自动驾驶智能汽车:理论,算法和实现

所需积分/C币:49 2017-03-27 20:30:28 4.83MB PDF

自动驾驶智能汽车:理论,算法和实现
Prof Hong cheng School of automation engineering University of Electronic Science and Technology 610054 Chengdu Sichuan People's republic of china acheng uestc. edu. cn Series editors Professor sameer singh, PhD Dr. Sing Bing Kang Research school of informatics Microsoft research Loughborough university Microsoft Corporation Loughborough One microsoft way UK Redmond. wa 98052 USA ISSN2191-6586 c-ISSN2191-6594 Advances in Computer Vision and Pattern recognition ISBN978-1-4471-2279-1 e-ISBN978-1-4471-2280-7 DOI10.1007/978-1-4471-22807 Springer london dordrecht Heidelberg New York British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library Library of Congress Control Number: 2011943117 o Springer-Verlag London Limited 201 Apart from any fair dealing for the purposes of research or private study, or criticism or review, as per- mitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced stored or transmitted, in any form or by any means, with the prior permission in writing of the publish ers, or in the case of reprographic reproduction in accordance with the terms of licenses issued by the Copyright Licensing Agency. Enquiries concerning reproduction outside those terms should be sent to the publishers The usc of rcgistcrcd namcs, trademarks, ctc, in this publication docs not imply, cvcn in thc abscncc of a specific statement, that such names are exempt from the relevant laws and regulations and therefore free for gcncral usc. The publisher makcs no rcprcscntation, express or implied, with regard to thc accuracy of thc information contained in this book and cannot accept any legal responsibility or liability for any crrors or omissions that may bc madc Printed on acid- frcc paper SpringerispartofSpringerScience+businessMedia(www.springer.com) Preface Over the years, the field of intelligent vehicles has become a major research theme in intelligent transportation systems since traffic accidents are serious and growing problems all over the world. The goal of an intelligent vehicle is to augment vehicle autonomous driving either entirely or partly for the purposes of safety, comforta bility, and saving energy. Indeed, many technologies of intelligent vehicles root in autonomous mobile robots. The tasks of intelligent vehicles become even more chal- lenging compared to indoor mobile robots for two reasons. First, real-time dynamic complex environment perception and modeling will challenge current indoor robot technologies. Autonomous intelligent vehicles have to finish the basic procedures perceiving and modeling environment, localizing and building maps, planning paths and making decisions, and controlling the vehicles within limit time for real-time purposes. Meanwhile, we face the challenge of processing large amounts of data from multi-sensors, such as cameras, lidars, radars. This is extremely hard in more complex outdoor environments. Toward this end, we have to implement those tasks in more efficient ways. Second, vehicle motion control faces the challenges of strong nonlinear characteristics due to high mass, especially in the processes of high speed and sudden steering. In this case, both lateral and longitudinal control algorithms of indoor robots do not work well This book presents our recent research work on intelligent vehicles and is aimed at the researchers and graduate students interested in intelligent vehicles our goal in writing this book is threefold. First, it creates an updated reference book of in telligent vehicles. Second, this book not only presents object/obstacle detection and recognition, but also introduces vehicle lateral and longitudinal control algorithms which benefits the readers keen to learn broadly about intelligent vehicles. finall we put emphasis on high-level concepts and at the same time provide the low-level details of implementation. We try to link theory, algorithms, and implementation to promote intelligent vehicle research This book is divided into four parts. The first part Autonomous Intelligent Ve hicles presents the research motivation and purposes, the state-of-art of intelligent vehicles research. Also, we introduce the framework of intelligent vehicles. The sec ond part Environment Perception and Modeling which includes road detection and tracking, Vehicle detection and tracking, Multiple-sensor based multiple-object tracking introduces environment perception and modeling. The third part vehicle Localization and Navigation which includes An integrated dGPs/Mu positioning approach, Vehicle navigation using global views presents vehicle navigation based on integrated GPS and INS. The fourth part Advanced Vehicle Motion control introduces vehicle lateral and longitudinal motion control Most of this book refers to our research work at Xian Jiaotong University and Carnegie Mellon University. During the last ten years of research, a large number of people had been working in the Springrobot Project at Xian Jiaotong University I would like to deliver my deep respect to my Ph. D advisor, Professor Nannin Zheng, who leaded me into this field. Also I would like to thank: Yuehu Liu, Xiaojun LV, Lin Ma, Xuetao Zhang, Junjie Qin, Jingbo Tang, Yingtuan Hou, Jing Yang, Li Zhao, Chong Sun, Fan Mu, Ran Li, Weijie Wang, and Huub van de Wetering Also, I would like to thank jie Yang at Carnegie mellon University who supported Hong Chengs research work during his stay at this university and Zicheng liu at Microsoft research who helped hong Cheng discuss vehicle navigation with global views. I also would like to our sincere and deep thanks to zhongjun dai who helped immensely with figure preparation and with the typesetting of the book in LaTex Many people have helped by proofreading draft materials and providing comments and suggestions, including Nana Chen, Rui Huang, Pingxin Long, Wenjun Jing, Yuzhuo Wang. Springer has provided excellent support throughout the final stages of preparation of this book, and I would like to thank our commissioning editor Wayne Wheeler for his support and professionalism as well as Simon Rees for his help Chengdu, Peoples Republic of china Hong Cheng Contents PartI Autonomous Intelligent vehicles 1 Introduction 1.1 Research Motivation and Purpose 1.2.1 Multi-sensor Fusion Based Environment Perception and.5 1. 2 The Key Technologies of Intelligent vehicles Modeling 6 1.2.2 Vehicle Localization and Map buildin 1. 2.3 Path Planning and decision-Makin g 1.2. 4 Low-Level motion control 1. 3 The Organization of This book References 10 2 The State-of-the-Art in the usa 2.1 Introduction 2.2 Carnegie Mellon University--Boss 2.3 Stanford University-Junior 15 2.4 Virginia Polytechnic Institute and State University--Odin 16 2.5 Massachusetts Institute of Technology--Talos 17 2.6 Cornell University-Skynet 18 2.7 University of Pennsylvania and Lehigh University--Little Ben 19 2.8 OShkosh Truck Corporation--TerraMax 20 References 21 3 The Framework of Intelligent vehicles 23 3.1 Introduction 23 3.2 Related Work 24 3.3 Interactive Safety Analysis Framework 25 References 28 VIll Content Part II Environment Perception and Modeling 4 Road Detection and Tracking 33 4.1 Introduction 33 4.2 Related Work 35 4.2.1 Model-Based Approaches 35 4.2.2 Multi-cue Fusion Based Approach ,,,35 4.2.3 Hypothesis-Validation Based Approaches 36 4.2.4 Neural Network Based Approaches 36 4.2.5 Stereo-Based Approaches 36 4.2.6 Temporal Correlation Based Approaches 37 4.2.7 Image Filtering Based Approaches 37 4.3 Lane Detection Using Adaptive Random Hough Transform 37 4.3.1 The Lane Shape model 37 4.3.2 The Adaptive Random Hough Transform 38 4.3.3 Experimental Results 41 4.4 Lane Tracking 43 4.4.1 Particle Filtering 43 4. 42 Lane model 45 4.4.3 Dynamic System Model 46 4.4.4 The Imaging model 46 4.4.5 The algorithm Implementation 48 4.5 Road Recognition using a mean shift algorithm 4.5.1 The Basic Mean Shift algorithm 52 4.5.2 Various Applications of the mean Shift Algorithm 54 4.5.3 The road Recognition algorithm 55 4.5.4 Experimental results and Analysis 56 References 5 Vehicle Detection and Tracking 6 5.1 Introduction.· 5.2 Related Work 62 5.3 Generating Candidate rois 63 5.4 Multi-resolution Vehicle hypothesis 65 5.5 Vehicle validation using gabor Features and svm 67 5.5.1 Vehicle representation 67 5.5.2 SVM Classifier 68 5.6 Boosted gabor features ,71 5.6. 1 Boosted Gabor Features Using Adaboost 72 5.6.2 Experimental Results and Analysis 74 References 79 6 Multiple-Sensor Based Multiple-Object Tracking 6.1 Introduction 81 6.2 Related Work 81 6.3 Obstacles Stationary or Moving Judgement USing Lidar Data 82 6. 4 Multi-obstacle Tracking and Situation Assessment 84 Contents IX 6.4.1 Multi-obstacle Tracking Based on EKF Using a Singl ensor .84 6.4.2 Lidar and radar track fusion 90 6.5 Conclusion and Future Work 92 References 94 Part Ii vehicle Localization and Navigation 7 An Integrated dGPs/MU Positioning Approach 7.1 Introduction Q 7.2 Related Work ,100 7.3 An Integrated DGPS/IMU Positioning Approach 101 7.3.1 The System equation 101 7.3.2 The Measurement Equation 104 7.3.3 Data fusion using ekf .105 References 106 8 Vehicle Navigation Using Global views 109 8.1 Introduction ■■ 10 8.2 The Problem and Proposed approach ..110 8.3 The Panoramic Imaging Model 112 8. 4 The Panoramic Inverse Perspective Mapping(pIPM) 114 8.4.1 The Mapping Relationship Between Each Image and a Panoramic image 114 8.4.2 The Panoramic Inverse Perspective Mapping 115 8.5 The Implementation of the pIPM 116 8.5.1 The field of view of n cameras in the vehicle coordinate System 116 8.5.2 Calculation of Each Interest Point's View Angle in the 8.5.3The Mapping Relations hip Between a 3D On-road Poirr ..116 Vehicle Coordinate system and a Panoramic Image 117 8.5.4 Image Interpolation in the Vehicle Coordinate System .. 117 8.6 The Elimination of wide-Angle lens' Radial error 118 8.7 Combining Panoramic Images with Electronic Maps 119 References 120 Part Iv Advanced Vehicle motion Control 9 The lateral Motion Control for Intelligent vehicles 125 9.1 Introduction .,125 9.2 Related work 125 9.3 The Mixed Lateral Control Strategy 126 9.3.1 Linear roads 127 9.3.2 Curvilinear roads 128 9.3.3 Calculating the radius of an arc 131 9.3.4 The Algorithm Flow 132 Contents 9. 4 The Relationship Between Motor Pulses and the Front Wheel Lean angle .133 References 136 10 Longitudinal Motion Control for Intelligent Vehicles ,139 10.1 Introduction 139 10.2 System ldentification in Vehicle Longitudinal Control 140 10.2.1 The First-Order Systems 140 10.2.2 First-Order Lag Systems ,,,,142 10.2.3 Identification of Our vehicle system 143 10.3 The Proposed velocity Controller ,145 10.3.1 Validating the Longitudinal Control System Function 145 10.3.2 Velocity Controller Design 146 10.4 Experimental Results and Analysis 148 References 150 Index 151 Part I Autonomous Intelligent Vehicles

...展开详情
试读 127P 自动驾驶智能汽车:理论,算法和实现

评论 下载该资源后可以进行评论 3

zljiaa 中国人写的,还没看,没啥信心
2018-03-15
回复
tichai7915 竟然打不开
2017-10-19
回复
kou_ryou 很适合初学者学习,建议读一读
2017-09-07
回复
img
ivccav

关注 私信 TA的资源

上传资源赚积分,得勋章
    最新推荐
    自动驾驶智能汽车:理论,算法和实现 49积分/C币 立即下载
    1/127
    自动驾驶智能汽车:理论,算法和实现第1页
    自动驾驶智能汽车:理论,算法和实现第2页
    自动驾驶智能汽车:理论,算法和实现第3页
    自动驾驶智能汽车:理论,算法和实现第4页
    自动驾驶智能汽车:理论,算法和实现第5页
    自动驾驶智能汽车:理论,算法和实现第6页
    自动驾驶智能汽车:理论,算法和实现第7页
    自动驾驶智能汽车:理论,算法和实现第8页
    自动驾驶智能汽车:理论,算法和实现第9页
    自动驾驶智能汽车:理论,算法和实现第10页
    自动驾驶智能汽车:理论,算法和实现第11页
    自动驾驶智能汽车:理论,算法和实现第12页
    自动驾驶智能汽车:理论,算法和实现第13页
    自动驾驶智能汽车:理论,算法和实现第14页
    自动驾驶智能汽车:理论,算法和实现第15页
    自动驾驶智能汽车:理论,算法和实现第16页
    自动驾驶智能汽车:理论,算法和实现第17页
    自动驾驶智能汽车:理论,算法和实现第18页
    自动驾驶智能汽车:理论,算法和实现第19页
    自动驾驶智能汽车:理论,算法和实现第20页

    试读已结束,剩余107页未读...

    49积分/C币 立即下载 >