Download or read book LiDAR and Camera Fusion in Autonomous Vehicles written by Jie Zhang and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: LiDAR and camera can be an excellent complement to the advantages in an autonomous vehicle system. Various fusion methods have been developed for sensor fusion. Due to information lost, the autonomous driving system cannot navigate complex driving scenarios. When integrating the camera and LiDAR data, to account for loss of some detail of characters when using late fusion, we could choose a convolution neural network to fuse the features. However, the current sensor fusion method has low efficiency for the actual self-driving task due to the complex scenarios. To improve the efficiency and effectiveness of context fusion in high density traffic, we propose a new fusion method and architecture to combine the multi-model information after extracting the features from the LiDAR and camera. This new method is able to pay extra attention to features we want by allocating the weight during the feature extractor level.

Download or read book Multi sensor Fusion for Autonomous Driving written by Xinyu Zhang and published by Springer Nature. This book was released on with total page 237 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Sensor Fusion for 3D Object Detection for Autonomous Vehicles written by Yahya Massoud and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Thanks to the major advancements in hardware and computational power, sensor technology, and artificial intelligence, the race for fully autonomous driving systems is heating up. With a countless number of challenging conditions and driving scenarios, researchers are tackling the most challenging problems in driverless cars. One of the most critical components is the perception module, which enables an autonomous vehicle to "see" and "understand" its surrounding environment. Given that modern vehicles can have large number of sensors and available data streams, this thesis presents a deep learning-based framework that leverages multimodal data - i.e. sensor fusion, to perform the task of 3D object detection and localization. We provide an extensive review of the advancements of deep learning-based methods in computer vision, specifically in 2D and 3D object detection tasks. We also study the progress of the literature in both single-sensor and multi-sensor data fusion techniques. Furthermore, we present an in-depth explanation of our proposed approach that performs sensor fusion using input streams from LiDAR and Camera sensors, aiming to simultaneously perform 2D, 3D, and Bird's Eye View detection. Our experiments highlight the importance of learnable data fusion mechanisms and multi-task learning, the impact of different CNN design decisions, speed-accuracy tradeoffs, and ways to deal with overfitting in multi-sensor data fusion frameworks.

Download or read book Automatic Laser Calibration Mapping and Localization for Autonomous Vehicles written by Jesse Sol Levinson and published by Stanford University. This book was released on 2011 with total page 153 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation presents several related algorithms that enable important capabilities for self-driving vehicles. Using a rotating multi-beam laser rangefinder to sense the world, our vehicle scans millions of 3D points every second. Calibrating these sensors plays a crucial role in accurate perception, but manual calibration is unreasonably tedious, and generally inaccurate. As an alternative, we present an unsupervised algorithm for automatically calibrating both the intrinsics and extrinsics of the laser unit from only seconds of driving in an arbitrary and unknown environment. We show that the results are not only vastly easier to obtain than traditional calibration techniques, they are also more accurate. A second key challenge in autonomous navigation is reliable localization in the face of uncertainty. Using our calibrated sensors, we obtain high resolution infrared reflectivity readings of the world. From these, we build large-scale self-consistent probabilistic laser maps of urban scenes, and show that we can reliably localize a vehicle against these maps to within centimeters, even in dynamic environments, by fusing noisy GPS and IMU readings with the laser in realtime. We also present a localization algorithm that was used in the DARPA Urban Challenge, which operated without a prerecorded laser map, and allowed our vehicle to complete the entire six-hour course without a single localization failure. Finally, we present a collection of algorithms for the mapping and detection of traffic lights in realtime. These methods use a combination of computer-vision techniques and probabilistic approaches to incorporating uncertainty in order to allow our vehicle to reliably ascertain the state of traffic-light-controlled intersections.

Download or read book Unsettled Topics Concerning Coating Detection by LiDAR in Autonomous Vehicles written by Cristina P. Magnusson and published by . This book was released on 2021-01-18 with total page 42 pages. Available in PDF, EPUB and Kindle. Book excerpt: Autonomous vehicles (AVs) utilize multiple devices, like high-resolution cameras and radar sensors, to interpret the driving environment and achieve full autonomy. One of these instruments-the light detection and ranging (LiDAR) sensor-functions like radar, but utilizes pulsed infrared (IR) light, typically at wavelengths of 905 nm or 1,550 nm. The LiDAR sensor receives the reflected light from objects and calculates each object's distance and position. In current vehicles, the exterior automotive paint system covers an area larger than any other exterior material. Therefore, understanding how LiDAR wavelengths interact with other vehicles' coatings is extremely important for the safety of future automated driving technologies. Some coatings are more easily detected by LiDAR than others. In general, dark colors can absorb as much as 95% of the incident LiDAR intensity, reducing the amount of signal reflected toward the sensor. White cars are more easily detected as they exhibit high IR reflectivity. Many other factors like gloss level, effect pigments, and refinishes can affect reflectivity and even blind LiDAR sensors. On the other hand, several variables define overall LiDAR and perception system performance, including IR reflectivity of paint but also the target object's geometry, the type of LiDAR technology employed, angle of the target surface, environmental conditions, and sensor fusion software architecture. Sensing Technologies and Materials are two different industries that have not directly interacted in the perception and system sense. With the new applications in the AV industry, approaches need to be taken in a multidisciplinary way to ensure a reliable and safe technology for the future. This report provides a transversal view of the different industry segments from pigment and coating manufacturers to LiDAR component and vehicle system development and integration, and a structured decomposition of the different variables and technologies involved. NOTE: SAE EDGE Research Reports are intended to identify and illuminate key issues in emerging, but still unsettled, technologies of interest to the mobility industry. The goal of SAE EDGE Research Reports is to stimulate discussion and work in the hope of promoting and speeding resolution of identified issues. These reports are not intended to resolve the challenges they identify or close any topic to further scrutiny.

Download or read book Light weighted Deep Learning for Lidar and Visual Odometry Fusion in Autonomous Driving written by Dingnan Zhang and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Visual odometry is a prevalent way to deal with the relative localization problem, which is experiencing rapid development applied to autonomous vehicles. Achieving rapid pose estimation with high accuracy is a challenging task due to the safety requirements in the complex dynamic driving environment. Visual odometry algorithms are mostly designed based on a pipeline structure based on feature detection, feature matching, motion estimation, bundle adjustment, etc. These existing algorithms usually need to be individually designed and fine-tuned to have an acceptable result. A new measurement system, monocular visual odometry based on a neural network, is used to extract features and perform feature matching. It estimates poses directly from a sequence form videos without any adopting module. Neural network learns effective feature representation automatically. Pose estimation is usually applied in automated driving systems. The efficiency of measurement is the primary task due to the safety requirement in a complex environment on the street. An efficient model is used in this system instead of a traditional convolutional neural network. Some applications on embedded platforms, such as robotics and autonomous driving have limited hardware resources. Autonomous vehicles have equipped with different sensors to perceive the environment. They need a light-weighted, low-latency network model with 3 camera-LiDAR fusion. This proposed model can efficiently reduce the computational cost of the model, while the accuracy is improved.

Download or read book Autonomous Vehicle Lidar written by Kai Zhou and published by . This book was released on 2019-12-31 with total page 112 pages. Available in PDF, EPUB and Kindle. Book excerpt: The largest high-tech companies and leading automobile manufacturers in the world have unleashed torrents of effort and capital to position themselves for the arrival of autonomous vehicles. What is the fuss about? What is at stake? What are the leading sensor technologies? What is meant by "flash lidar" or "time-of-flight" sensors? With no less than 40 - 50 lidar companies vying to create mainstream automotive sensors, the climate is unique for young scientists and engineers to enter the field. What are the alliances forming between the companies, and how are they shifting? Who are current incumbents in the field? This tutorial text aims to introduce a technical but nonspecialist reader to autonomous vehicle lidar, starting from the fundamental physics of lidar and motivation for its application to autonomous vehicle systems. We will then introduce time of flight design concepts, following the light pathway through the source and transmitter optics to the photodetector. Next two distinct timing methods will be introduced, followed up by a brief discussion of beam steering. After finishing this text, the reader should be prepared to enter into laboratory explorations on the topic.

Download or read book A Novel Fusion Technique for 2D LIDAR and Stereo Camera Data Using Fuzzy Logic for Improved Depth Perception written by Harsh Saksena and published by . This book was released on 2021 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: Obstacle detection, avoidance and path finding for autonomous vehicles requires precise information of the vehicle's system environment for faultless navigation and decision making. As such vision and depth perception sensors have become an integral part of autonomous vehicles in the current research and development of the autonomous industry. The advancements made in vision sensors such as radars, Light Detection And Ranging (LIDAR) sensors and compact high resolution cameras is encouraging, however individual sensors can be prone to error and misinformation due to environmental factors such as scene illumination, object reflectivity and object transparency. The application of sensor fusion in a system, by the utilization of multiple sensors perceiving similar or relatable information over a network, is implemented to provide a more robust and complete system information and minimize the overall perceived error of the system. 3D LIDAR and monocular camera are the most commonly utilized vision sensors for the implementation of sensor fusion. 3D LIDARs boast a high accuracy and resolution for depth capturing for any given environment and have a broad range of applications such as terrain mapping and 3D reconstruction. Despite 3D LIDAR being the superior sensor for depth, the high cost and sensitivity to its environment make it a poor choice for mid-range application such as autonomous rovers, RC cars and robots. 2D LIDARs are more affordable, easily available and have a wider range of applications than 3D LIDARs, making them the more obvious choice for budget projects. The primary objective of this thesis is to implement a smart and robust sensor fusion system using 2D LIDAR and a stereo depth camera to capture depth and color information of an environment. The depth points generated by the LIDAR are fused with the depth map generated by the stereo camera by a Fuzzy system that implements smart fusion and corrects any gaps in the depth information of the stereo camera. The use of Fuzzy system for sensor fusion of 2D LIDAR and stereo camera is a novel approach to the sensor fusion problem and the output of the fuzzy fusion provides higher depth confidence than the individual sensors provide. In this thesis, we will explore the multiple layers of sensor and data fusion that have been applied to the vision system, both on the camera and lidar data individually and in relation to each other. We will go into detail regarding the development and implementation of fuzzy logic based fusion approach, the fuzzification of input data and the method of selection of the fuzzy system for depth specific fusion for the given vision system and how fuzzy logic can be utilized to provide information which is vastly more reliable than the information provided by the camera and LIDAR separately.

Download or read book Autonomous Driving Perception written by Rui Fan and published by Springer Nature. This book was released on 2023-10-06 with total page 391 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover the captivating world of computer vision and deep learning for autonomous driving with our comprehensive and in-depth guide. Immerse yourself in an in-depth exploration of cutting-edge topics, carefully crafted to engage tertiary students and ignite the curiosity of researchers and professionals in the field. From fundamental principles to practical applications, this comprehensive guide offers a gentle introduction, expert evaluations of state-of-the-art methods, and inspiring research directions. With a broad range of topics covered, it is also an invaluable resource for university programs offering computer vision and deep learning courses. This book provides clear and simplified algorithm descriptions, making it easy for beginners to understand the complex concepts. We also include carefully selected problems and examples to help reinforce your learning. Don't miss out on this essential guide to computer vision and deep learning for autonomous driving.

Download or read book Explainable Artificial Intelligence for Autonomous Vehicles written by Kamal Malik and published by CRC Press. This book was released on 2024-08-14 with total page 205 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explainable AI for Autonomous Vehicles: Concepts, Challenges, and Applications is a comprehensive guide to developing and applying explainable artificial intelligence (XAI) in the context of autonomous vehicles. It begins with an introduction to XAI and its importance in developing autonomous vehicles. It also provides an overview of the challenges and limitations of traditional black-box AI models and how XAI can help address these challenges by providing transparency and interpretability in the decision-making process of autonomous vehicles. The book then covers the state-of-the-art techniques and methods for XAI in autonomous vehicles, including model-agnostic approaches, post-hoc explanations, and local and global interpretability techniques. It also discusses the challenges and applications of XAI in autonomous vehicles, such as enhancing safety and reliability, improving user trust and acceptance, and enhancing overall system performance. Ethical and social considerations are also addressed in the book, such as the impact of XAI on user privacy and autonomy and the potential for bias and discrimination in XAI-based systems. Furthermore, the book provides insights into future directions and emerging trends in XAI for autonomous vehicles, such as integrating XAI with other advanced technologies like machine learning and blockchain and the potential for XAI to enable new applications and services in the autonomous vehicle industry. Overall, the book aims to provide a comprehensive understanding of XAI and its applications in autonomous vehicles to help readers develop effective XAI solutions that can enhance autonomous vehicle systems' safety, reliability, and performance while improving user trust and acceptance. This book: Discusses authentication mechanisms for camera access, encryption protocols for data protection, and access control measures for camera systems. Showcases challenges such as integration with existing systems, privacy, and security concerns while implementing explainable artificial intelligence in autonomous vehicles. Covers explainable artificial intelligence for resource management, optimization, adaptive control, and decision-making. Explains important topics such as vehicle-to-vehicle (V2V) communication, vehicle-to-infrastructure (V2I) communication, remote monitoring, and control. Emphasizes enhancing safety, reliability, overall system performance, and improving user trust in autonomous vehicles. The book is intended to provide researchers, engineers, and practitioners with a comprehensive understanding of XAI's key concepts, challenges, and applications in the context of autonomous vehicles. It is primarily written for senior undergraduate, graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer science and engineering, information technology, and automotive engineering.

Download or read book Autonomous Driving and Advanced Driver Assistance Systems ADAS written by Lentin Joseph and published by CRC Press. This book was released on 2021-12-15 with total page 540 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Robust Environmental Perception and Reliability Control for Intelligent Vehicles written by Huihui Pan and published by Springer Nature. This book was released on 2023-11-25 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the most recent state-of-the-art algorithms on robust environmental perception and reliability control for intelligent vehicle systems. By integrating object detection, semantic segmentation, trajectory prediction, multi-object tracking, multi-sensor fusion, and reliability control in a systematic way, this book is aimed at guaranteeing that intelligent vehicles can run safely in complex road traffic scenes. Adopts the multi-sensor data fusion-based neural networks to environmental perception fault tolerance algorithms, solving the problem of perception reliability when some sensors fail by using data redundancy. Presents the camera-based monocular approach to implement the robust perception tasks, which introduces sequential feature association and depth hint augmentation, and introduces seven adaptive methods. Proposes efficient and robust semantic segmentation of traffic scenes through real-time deep dual-resolution networks and representation separation of vision transformers. Focuses on trajectory prediction and proposes phased and progressive trajectory prediction methods that is more consistent with human psychological characteristics, which is able to take both social interactions and personal intentions into account. Puts forward methods based on conditional random field and multi-task segmentation learning to solve the robust multi-object tracking problem for environment perception in autonomous vehicle scenarios. Presents the novel reliability control strategies of intelligent vehicles to optimize the dynamic tracking performance and investigates the completely unknown autonomous vehicle tracking issues with actuator faults.

Download or read book Autonomous Vehicle Lidar Tutorial written by Gemma Salk and published by Independently Published. This book was released on 2021-03-20 with total page 98 pages. Available in PDF, EPUB and Kindle. Book excerpt: LiDAR, typically used as an acronym for " light detection and ranging", is essentially a sonar that uses pulsed laser waves to map the distance to surrounding objects. It is used by a large number of autonomous vehicles to navigate environments in real-time. This tutorial text aims to introduce a technical but nonspecialist reader to autonomous vehicle lidar, starting from the fundamental physics of lidar and motivation for its application to autonomous vehicle systems. We will then introduce a time of flight design concept, following the light pathway through the source and transmitter optics to the photodetector. The next two distinct timing methods will be introduced, followed up by a brief discussion of beam steering. After finishing this text, the reader should be prepared to enter into laboratory explorations on the topic.

Download or read book Fundamentals of Connected and Automated Vehicles written by Jeffrey Wishart and published by SAE International. This book was released on 2022-01-20 with total page 273 pages. Available in PDF, EPUB and Kindle. Book excerpt: The automotive industry is transforming to a greater degree that has occurred since Henry Ford introduced mass production of the automobile with the Model T in 1913. Advances in computing, data processing, and artificial intelligence (deep learning in particular) are driving the development of new levels of automation that will impact all aspects of our lives including our vehicles. What are Connected and Automated Vehicles (CAVs)? What are the underlying technologies that need to mature and converge for them to be widely deployed? Fundamentals of Connected and Automated Vehicles is written to answer these questions, educating the reader with the information required to make informed predictions of how and when CAVs will impact their lives. Topics covered include: History of Connected and Automated Vehicles, Localization, Connectivity, Sensor and Actuator Hardware, Computer Vision, Sensor Fusion, Path Planning and Motion Control, Verification and Validation, and Outlook for future of CAVs.

Download or read book Creating Autonomous Vehicle Systems Second Edition written by Liu Shaoshan and published by Springer Nature. This book was released on 2022-05-31 with total page 221 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is one of the first technical overviews of autonomous vehicles written for a general computing and engineering audience. The authors share their practical experiences designing autonomous vehicle systems. These systems are complex, consisting of three major subsystems: (1) algorithms for localization, perception, and planning and control; (2) client systems, such as the robotics operating system and hardware platform; and (3) the cloud platform, which includes data storage, simulation, high-definition (HD) mapping, and deep learning model training. The algorithm subsystem extracts meaningful information from sensor raw data to understand its environment and make decisions as to its future actions. The client subsystem integrates these algorithms to meet real-time and reliability requirements. The cloud platform provides offline computing and storage capabilities for autonomous vehicles. Using the cloud platform, new algorithms can be tested so as to update the HD map—in addition to training better recognition, tracking, and decision models. Since the first edition of this book was released, many universities have adopted it in their autonomous driving classes, and the authors received many helpful comments and feedback from readers. Based on this, the second edition was improved by extending and rewriting multiple chapters and adding two commercial test case studies. In addition, a new section entitled “Teaching and Learning from this Book” was added to help instructors better utilize this book in their classes. The second edition captures the latest advances in autonomous driving and that it also presents usable real-world case studies to help readers better understand how to utilize their lessons in commercial autonomous driving projects. This book should be useful to students, researchers, and practitioners alike. Whether you are an undergraduate or a graduate student interested in autonomous driving, you will find herein a comprehensive overview of the whole autonomous vehicle technology stack. If you are an autonomous driving practitioner, the many practical techniques introduced in this book will be of interest to you. Researchers will also find extensive references for an effective, deeper exploration of the various technologies.

Download or read book Sensor Fusion in Localization Mapping and Tracking written by Constantin Wellhausen and published by . This book was released on 2024 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Making autonomous driving possible requires extensive information about the surroundings as well as the state of the vehicle. While specific information can be obtained through singular sensors, a full estimation requires a multi sensory approach, including redundant sources of information to increase robustness. This thesis gives an overview of tasks that arise in sensor fusion in autonomous driving, and presents solutions at a high level of detail, including derivations and parameters where required to enable re-implementation. The thesis includes theoretical considerations of the approaches as well as practical evaluations. Evaluations are also included for approaches that did not prove to solve their tasks robustly. This follows the belief that both results further the state of the art by giving researchers ideas about suitable and unsuitable approaches, where otherwise the unsuitable approaches may be re-implemented multiple times with similar results. The thesis focuses on model-based methods, also referred to in the following as classical methods, with a special focus on probabilistic and evidential theories. Methods based on deep learning are explicitly not covered to maintain explainability and robustness which would otherwise strongly rely on the available training data. The main focus of the work lies in three main fields of autonomous driving: localization, which estimates the state of the ego-vehicle, mapping or obstacle detection, where drivable areas are identified, and object detection and tracking, which estimates the state of all surrounding traffic participants. All algorithms are designed with the requirements of autonomous driving in mind, with a focus on robustness, real-time capability and usability of the approaches in all potential scenarios that may arise in urban driving. In localization the state of the vehicle is determined. While traditionally global positioning systems such as a Global Navigation Satellite System (GNSS) are often used for this task, they are prone to errors and may produce jumps in the position estimate which may cause unexpected and dangerous behavior. The focus of research in this thesis is the development of a localization system which produces a smooth state estimate without any jumps. For this two localization approaches are developed and executed in parallel. One localization is performed without global information to avoid jumps. This however only provides odometry, which drifts over time and does not give global positioning. To provide this information the second localization includes GNSS information, thus providing a global estimate which is free of global drift. Additionally the use of LiDAR odometry for improving the localization accuracy is evaluated. For mapping the focus of this thesis is on providing a computationally efficient mapping system which is capable of being used in arbitrarily large areas with no predefined size. This is achieved by mapping only the direct environment of the vehicle, with older information in the map being discarded. This is motivated by the observation that the environment in autonomous driving is highly dynamic and must be mapped anew every time the vehicles sensors observe an area. The provided map gives subsequent algorithms information about areas where the vehicle can or cannot drive. For this an occupancy grid map is used, which discretizes the map into cells of a fixed size, with each cell estimating whether its corresponding space in the world is occupied. However the grid map is not created for the entire area which could potentially be visited, as this may be very large and potentially impossible to represent in the working memory. Instead the map is created only for a window around the vehicle, with the vehicle roughly in the center. A hierarchical map organization is used to allow efficient moving of the window as the vehicle moves through an area. For the hierarchical map different data structures are evaluated for their time and space complexity in order to find the most suitable implementation for the presented mapping approach. Finally for tracking a late-fusion approach to the multi-sensor fusion task of estimating states of all other traffic participants is presented. Object detections are obtained from LiDAR, camera and Radar sensors, with an additional source of information being obtained from vehicle-to-everything communication which is also fused in the late fusion. The late fusion is developed for easy extendability and with arbitrary object detection algorithms in mind. For the first evaluation it relies on black box object detections provided by the sensors. In the second part of the research in object tracking multiple algorithms for object detection on LiDAR data are evaluated for the use in the object tracking framework to ease the reliance on black box implementations. A focus is set on detecting objects from motion, where three different approaches are evaluated for motion estimation in LiDAR data: LiDAR optical flow, evidential dynamic mapping and normal distribution transforms. The thesis contains both theoretical contributions and practical implementation considerations for the presented approaches with a high degree of detail including all necessary derivations. All results are implemented and evaluated on an autonomous vehicle and real-world data. With the developed algorithms autonomous driving is realized for urban areas.

Download or read book Autonomous driving algorithms and Its IC Design written by Jianfeng Ren and published by Springer Nature. This book was released on 2023-08-09 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the rapid development of artificial intelligence and the emergence of various new sensors, autonomous driving has grown in popularity in recent years. The implementation of autonomous driving requires new sources of sensory data, such as cameras, radars, and lidars, and the algorithm processing requires a high degree of parallel computing. In this regard, traditional CPUs have insufficient computing power, while DSPs are good at image processing but lack sufficient performance for deep learning. Although GPUs are good at training, they are too “power-hungry,” which can affect vehicle performance. Therefore, this book looks to the future, arguing that custom ASICs are bound to become mainstream. With the goal of ICs design for autonomous driving, this book discusses the theory and engineering practice of designing future-oriented autonomous driving SoC chips. The content is divided into thirteen chapters, the first chapter mainly introduces readers to the current challenges and research directions in autonomous driving. Chapters 2–6 focus on algorithm design for perception and planning control. Chapters 7–10 address the optimization of deep learning models and the design of deep learning chips, while Chapters 11-12 cover automatic driving software architecture design. Chapter 13 discusses the 5G application on autonomous drving. This book is suitable for all undergraduates, graduate students, and engineering technicians who are interested in autonomous driving.