This book contains the latest research on machine learning and embedded computing in advanced driver assistance systems (ADAS). It encompasses research in detection, tracking, LiDAR and camera processing, ethics, and communications. Several new datasets are also provided for future research work. Researchers and others interested in these topics will find important advances contained in this book.

In the context of advanced driver-assistance systems (ADAS), vehicles are equipped with multiple sensors to record the vehicle's environment and use intelligent algorithms to understand the data. This study contributes to the research in modern ADAS on different aspects. Methods deployed in ADAS must be accurate and computationally efficient in order to run fast on embedded platforms. We introduce a novel approach for pedestrian detection that economizes on the computational cost of cascades. We demonstrate that (a) our two-stage cascade achieves a high accuracy while running in real time, and (b) our three-stage cascade ranks as the fourth best-performing method on one of the most challenging pedestrian datasets. The other challenge faced with ADAS is the scarcity of positive training data. We introduce a novel approach that enables AdaBoost detectors to benefit from a high number of negative samples. We demonstrate that our approach ranks as the second-best among its competitors on two challenging pedestrian datasets while being multiple times faster. Acquiring labeled training data is costly and time-consuming, particularly for traffic sign recognition. We investigate the use of synthetic data with the aspiration to reduce the human efforts behind the data preparation. We (a) algorithmically and architecturally adapt the adversarial modeling framework to the image data provided in ADAS, and (b) conduct various evaluations and discuss promising future research directions.

This book contains the latest research on machine learning and embedded computing in advanced driver assistance systems (ADAS). It encompasses research in detection, tracking, LiDAR and camera processing, ethics, and communications. Several new datasets are also provided for future research work. Researchers and others interested in these topics will find important advances contained in this book.

This book provides a comprehensive reference for both academia and industry on the fundamentals, technology details, and applications of Advanced Driver-Assistance Systems (ADAS) and autonomous driving, an emerging and rapidly growing area. The book written by experts covers the most recent research results and industry progress in the following areas: ADAS system design and test methodologies, advanced materials, modern automotive technologies, artificial intelligence, reliability concerns, and failure analysis in ADAS. Numerous images, tables, and didactic schematics are included throughout. This essential book equips readers with an in-depth understanding of all aspects of ADAS, providing insights into key areas for future research and development. • Provides comprehensive coverage of the state-of-the-art in ADAS • Covers advanced materials, deep learning, quality and reliability concerns, and fault isolation and failure analysis • Discusses ADAS system design and test methodologies, novel automotive technologies • Features contributions from both academic and industry authors, for a complete view of this important technology

Autonomous Driving and Advanced Driver-Assistance Systems (ADAS): Applications, Development, Legal Issues, and Testing outlines the latest research related to autonomous cars and advanced driver-assistance systems, including the development, testing, and verification for real-time situations of sensor fusion, sensor placement, control algorithms, and computer vision. Features: Co-edited by an experienced roboticist and author and an experienced academic Addresses the legal aspect of autonomous driving and ADAS Presents the application of ADAS in autonomous vehicle parking systems With an infinite number of real-time possibilities that need to be addressed, the methods and the examples included in this book are a valuable source of information for academic and industrial researchers, automotive companies, and suppliers.

The 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT) aims to provide a forum that brings together International researchers from academia and practitioners in the industry to meet and exchange ideas and recent research work on all aspects of Information and Communication Technologies including Computing, communication, IOT, LiDAR, Image Analysis, wireless communication and other new technologies

A comparison of performance between tradition support vector machine (SVM), single kernel, multiple kernel learning (MKL), and modern deep learning (DL) classifiers are observed in this thesis. The goal is to implement different machine-learning classification system for object detection of three-dimensional (3D) Light Detection and Ranging (LiDAR) data. The linear SVM, non-linear single kernel, and MKL requires hand crafted features for training and testing their algorithm. The DL approach learns the features itself and trains the algorithm. At the end of these studies, an assessment of all the different classification methods are shown.