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Author: Chaojie Ou Publisher: ISBN: Category : Languages : en Pages :
Book Description
Driving safety continues receiving widespread attention from car designers, safety regulators, and automotive research community as driving accidents due to driver distraction or fatigue have increased drastically over the years. In the past decades, there has been a remarkable push towards designing and developing new driver assistance systems with much better recognition and prediction capabilities. Equipped with various sensory systems, these Advanced Driver Assistance Systems (ADAS) are able to accurately perceive information on road conditions, predict traffic situations, estimate driving risks, and provide drivers with imminent warnings and visual assistance. In this thesis, we focus on two main aspects of driver behavior modeling in the design of new generation of ADAS. We first aim at improving the generalization ability of driver distraction recognition systems to diverse driving scenarios using the latest tools of machine learning and connectionist modeling, namely deep learning. To this end, we collect a large dataset of images on various driving situations of drivers from the Internet. Then we introduce Generative Adversarial Networks (GANs) as a data augmentation tool to enhance detection accuracy. A novel driver monitoring system is also introduced. This monitoring system combines multi-information resources, including a driver distraction recognition system, to assess the danger levels of driving situations. Moreover, this thesis proposes a multi-modal system for distraction recognition under various lighting conditions and presents a new Convolutional Neural Network (CNN) architecture, which can operate real-time on a resources-limited computational platform. The new CNN is built upon a novel network bottleneck of Depthwise Separable Convolution layers. The second part of this thesis focuses on driver maneuver prediction, which infers the direction a driver will turn to before a green traffic light is on and predicts accurately whether or not he/she will change the current driving lane. Here, a new method to label driving maneuver records is proposed, by which driving feature sequences for the training of prediction systems are more closely related to their labels. To this end, a new prediction system, which is based on Quasi-Recurrent Neural Networks, is introduced. In addition, and as an application of maneuver prediction, a novel driving proficiency assessment method is proposed. This method exploits the generalization abilities of different maneuver prediction systems to estimate drivers' driving abilities, and it demonstrates several advantages against existing assessment methods. In conjunction with the theoretical contribution, a series of comprehensive experiments are conducted, and the proposed methods are assessed against state-of-the-art works. The analysis of experimental results shows the improvement of results as compared with existing techniques.
Author: Chaojie Ou Publisher: ISBN: Category : Languages : en Pages :
Book Description
Driving safety continues receiving widespread attention from car designers, safety regulators, and automotive research community as driving accidents due to driver distraction or fatigue have increased drastically over the years. In the past decades, there has been a remarkable push towards designing and developing new driver assistance systems with much better recognition and prediction capabilities. Equipped with various sensory systems, these Advanced Driver Assistance Systems (ADAS) are able to accurately perceive information on road conditions, predict traffic situations, estimate driving risks, and provide drivers with imminent warnings and visual assistance. In this thesis, we focus on two main aspects of driver behavior modeling in the design of new generation of ADAS. We first aim at improving the generalization ability of driver distraction recognition systems to diverse driving scenarios using the latest tools of machine learning and connectionist modeling, namely deep learning. To this end, we collect a large dataset of images on various driving situations of drivers from the Internet. Then we introduce Generative Adversarial Networks (GANs) as a data augmentation tool to enhance detection accuracy. A novel driver monitoring system is also introduced. This monitoring system combines multi-information resources, including a driver distraction recognition system, to assess the danger levels of driving situations. Moreover, this thesis proposes a multi-modal system for distraction recognition under various lighting conditions and presents a new Convolutional Neural Network (CNN) architecture, which can operate real-time on a resources-limited computational platform. The new CNN is built upon a novel network bottleneck of Depthwise Separable Convolution layers. The second part of this thesis focuses on driver maneuver prediction, which infers the direction a driver will turn to before a green traffic light is on and predicts accurately whether or not he/she will change the current driving lane. Here, a new method to label driving maneuver records is proposed, by which driving feature sequences for the training of prediction systems are more closely related to their labels. To this end, a new prediction system, which is based on Quasi-Recurrent Neural Networks, is introduced. In addition, and as an application of maneuver prediction, a novel driving proficiency assessment method is proposed. This method exploits the generalization abilities of different maneuver prediction systems to estimate drivers' driving abilities, and it demonstrates several advantages against existing assessment methods. In conjunction with the theoretical contribution, a series of comprehensive experiments are conducted, and the proposed methods are assessed against state-of-the-art works. The analysis of experimental results shows the improvement of results as compared with existing techniques.
Author: Yang Xing Publisher: Elsevier ISBN: 0128191147 Category : Technology & Engineering Languages : en Pages : 260
Book Description
Advanced Driver Intention Inference: Theory and Design describes one of the most important function for future ADAS, namely, the driver intention inference. The book contains the state-of-art knowledge on the construction of driver intention inference system, providing a better understanding on how the human driver intention mechanism will contribute to a more naturalistic on-board decision system for automated vehicles. Features examples of using machine learning/deep learning to build industry products Depicts future trends for driver behavior detection and driver intention inference Discuss traffic context perception techniques that predict driver intentions such as Lidar and GPS
Author: Ning Li Publisher: ISBN: Category : Languages : en Pages : 125
Book Description
As vehicle automation becomes increasingly prevalent and capable, drivers have the opportunity to delegate primary driving task control to automated systems. In recent years, significant efforts have been placed on developing and deploying Advanced Driver Assistance Systems (ADAS). These systems are designed to work with human drivers to increase vehicle safety, control, and performance in both ordinary and emergent situations. Current ADAS are mainly presented in rule-based or manually programmed design based on the summary and modeling of pre-collected human performance data. However, the pre-fixed system with limited personalization may not match human drivers' needs, which may arise the driver's dissatisfaction and cause ineffective system improvement. Human-centered machine learning (HCML) includes explicitly recognizing this human operator's role, as well as re-constructing machine learning workflows based on human working practices. The goal of this dissertation is to build a novel driver behavior modeling framework to understand and predict interactions with the driver assistance system from a human-centered perspective. It can lead not only to more usable machine learning tools but to new ways of improving the driver assistance systems. A driving simulator study was conducted to evaluate drivers' interactions with Forward Collision Warning (FCW) system. Gaussian Mixture Model (GMM) clusterization was used to identify different driving styles based drivers' driving performance, secondary task engagement, eye glance behavior and survey information. The impact of the FCW system on the different driving styles was also evaluated and discussed from three perspectives: initial reaction, distraction types, and safety benefits. A driver behavior model was also built using inverse reinforcement learning. Lastly, the timing prediction of FCW using driving preference was compared to the algorithm from a traditional FCW system. The findings of this study showed that ADAS without human feedback may not always bring positive safety benefits. Learning driver's preference through inverse reinforcement learning could better account for future scenarios and better predict driver behavior (e.g., braking action). This algorithm can be incorporated into real world in-vehicle warning systems such that the feedback and driving styles of the human operator are appropriately considered.
Author: Xiaolin Song Publisher: Synthesis Lectures on Advances ISBN: 9781636392622 Category : Computers Languages : en Pages : 171
Book Description
A road traffic participant is a person who directly participates in road traffic, such as vehicle drivers, passengers, pedestrians, or cyclists, however, traffic accidents cause numerous property losses, bodily injuries, and even deaths to them. To bring down the rate of traffic fatalities, the development of the intelligent vehicle is a much-valued technology nowadays. It is of great significance to the decision making and planning of a vehicle if the pedestrians' intentions and future trajectories, as well as those of surrounding vehicles, could be predicted, all in an effort to increase driving safety. Based on the image sequence collected by onboard monocular cameras, we use the Long Short-Term Memory (LSTM) based network with an enhanced attention mechanism to realize the intention and trajectory prediction of pedestrians and surrounding vehicles. However, although the fully automatic driving era still seems far away, human drivers are still a crucial part of the road‒driver‒vehicle system under current circumstances, even dealing with low levels of automatic driving vehicles. Considering that more than 90 percent of fatal traffic accidents were caused by human errors, thus it is meaningful to recognize the secondary task while driving, as well as the driving style recognition, to develop a more personalized advanced driver assistance system (ADAS) or intelligent vehicle. We use the graph convolutional networks for spatial feature reasoning and the LSTM networks with the attention mechanism for temporal motion feature learning within the image sequence to realize the driving secondary-task recognition. Moreover, aggressive drivers are more likely to be involved in traffic accidents, and the driving risk level of drivers could be affected by many potential factors, such as demographics and personality traits. Thus, we will focus on the driving style classification for the longitudinal car-following scenario. Also, based on the Structural Equation Model (SEM) and Strategic Highway Research Program 2 (SHRP 2) naturalistic driving database, the relationships among drivers' demographic characteristics, sensation seeking, risk perception, and risky driving behaviors are fully discussed. Results and conclusions from this short book are expected to offer potential guidance and benefits for promoting the development of intelligent vehicle technology and driving safety.
Author: Azim Eskandarian Publisher: Springer ISBN: 9780857290847 Category : Technology & Engineering Languages : en Pages : 0
Book Description
The Handbook of Intelligent Vehicles provides a complete coverage of the fundamentals, new technologies, and sub-areas essential to the development of intelligent vehicles; it also includes advances made to date, challenges, and future trends. Significant strides in the field have been made to date; however, so far there has been no single book or volume which captures these advances in a comprehensive format, addressing all essential components and subspecialties of intelligent vehicles, as this book does. Since the intended users are engineering practitioners, as well as researchers and graduate students, the book chapters do not only cover fundamentals, methods, and algorithms but also include how software/hardware are implemented, and demonstrate the advances along with their present challenges. Research at both component and systems levels are required to advance the functionality of intelligent vehicles. This volume covers both of these aspects in addition to the fundamentals listed above.
Author: Liew-Kee Kor Publisher: Springer ISBN: 9811372799 Category : Computers Languages : en Pages : 595
Book Description
This book is a product of the Third International Conference on Computing, Mathematics and Statistics (iCMS2017) to be held in Langkawi in November 2017. It is divided into four sections according to the thrust areas: Computer Science, Mathematics, Statistics, and Multidisciplinary Applications. All sections sought to confront current issues that society faces today. The book brings collectively quantitative, as well as qualitative, research methods that are also suitable for future research undertakings. Researchers in Computer Science, Mathematics and Statistics can use this book as a sourcebook to enrich their research works.
Author: Leonard Evans Publisher: Springer Science & Business Media ISBN: 1461321735 Category : Psychology Languages : en Pages : 503
Book Description
This volume contains the papers and discussions from a Symposium on :'Hu man Behavior and Traffic Safety" held at the General Motors Research Labora tories on September 23-25, 1984. This Symposium was the twenty-ninth in an annual series sponsored by the Research Laboratories. Initiated in 1957, these symposia have as their objective the promotion of the interchange of knowledge among specialists from many allied disciplines in rapidly developing or chang ing areas of science or technology. Attendees characteristically represent the aca demic, government, and industrial institutions that are noted for their ongoing activities in the particular area of interest. of this Symposium was to focus on the role of human behavior The objective in traffic safety. In this regard, a clear distinction is drawn between, on the one hand, "human behavior," and on the other "human performance." Human per formance at the driving task, or what the driver can do, has been the subject of much research reported in the technical literature. Although clearly of some rel evance, questions of performance do not appear to be central to most traffic crashes. Of much more central importance is human behavior, or what the driver in fact does. This is much more difficult to determine, and is the subject of the Symposium.
Author: Carlo Cacciabue Publisher: Springer Science & Business Media ISBN: 1846286182 Category : Computers Languages : en Pages : 441
Book Description
This book presents a general overview of the various factors that contribute to modelling human behaviour in automotive environments. This long-awaited volume, written by world experts in the field, presents state-of-the-art research and case studies. It will be invaluable reading for professional practitioners graduate students, researchers and alike.
Author: Nanxiang Li Publisher: ISBN: Category : Automobile driving Languages : en Pages : 262
Book Description
With the development of new in-vehicle technology, drivers are exposed to more sources of distractions, which can lead to unintentional accidents. Monitoring the driver attention level has become a relevant research problem. Many studies aim to understand the driver behavior using measurements from different perspectives, such as driving task performance, secondary task performance and driver physiology signal. Although these studies provide important characteristics about driver distractions, there are open challenges that remain to be addressed. First, there is no standard for quantifying the driver distraction level in either absolute or relative terms. Defining a reliable driver distraction metric is important to train machine learning algorithms that predict driver distractions. To address this question, we propose to use human perception to measure the perceived distraction levels across different types of distractions (e.g. visual and cognitive). In the visual-cognitive distraction space, we define distraction modes to represent the driver distraction level using data driven approaches. This representation provides a more comprehensive description of the detrimental effects caused by secondary tasks. It provides an ideal framework to analyze the effects of future in-vehicle systems on driver behaviors. Second, most of the previous studies have largely relied on driving simulators. Instead, we consider real-world driving scenarios on real roads. We use multimodal features extracted from various noninvasive sensors including the controller area network-bus (CAN-Bus), video cameras and microphone arrays. By applying different machine learning techniques, including binary classification, multiclass classification and regression models, we build models to track driver's attention level, detect driver distraction, and identify multi-modal discriminative features to capture distracted driver behaviors. Finally, we explore the detection of contextual information about the driver and the road to enhance the proposed driver attention model. In particular, we focus on detecting mirror check actions and detection of frontal vehicles. This contextual information can inform the in-vehicle safety system whether the drivers appropriately respond to the driving tasks required by the road conditions. In addition, we also develop a user-independent calibration free gaze estimation model, which is closely related to the driver visual/cognitive distraction estimation.
Author: Fei Hu Publisher: CRC Press ISBN: 1000877256 Category : Computers Languages : en Pages : 608
Book Description
Deep Learning (DL) is an effective approach for AI-based vehicular networks and can deliver a powerful set of tools for such vehicular network dynamics. In various domains of vehicular networks, DL can be used for learning-based channel estimation, traffic flow prediction, vehicle trajectory prediction, location-prediction-based scheduling and routing, intelligent network congestion control mechanism, smart load balancing and vertical handoff control, intelligent network security strategies, virtual smart and efficient resource allocation and intelligent distributed resource allocation methods. This book is based on the work from world-famous experts on the application of DL for vehicle networks. It consists of the following five parts: (I) DL for vehicle safety and security: This part covers the use of DL algorithms for vehicle safety or security. (II) DL for effective vehicle communications: Vehicle networks consist of vehicle-to-vehicle and vehicle-to-roadside communications. This part covers how Intelligent vehicle networks require a flexible selection of the best path across all vehicles, adaptive sending rate control based on bandwidth availability and timely data downloads from a roadside base-station. (III) DL for vehicle control: The myriad operations that require intelligent control for each individual vehicle are discussed in this part. This also includes emission control, which is based on the road traffic situation, the charging pile load is predicted through DL andvehicle speed adjustments based on the camera-captured image analysis. (IV) DL for information management: This part covers some intelligent information collection and understanding. We can use DL for energy-saving vehicle trajectory control based on the road traffic situation and given destination information; we can also natural language processing based on DL algorithm for automatic internet of things (IoT) search during driving. (V) Other applications. This part introduces the use of DL models for other vehicle controls. Autonomous vehicles are becoming more and more popular in society. The DL and its variants will play greater roles in cognitive vehicle communications and control. Other machine learning models such as deep reinforcement learning will also facilitate intelligent vehicle behavior understanding and adjustment. This book will become a valuable reference to your understanding of this critical field.
Author: Chaojie Ou
Author: Chaojie Ou
Author: Yang Xing
Author: Ning Li
Author: Xiaolin Song
Author: Azim Eskandarian
Author: Liew-Kee Kor
Author: Leonard Evans
Author: Carlo Cacciabue
Author: Nanxiang Li
Author: Fei Hu