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Creating autonomous vehicle systems /

By: Liu, Shaoshan.
Contributor(s): Li, Liyun [author] | Tang, Jie [author] | Wu, Shuang [author] | Gaudiot, Jean-Luc [author].
Series: Publisher: San Rafael, California : Morgan & Claypool, 2018Description: x, 186 pages : collor illustrations ; 27 cm.Content type: text Media type: unmediated Carrier type: volumeISBN: 1681730073; 9781681730073.Subject(s): Optical radar | Autonomous vehicles -- Data processing | Mechatronics | RoboticsDDC classification: 629.2 LIU
Contents:
Table of content : 1. Introduction to autonomous driving -- 1.1 Autonomous driving technologies overview -- 1.2 Autonomous driving algorithms -- 1.2.1 Sensing -- 1.2.2 Perception -- 1.2.3 Object recognition and tracking -- 1.2.4 Action -- 1.3 Autonomous driving client system -- 1.3.1 Robot operating system (ROS) -- 1.3.2 Hardware platform -- 1.4 Autonomous driving cloud platform -- 1.4.1 Simulation -- 1.4.2 HD map production -- 1.4.3 Deep learning model training -- 1.5 It is just the beginning -- 2. Autonomous vehicle localization -- 2.1 Localization with GNSS -- 2.1.1 GNSS overview -- 2.1.2 GNSS error analysis -- 2.1.3 Satellite-based augmentation systems -- 2.1.4 Real-time kinematic and differential GPS -- 2.1.5 Precise point positioning -- 2.1.6 GNSS INS integration -- 2.2 Localization with LiDAR and high-definition maps -- 2.2.1 LiDAR overview -- 2.2.2 High-definition maps overview -- 2.2.3 Localization with LiDAR and HD map -- 2.3 Visual odometry -- 2.3.1 Stereo visual odometry -- 2.3.2 Monocular visual odometry -- 2.3.3 Visual inertial odometry -- 2.4 Dead reckoning and wheel odometry -- 2.4.1 Wheel encoders -- 2.4.2 Wheel odometry errors -- 2.4.3 Reduction of wheel odometry errors -- 2.5 Sensor fusion -- 2.5.1 CMU Boss for urban challenge -- 2.5.2 Stanford Junior for urban challenge -- 2.5.3 Bertha from Mercedes Benz -- 2.6 References -- 3. Perception in autonomous driving -- 3.1 Introduction -- 3.2 Datasets -- 3.3 Detection -- 3.4 Segmentation -- 3.5 Stereo, optical flow, and scene flow -- 3.5.1 Stereo and depth -- 3.5.2 Optical flow -- 3.5.3 Scene flow -- 3.6 Tracking -- 3.7 Conclusions -- 3.8 References -- 4. Deep learning in autonomous driving perception -- 4.1 Convolutional neural networks -- 4.2 Detection -- 4.3 Semantic segmentation -- 4.4 Stereo and optical flow -- 4.4.1 Stereo -- 4.4.2 Optical flow -- 4.5 Conclusion -- 4.6 References -- 5. Prediction and routing -- 5.1 Planning and control overview -- 5.1.1 Architecture: planning and control in a broader sense -- 5.1.2 Scope of each module: solve the problem with modules -- 5.2 Traffic prediction -- 5.2.1 Behavior prediction as classification -- 5.2.2 Vehicle trajectory generation -- 5.3 Lane level routing -- 5.3.1 Constructing a weighted directed graph for routing -- 5.3.2 Typical routing algorithms -- 5.3.3 Routing graph cost: weak or strong routing -- 5.4 Conclusions -- 5.5 References -- 6. Decision, planning, and control -- 6.1 Behavioral decisions -- 6.1.1 Markov decision process approach -- 6.1.2 Scenario-based divide and conquer approach -- 6.2 Motion planning -- 6.2.1 Vehicle model, road model, and SL-coordination system -- 6.2.2 Motion planning with path planning and speed planning -- 6.2.3 Motion planning with longitudinal planning and lateral planning -- 6.3 Feedback control -- 6.3.1 Bicycle model -- 6.3.2 PID control -- 6.4 Conclusions -- 6.5 References -- 7. Reinforcement learning-based planning and control -- 7.1 Introduction -- 7.2 Reinforcement learning -- 7.2.1 Q-learning -- 7.2.2 Actor-critic methods -- 7.3 Learning-based planning and control in autonomous driving -- 7.3.1 Reinforcement learning on behavioral decision -- 7.3.2 Reinforcement learning on planning and control -- 7.4 Conclusions -- 7.5 References -- 8. Client systems for autonomous driving -- 8.1 Autonomous driving: a complex system -- 8.2 Operating system for autonomous driving -- 8.2.1 ROS overview -- 8.2.2 System reliability -- 8.2.3 Performance improvement -- 8.2.4 Resource management and security -- 8.3 Computing platform -- 8.3.1 Computing platform implementation -- 8.3.2 Existing computing solutions -- 8.3.3 Computer architecture design exploration -- 8.4 References -- 9. Cloud platform for autonomous driving -- 9.1 Introduction -- 9.2 Infrastructure -- 9.2.1 Distributed computing framework -- 9.2.2 Distributed storage -- 9.2.3 Heterogeneous computing -- 9.3 Simulation -- 9.3.1 BinPipeRDD -- 9.3.2 Connecting Spark and ROS -- 9.3.3 Performance -- 9.4 Model training -- 9.4.1 Why use Spark? -- 9.4.2 Training platform architecture -- 9.4.3 Heterogeneous computing -- 9.5 HD map generation -- 9.5.1 HD map -- 9.5.2 Map generation in the cloud -- 9.6 Conclusions -- 9.7 References -- Author biographies
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Holdings
Item type Current library Call number Status Date due Barcode Item holds
Standard Loan Standard Loan ATU Sligo Yeats Library Main Lending Collection 629.2 LIU (Browse shelf(Opens below)) Available 0062858
Total holds: 0

Includes bibliographical references.

Table of content : 1. Introduction to autonomous driving -- 1.1 Autonomous driving technologies overview -- 1.2 Autonomous driving algorithms -- 1.2.1 Sensing -- 1.2.2 Perception -- 1.2.3 Object recognition and tracking -- 1.2.4 Action -- 1.3 Autonomous driving client system -- 1.3.1 Robot operating system (ROS) -- 1.3.2 Hardware platform -- 1.4 Autonomous driving cloud platform -- 1.4.1 Simulation -- 1.4.2 HD map production -- 1.4.3 Deep learning model training -- 1.5 It is just the beginning -- 2. Autonomous vehicle localization -- 2.1 Localization with GNSS -- 2.1.1 GNSS overview -- 2.1.2 GNSS error analysis -- 2.1.3 Satellite-based augmentation systems -- 2.1.4 Real-time kinematic and differential GPS -- 2.1.5 Precise point positioning -- 2.1.6 GNSS INS integration -- 2.2 Localization with LiDAR and high-definition maps -- 2.2.1 LiDAR overview -- 2.2.2 High-definition maps overview -- 2.2.3 Localization with LiDAR and HD map -- 2.3 Visual odometry -- 2.3.1 Stereo visual odometry -- 2.3.2 Monocular visual odometry -- 2.3.3 Visual inertial odometry -- 2.4 Dead reckoning and wheel odometry -- 2.4.1 Wheel encoders -- 2.4.2 Wheel odometry errors -- 2.4.3 Reduction of wheel odometry errors -- 2.5 Sensor fusion -- 2.5.1 CMU Boss for urban challenge -- 2.5.2 Stanford Junior for urban challenge -- 2.5.3 Bertha from Mercedes Benz -- 2.6 References -- 3. Perception in autonomous driving -- 3.1 Introduction -- 3.2 Datasets -- 3.3 Detection -- 3.4 Segmentation -- 3.5 Stereo, optical flow, and scene flow -- 3.5.1 Stereo and depth -- 3.5.2 Optical flow -- 3.5.3 Scene flow -- 3.6 Tracking -- 3.7 Conclusions -- 3.8 References -- 4. Deep learning in autonomous driving perception -- 4.1 Convolutional neural networks -- 4.2 Detection -- 4.3 Semantic segmentation -- 4.4 Stereo and optical flow -- 4.4.1 Stereo -- 4.4.2 Optical flow -- 4.5 Conclusion -- 4.6 References -- 5. Prediction and routing -- 5.1 Planning and control overview -- 5.1.1 Architecture: planning and control in a broader sense -- 5.1.2 Scope of each module: solve the problem with modules -- 5.2 Traffic prediction -- 5.2.1 Behavior prediction as classification -- 5.2.2 Vehicle trajectory generation -- 5.3 Lane level routing -- 5.3.1 Constructing a weighted directed graph for routing -- 5.3.2 Typical routing algorithms -- 5.3.3 Routing graph cost: weak or strong routing -- 5.4 Conclusions -- 5.5 References -- 6. Decision, planning, and control -- 6.1 Behavioral decisions -- 6.1.1 Markov decision process approach -- 6.1.2 Scenario-based divide and conquer approach -- 6.2 Motion planning -- 6.2.1 Vehicle model, road model, and SL-coordination system -- 6.2.2 Motion planning with path planning and speed planning -- 6.2.3 Motion planning with longitudinal planning and lateral planning -- 6.3 Feedback control -- 6.3.1 Bicycle model -- 6.3.2 PID control -- 6.4 Conclusions -- 6.5 References -- 7. Reinforcement learning-based planning and control -- 7.1 Introduction -- 7.2 Reinforcement learning -- 7.2.1 Q-learning -- 7.2.2 Actor-critic methods -- 7.3 Learning-based planning and control in autonomous driving -- 7.3.1 Reinforcement learning on behavioral decision -- 7.3.2 Reinforcement learning on planning and control -- 7.4 Conclusions -- 7.5 References -- 8. Client systems for autonomous driving -- 8.1 Autonomous driving: a complex system -- 8.2 Operating system for autonomous driving -- 8.2.1 ROS overview -- 8.2.2 System reliability -- 8.2.3 Performance improvement -- 8.2.4 Resource management and security -- 8.3 Computing platform -- 8.3.1 Computing platform implementation -- 8.3.2 Existing computing solutions -- 8.3.3 Computer architecture design exploration -- 8.4 References -- 9. Cloud platform for autonomous driving -- 9.1 Introduction -- 9.2 Infrastructure -- 9.2.1 Distributed computing framework -- 9.2.2 Distributed storage -- 9.2.3 Heterogeneous computing -- 9.3 Simulation -- 9.3.1 BinPipeRDD -- 9.3.2 Connecting Spark and ROS -- 9.3.3 Performance -- 9.4 Model training -- 9.4.1 Why use Spark? -- 9.4.2 Training platform architecture -- 9.4.3 Heterogeneous computing -- 9.5 HD map generation -- 9.5.1 HD map -- 9.5.2 Map generation in the cloud -- 9.6 Conclusions -- 9.7 References -- Author biographies

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