LEADER 04338nam 22006135 450 001 9910742498103321 005 20230828144642.0 010 $a981-9932-80-7 024 7 $a10.1007/978-981-99-3280-1 035 $a(MiAaPQ)EBC30722810 035 $a(Au-PeEL)EBL30722810 035 $a(DE-He213)978-981-99-3280-1 035 $a(PPN)272273740 035 $a(EXLCZ)9928100205400041 100 $a20230828d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMulti-sensor Fusion for Autonomous Driving$b[electronic resource] /$fby Xinyu Zhang, Jun Li, Zhiwei Li, Huaping Liu, Mo Zhou, Li Wang, Zhenhong Zou 205 $a1st ed. 2023. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2023. 215 $a1 online resource (237 pages) 311 08$aPrint version: Zhang, Xinyu Multi-Sensor Fusion for Autonomous Driving Singapore : Springer,c2023 9789819932795 327 $aPart I: Basic -- Chapter 1. Introduction -- Chapter 2. Overview of Data Fusion in Autonomous Driving Perception -- Part II: Method -- Chapter 3. Multi-sensor Calibration -- Chapter 4. Multi-sensor Object Detection -- Chapter 5. Multi-sensor Scene Segmentation -- Chapter 6. Multi-sensor Fusion Localization -- Part III: Advance -- Chapter 7. OpenMPD: An Open Multimodal Perception Dataset -- Chapter 8. Vehicle-Road Multi-view Interactive Data Fusion -- Chapter 9. Information Quality in Data Fusion -- Chapter 10. Conclusions. 330 $aAlthough sensor fusion is an essential prerequisite for autonomous driving, it entails a number of challenges and potential risks. For example, the commonly used deep fusion networks are lacking in interpretability and robustness. To address these fundamental issues, this book introduces the mechanism of deep fusion models from the perspective of uncertainty and models the initial risks in order to create a robust fusion architecture. This book reviews the multi-sensor data fusion methods applied in autonomous driving, and the main body is divided into three parts: Basic, Method, and Advance. Starting from the mechanism of data fusion, it comprehensively reviews the development of automatic perception technology and data fusion technology, and gives a comprehensive overview of various perception tasks based on multimodal data fusion. The book then proposes a series of innovative algorithms for various autonomous driving perception tasks, to effectively improve the accuracy and robustness of autonomous driving-related tasks, and provide ideas for solving the challenges in multi-sensor fusion methods. Furthermore, to transition from technical research to intelligent connected collaboration applications, it proposes a series of exploratory contents such as practical fusion datasets, vehicle-road collaboration, and fusion mechanisms. In contrast to the existing literature on data fusion and autonomous driving, this book focuses more on the deep fusion method for perception-related tasks, emphasizes the theoretical explanation of the fusion method, and fully considers the relevant scenarios in engineering practice. Helping readers acquire an in-depth understanding of fusion methods and theories in autonomous driving, it can be used as a textbook for graduate students and scholars in related fields or as a reference guide for engineers who wish to apply deep fusion methods. 606 $aRobotics 606 $aComputer vision 606 $aData mining 606 $aRobotics 606 $aComputer Vision 606 $aData Mining and Knowledge Discovery 615 0$aRobotics. 615 0$aComputer vision. 615 0$aData mining. 615 14$aRobotics. 615 24$aComputer Vision. 615 24$aData Mining and Knowledge Discovery. 676 $a629.046 700 $aZhang$b Xinyu$01423688 701 $aLi$b Jun$0925356 701 $aLi$b Zhiwei$01423689 701 $aLiu$b Huaping$0954499 701 $aZhou$b Mo$01423690 701 $aWang$b Li$0639010 701 $aZou$b Zhenhong$01423691 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910742498103321 996 $aMulti-Sensor Fusion for Autonomous Driving$93552058 997 $aUNINA