Plug-in hybrid electric vehicles (PHEVs) offer an immediate solution for emissions reduction and fuel displacement within the current infrastructure. Targeting PHEV powertrain optimization, a plethora of energy management strategies (EMSs) have been proposed. Although these algorithms present various levels of complexity and accuracy, they find a limitation in terms of availability of future trip information, which generally prevents exploitation of the full PHEV potential in real-life cycles. This paper presents a comprehensive analysis of EMS evolution toward blended mode (BM) and optimal control, providing a thorough survey of the latest progress in optimization-based algorithms. This is performed in the context of connected vehicles and highlights certain contributions that intelligent transportation systems (ITSs), traffic information, and cloud computing can provide to enhance PHEV energy management. The study is culminated with an analysis of future trends in terms of optimization algorithm development, optimization criteria, PHEV integration in the smart grid, and vehicles as part of the fleet. Index Terms-Connected vehicles, energy management strategy (EMS), intelligent transportation systems (ITS), optimal control, plug-in hybrid electric vehicle (PHEV). I. INTRODUCTION A IR quality has become a serious concern in cities and urban areas in recent years. This has promoted new legislation, affecting the European automotive sector through Euro I-VI, which limits emissions of CO, HC, NO x , and particulate matter [1]. As Euro VI became into force, the spotlight is nowadays on CO 2 emissions. The European Commission has established a 130 g CO 2 /km target for 2015, which will be reduced to 95 g CO 2 /km in 2021 [2]. Similar policies have been imposed in other automotive markets, such as the USA, China, and Japan. This legislation has encouraged the introduction of Manuscript

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Driver decisions and behaviors are essential factors that can affect the driving safety. To understand the driver behaviors, a driver activities recognition system is designed based on the deep convolutional neural networks (CNN) in this study. Specifically, seven common driving activities are identified, which are the normal driving, right mirror checking, rear mirror checking, left mirror checking, using in-vehicle radio device, texting, and answering the mobile phone, respectively. Among these activities, the first four are regarded as normal driving tasks, while the rest three are classified into the distraction group. The experimental images are collected using a low-cost camera, and ten drivers are involved in the naturalistic data collection. The raw images are segmented using the Gaussian mixture model (GMM) to extract the driver body from the background before training the behavior recognition CNN model. To reduce the training cost, transfer learning method is applied to fine tune the pre-trained CNN models. Three different pre-trained CNN models, namely, AlexNet, GoogLeNet, and ResNet50 are adopted and evaluated. The detection results for the seven tasks achieved an average of 81.6% accuracy using the AlexNet, 78.6% and 74.9% accuracy using the GoogLeNet and ResNet50, respectively. Then, the CNN models are trained for the binary classification task and identify whether the driver is being distracted or not. The binary detection rate achieved 91.4% accuracy, which shows the advantages of using the proposed deep learning approach. Finally, the real-world application are analysed and discussed. Index Terms-Driver Behaviour, driver distraction, convolutional neural network, transfer learning. I. INTRODUCTION A. Motivations D RIVER is in the center of the Road-Vehicle-Driver (RVD) loop. Driver decision and behaviors are the major aspects that can affect driving safety. It is reported that more

Intelligent vehicles and advanced driver assistance systems (ADAS) need to have proper awareness of the traffic context as well as the driver status since ADAS share the vehicle control authorities with the human driver. This study provides an overview of the ego-vehicle driver intention inference (DII), which mainly focus on the lane change intention on highways. First, a human intention mechanism is discussed in the beginning to gain an overall understanding of the driver intention. Next, the ego-vehicle driver intention is classified into different categories based on various criteria. A complete DII system can be separated into different modules, which consists of traffic context awareness, driver states monitoring, and the vehicle dynamic measurement module. The relationship between these modules and the corresponding impacts on the DII are analyzed. Then, the lane change intention inference (LCII) system is reviewed from the perspective of input signals, algorithms, and evaluation. Finally, future concerns and emerging trends in this area are highlighted.

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