nearly constant distance to the preceding vehicle. It has been proved that, compared with driving individually, such a platoon-based driving pattern can significantly improve road capacity and energy efficiency. Moreover, with the emerging vehicular ad hoc network (VANET), the performance of a platoon in terms of road capacity, safety, energy efficiency, etc., can be further improved. On the other hand, the physical dynamics of vehicles inside the platoon can also affect the performance of a VANET. Such a complex system can be considered a platoon-based vehicular cyber-physical system (VCPS), which has attracted significant attention recently. In this paper, we present a comprehensive survey on a platoon-based VCPS. We first review the related work of a platoon-based VCPS. We then introduce two elementary techniques involved in a platoon-based VCPS, i.e., the vehicular networking architecture and standards, and traffic dynamics, respectively. We further discuss the fundamental issues in a platoon-based VCPS, including vehicle platooning/clustering, cooperative adaptive cruise control, platoon-based vehicular communications, etc., all of which are characterized by the tightly coupled relationship between traffic dynamics and VANET behaviors. Since system verification is critical to VCPS development, we also give an overview of VCPS simulation tools. Finally, we share our view on some open issues that may lead to new research directions.

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A Survey on Platoon-Based Vehicular Cyber-Physical Systems

Rule based energy management strategy for a series–parallel plug-in hybrid electric bus optimized by dynamic programming

Design and Analysis ofFault-Tolerant Digital Systems: B. W. JOHNSON (Addison Wesley, 1989,577 pp., £41.35) The book provides an introduction to the important aspects of designing fault-tolerant systems, and an evaluation of how well the reliability goals have been achieved. The book is mainly oriented towards a final year undergraduate course on fault-tolerant computing, primarily with an implementation bias. In chapters 1 and 2, definitions and basic terminology are covered, which sets the stage for the remaining chapters, and provides the background and motivation for the remainder of the book. Chapter 3 provides a thorough analysis of fault-tolerance techniques and concepts. This chapter in particular is remarkably well written, covering the issues of hardware and information redundancy, which form the mainstay offault-tolerant computing. Subsequent chapters on the use and evaluation of the various approaches illustrate the principles as they have been put into practice. At the end of chapter 5, small projects that allow the reader to apply the material presented in the preceding chapters are included. The resurgence of interest in fault-tolerance with the emergence of VLSI is the theme of chapter 6, focussing on designing fault-tolerant systems in a VLSI environment. The problems and opportunities presented by VLSI are discussed and the use of redundancy techniques in order to enhance manufacturing yield and to provide in-service reliability are reviewed. The final chapter covers testing, design for testability and testability analysis, which must be considered during each phase of the design process to guarantee that resulting designs can be thoroughly tested. Each chapter is followed by a summary of the key issues and concepts presented therein, and a separate list of references, which makes it easily readable. In addition, there is a reading list with more comprehensive and specialised references devoted to each chapter. Overall, the book is well written, and contains a great deal of information in 577 pages. The book has a definite implementation bias, and draws considerably on the author's experience in industry, particularly reflected in the projects accompanying chapter 5. The book should be a useful addition to a library, and a suitable text to accompany a lecture course on fault-tolerant computing. R. RAMASWAMI, Department ofComputation, UMIST

Book Review: Design and Analysis of Fault-Tolerant Digital SystemsDesign and Analysis of Fault-Tolerant Digital Systems: JohnsonB. W. (Addison Wesley, 1989, 577 pp., £41.35)

One of the major challenges that autonomous cars are facing today is driving in urban environments. To make it a reality, autonomous vehicles require the ability to communicate with other road users and understand their intentions. Such interactions are essential between vehicles and pedestrians, the most vulnerable road users. Understanding pedestrian behavior, however, is not intuitive and depends on various factors, such as demographics of the pedestrians, traffic dynamics, environmental conditions, and so on. In this paper, we identify these factors by surveying pedestrian behavior studies, both the classical works on pedestrian–driver interaction and the modern ones that involve autonomous vehicles. To this end, we will discuss various methods of studying pedestrian behavior and analyze how the factors identified in the literature are interrelated. We will also review the practical applications aimed at solving the interaction problem, including design approaches for autonomous vehicles that communicate with pedestrians and visual perception and reasoning algorithms tailored to understanding pedestrian intention. Based on our findings, we will discuss the open problems and propose future research directions.

Autonomous Vehicles That Interact With Pedestrians: A Survey of Theory and Practice

Hybrid Electric Vehicles (HEV) combine the power from an electric motor with that from an internal combustion engine to propel the vehicle. The HEV electric motor is typically powered by a battery pack through power electronics. The HEV battery is recharged either by the engine or from regenerative braking. The electric drive mode is very limited for an HEV due to the limited battery power. A more powerful battery will increase the electric drive range of the vehicle, thus improving fuel economy. However, the battery will need to be recharged using an electric outlet since the regenerative braking and limited engine usage will not be sufficient to fully recharge the larger battery pack. In this paper, a rule-based energy management strategy for a Plug-in Hybrid Electric Vehicle (PHEV) is presented. Since large amount of electric energy is stored in the battery from the electric power grid, the fuel consumption is reduced significantly as compared with HEV counterpart. The proposed energy management strategy is implemented on a PHEV model in ADVISOR and the model is then simulated for a number of predefined drive cycles. The proposed PHEV algorithm results are compared with those for HEV with similar battery capacity as PHEV.

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A rule-based energy management strategy for Plug-in Hybrid Electric Vehicle (PHEV)

Enhancing traffic efficiency and alleviating &#x0028 even circumventing &#x0029 traffic congestion with advanced traffic signal control &#x0028 TSC &#x0029 strategies are always the main issues to be addressed in urban transportation systems. Since model predictive control &#x0028 MPC &#x0029 has a lot of advantages in modeling complex dynamic systems, it has been widely studied in traffic signal control over the past 20 years. There is a need for an in-depth understanding of MPC-based TSC methods for traffic networks. Therefore, this paper presents the motivation of using MPC for TSC and how MPC-based TSC approaches are implemented to manage and control the dynamics of traffic flows both in urban road networks and freeway networks. Meanwhile, typical performance evaluation metrics, solution methods, examples of simulations, and applications related to MPC-based TSC approaches are reported. More importantly, this paper summarizes the recent developments and the research trends in coordination and control of traffic networks with MPC-based TSC approaches. Remaining challenges and open issues are discussed towards the end of this paper to discover potential future research directions.

A survey of model predictive control methods for traffic signal control

Modeling vehicle-pedestrian interactions in the road environment is essential to develop pedestrian detection and pedestrian crash avoidance systems. In this paper, one novel approach is proposed to estimate the vehicle-pedestrian encountering risk in the road environment based on a large scale naturalistic driving data collection. Considering the difficulty to record actual pedestrian crashes in the naturalistic data collection, the encountering risk is estimated by the chances for driver to meet with pedestrian in the roadway as well as the chances for the driver and pedestrian to get into a potential conflict. Effects of different scenarios consisting of road conditions, pedestrian behaviors, and pedestrian numbers on the risk levels are also evaluated, and significant results are provided.

Estimation of the vehicle-pedestrian encounter/conflict risk on the road based on TASI 110-car naturalistic driving data collection

Driver distraction detection and intervention are important for designing modern driver-assistance systems and for improving safety. The main research question of this paper is to investigate how the cumulative driver off-road glance duration can be controlled to reduce the probability of occurrences of crash and near-crash events. Based on the available data sets from the Virginia Tech Transportation Institute (VTTI) 100-car study, the conditional probability is calculated to study the chance of crash and near-crash events when the given cumulative off-road glance duration in 6 s has been reached. Different off-road eye-glance locations and traffic density levels are also evaluated. The results show that one linear relationship can be obtained between the cumulative off-road eye-glance duration in 6 s and the risk of occurrences of crash and near-crash events, which varies for different off-road eye-glance locations. In addition, the traffic density level is found to be one significant moderator to this linear relationship. Detailed comparisons are made for different traffic density levels, and one nonlinear equation is obtained to predict the probability of occurrences of crash and near-crash events by considering both cumulative off-road glance duration and traffic density levels.

Studying the Effects of Driver Distraction and Traffic Density on the Probability of Crash and Near-Crash Events in Naturalistic Driving Environment

Pedestrian detection is a challenging task due to the high variance of pedestrians and fast changing background, especially for a single in-car camera system. Traditional HOG+SVM methods have two challenges: (1) false positives and (2) processing speed. In this paper, a new pedestrian detection method using multimodal HOG for pedestrian feature extraction and kernel based Extreme Learning Machine (ELM) for classification is presented. The experimental results using our naturalistic driving dataset show that the proposed method outperforms the traditional HOG+SVM method in both recognition accuracy and processing speed.

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Yaobin Chen

Papers

A rule-based energy management strategy for Plug-in Hybrid Electric Vehicle (PHEV)

A survey of model predictive control methods for traffic signal control

Estimation of the vehicle-pedestrian encounter/conflict risk on the road based on TASI 110-car naturalistic driving data collection

Studying the Effects of Driver Distraction and Traffic Density on the Probability of Crash and Near-Crash Events in Naturalistic Driving Environment

An Extreme Learning Machine-based pedestrian detection method