AI-enabled technologies for autonomous and connected vehicles / Yi Lu Murphey, Ilya Kolmanovsky, Paul Watta, editors.
Murphey, Yi Lu.; Kolmanovsky, Ilya.; Watta, Paul.
2023
TE228.38
Available Online

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Title AI-enabled technologies for autonomous and connected vehicles / Yi Lu Murphey, Ilya Kolmanovsky, Paul Watta, editors.
ISBN 9783031067808 (electronic bk.)
3031067800 (electronic bk.)
9783031067792
3031067797
DOI https://doi.org/10.1007/978-3-031-06780-8
Imprint Cham : Springer, [2023]
Language English
Description 1 online resource (563 pages)
Other Standard Identifiers 10.1007/978-3-031-06780-8 doi
Call Number TE228.38
Dewey Decimal Classification 629.2/2
Summary This book reports on cutting-edge research and advances in the field of intelligent vehicle systems. It presents a broad range of AI-enabled technologies, with a focus on automated, autonomous and connected vehicle systems. It covers advanced machine learning technologies, including deep and reinforcement learning algorithms, transfer learning and learning from big data, as well as control theory applied to mobility and vehicle systems. Furthermore, it reports on cutting-edge technologies for environmental perception and vehicle-to-everything (V2X), discussing socioeconomic and environmental implications, and aspects related to human factors and energy-efficiency alike, of automated mobility. Gathering chapters written by renowned researchers and professionals, this book offers a good balance of theoretical and practical knowledge. It provides researchers, practitioners and policy makers with a comprehensive and timely guide on the field of autonomous driving technologies.
Note 3.1 CNN Models for Object Classification
Bibliography, etc. Note References-Environmental Perception for Intelligent Vehicles-1 Sensors-1.1 Development of Sensors in Intelligent Autonomous Vehicles-1.2 Camera-1.3 LiDAR-1.4 Radar-1.5 Future of Sensors in Intelligent Vehicles-2 Data Restoration-2.1 RGB Image Restoration-2.2 LiDAR Point Cloud Restoration-3 Semantic Segmentation-3.1 Semantic Segmentation for RGB Images-3.2 Semantic Segmentation for RGB-D Images-3.3 Semantic Segmentation for LiDAR Point Cloud-4 Object Detection-4.1 2D Object Detection-4.2 2D Object Detection of Fisheye Camera
Access Note Access limited to authorized users.
Added Author Murphey, Yi Lu.
Kolmanovsky, Ilya.
Watta, Paul.
Series Lecture notes in intelligent transportation and infrastructure.
Available in Other Form AI-Enabled Technologies for Autonomous and Connected Vehicles.
Linked Resources Online Access
Record Appears in Online Resources > Ebooks
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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

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