Linked e-resources
Details
Table of Contents
Intro
Preface
Contents
Advances, Opportunities and Challenges in AI-enabled Technologies for Autonomous and Connected Vehicles
1 Introduction
2 Autonomous Vehicles: Current Technologies and Challenges
2.1 Levels of Autonomy
2.2 AI Technologies in Autonomous Vehicles
3 Connectivity and Mobility
3.1 Mobility Research
3.2 Prediction Model for V2X Communications
3.3 Big Data Research in Transportation
3.4 Automotive Cybersecurity
References
Sensors and Perception
Semi-autonomous Truck Platooning with a Lean Sensor Package
1 Overview
2 Literature Survey
3 Auburn Platooning System
3.1 Dynamic Base Real-Time Kinematic Positioning (DRTK)
3.2 Delphi Electronically Scanning Radar (ESR)
3.3 Dedicated Short Range Communications
3.4 Sensor Fusion
4 Testing Campaign
5 Results of Sensor Impairment
5.1 Effect of a Faulty Radar on Platooning
5.2 Effect of a Degraded GPS on Platooning
5.3 Effect of Radio Interference
5.4 Summary
6 Conditional Effects
6.1 Occlusions
6.2 Rain
6.3 Antenna Position
6.4 RF Interference
6.5 GPS Outage
6.6 Road Curvature
6.7 Grade
7 Conclusion
4.3 3D Object Detection
5 Object Tracking
5.1 Object Tracking for RGB Images
5.2 Object Tracking for Point Cloud
6 Simultaneous Localization and Mapping
6.1 SLAM Overview
6.2 2D Visual Location and Mapping
6.3 3D Visual Location and Mapping
7 Multi-sensor Fusion
7.1 Multi-sensor Fusion Overview
7.2 LiDAR and Camera Fusion
7.3 LiDAR and Radar Fusion
7.4 Radar and Camera Fusion
References
3D Object Detection for Autonomous Driving
1 Introduction
2 Recent Advance in 3D Object Detection
2.1 Cloud Point-Based Method
2.2 Image-Based Method
3 Descriptor Enhanced Stereo R-CNN
3.1 Overview
3.2 Coarse 3D Box Estimation
3.3 Unsupervised Learning of the Local Descriptor
3.4 Descriptor-Enhanced 3D Box Alignment
4 Experiments
4.1 Implementation Details
4.2 Comparison with State-of-the-Art
4.3 Multi-class Comparison with 3DBBX
4.4 Ablation Study
4.5 Runtime
5 Conclusion and Future Work
References
Comparative Study on Transfer Learning for Object Classification and Detection
1 Introduction
2 State-of-the-Art Review in DNNs and Transfer Learning
3 Architecture and Characteristics of CNN Models
Preface
Contents
Advances, Opportunities and Challenges in AI-enabled Technologies for Autonomous and Connected Vehicles
1 Introduction
2 Autonomous Vehicles: Current Technologies and Challenges
2.1 Levels of Autonomy
2.2 AI Technologies in Autonomous Vehicles
3 Connectivity and Mobility
3.1 Mobility Research
3.2 Prediction Model for V2X Communications
3.3 Big Data Research in Transportation
3.4 Automotive Cybersecurity
References
Sensors and Perception
Semi-autonomous Truck Platooning with a Lean Sensor Package
1 Overview
2 Literature Survey
3 Auburn Platooning System
3.1 Dynamic Base Real-Time Kinematic Positioning (DRTK)
3.2 Delphi Electronically Scanning Radar (ESR)
3.3 Dedicated Short Range Communications
3.4 Sensor Fusion
4 Testing Campaign
5 Results of Sensor Impairment
5.1 Effect of a Faulty Radar on Platooning
5.2 Effect of a Degraded GPS on Platooning
5.3 Effect of Radio Interference
5.4 Summary
6 Conditional Effects
6.1 Occlusions
6.2 Rain
6.3 Antenna Position
6.4 RF Interference
6.5 GPS Outage
6.6 Road Curvature
6.7 Grade
7 Conclusion
4.3 3D Object Detection
5 Object Tracking
5.1 Object Tracking for RGB Images
5.2 Object Tracking for Point Cloud
6 Simultaneous Localization and Mapping
6.1 SLAM Overview
6.2 2D Visual Location and Mapping
6.3 3D Visual Location and Mapping
7 Multi-sensor Fusion
7.1 Multi-sensor Fusion Overview
7.2 LiDAR and Camera Fusion
7.3 LiDAR and Radar Fusion
7.4 Radar and Camera Fusion
References
3D Object Detection for Autonomous Driving
1 Introduction
2 Recent Advance in 3D Object Detection
2.1 Cloud Point-Based Method
2.2 Image-Based Method
3 Descriptor Enhanced Stereo R-CNN
3.1 Overview
3.2 Coarse 3D Box Estimation
3.3 Unsupervised Learning of the Local Descriptor
3.4 Descriptor-Enhanced 3D Box Alignment
4 Experiments
4.1 Implementation Details
4.2 Comparison with State-of-the-Art
4.3 Multi-class Comparison with 3DBBX
4.4 Ablation Study
4.5 Runtime
5 Conclusion and Future Work
References
Comparative Study on Transfer Learning for Object Classification and Detection
1 Introduction
2 State-of-the-Art Review in DNNs and Transfer Learning
3 Architecture and Characteristics of CNN Models