Creating autonomous vehicle systems /
By: Liu, Shaoshan
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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![](/opac-tmpl/bootstrap/images/filefind.png)
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Item type | Current library | Call number | Status | Date due | Barcode | Item holds |
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ATU Sligo Yeats Library Main Lending Collection | 629.2 LIU (Browse shelf(Opens below)) | Available | 0062858 |
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