دانلود کتاب Creating Autonomous Vehicle Sys

فهرست مطالب :

Preface to the Second Edition
Teaching and Learning from This Book
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
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
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 Conclusion
3.8 References
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
4.4.3 Unsupervised Learning for Dense Correspondence
4.5 Conclusion
4.6 References
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
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
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
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
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
Autonomous Last-Mile Delivery Vehicles in Complex Traffic Environments
10.1 Background and Motivations
10.2 Autonomous Delivery Technologies in Complex Traffic Conditions
10.3 JD.com: An Autonomous Driving Solution
10.3.1 Autonomous Driving Architecture
10.3.2 Localization and HD Map
10.3.3 Perception
10.3.4 Prediction, Decision, and Planning
10.4 Safety and Security Strategies
10.4.1 Simulation-Level Verification
10.4.2 Vehicle-End Monitoring
10.4.3 Remote Monitoring
10.5 Production Deployments
10.6 Lessons Learned
10.7 References
PerceptIn’s Autonomous Vehicles Lite
11.1 Introduction
11.2 Expensive Autonomous Driving Technologies
11.2.1 Sensing
11.2.2 Localization
11.2.3 Perception
11.2.4 Decision Making
11.2.5 HD Map Creation and Maintenance
11.2.6 System Integration
11.3 Achieving Affordability and Reliability
11.3.1 Sensor Fusion
11.3.2 Modular Design
11.3.3 High-Precision Visual Map
11.4 Deploying Autonomous LSEV for Mobility as a Service
11.5 Conclusions
11.6 References
Author Biographies
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