This book presents the augmented reality (AR) and virtual reality (VR) automotive applications. It unites automobile with a leading technology i.e. augmented and virtual reality and uses the advantages of the latter to solve the problems faced by the former. The book highlights the reasons for the growing abundance and complexity in this sector. Virtual and augmented reality presents a powerful engineering tool that finds application in various engineering fields. It brings new possibilities that result is increasing of productivity and reliability of production, quality of products and processes. The book further illustrates the possible challenges in its applications and suggests ways to overcome them. The book includes nine chapters focusing on automobile collision avoidance, self-driving cars, autonomous vehicles, navigation systems, and many more applications.
Author(s): Aboul Ella Hassanien, Deepak Gupta, Ashish Khanna, Adam Slowik
Series: Studies in Systems, Decision and Control, 412
Publisher: Springer
Year: 2022
Language: English
Pages: 183
City: Cham
Preface
About This Book
Contents
About the Editors
Automotive Collision Avoidance System: A Review
1 Introduction
2 Literature Survey
3 Automotive Collison Avoidance System
4 Sensors for Collision Avoidance System
4.1 Active Sensors
4.2 Passive Sensors
4.3 Sensor Fusion
5 Vehicle Detection Schemes
5.1 Cueing
6 Tracking
7 Collision Avoidance Challenges
7.1 Sensor Challenges
8 Conclusion
References
Motion Planning and Manoeuvring in Self Driving Cars
1 Introduction
2 Architecture
2.1 Motion Planning
2.2 Search Space Planning
2.3 Planning Techniques
2.4 Maneuver Planning
2.5 Crash Detection
2.6 Intersection Passing Situation
3 Limitations
3.1 Hindrance Operation
3.2 Vehicular Motion
3.3 Risk Gauge
3.4 Adjacent Vehicle Sense
4 Test Results
4.1 Grand Cooperative Driving Challenge
4.2 Second Autonomous Self-driving Vehicle Competition
5 Discussion
6 Conclusion
References
Autonomous Vehicle Assisted by Heads up Display (HUD) with Augmented Reality Based on Machine Learning Techniques
1 Introduction
2 Related Works
3 System Model
4 Performance Analysis
5 Conclusion
References
Special Sensors for Autonomous Navigation Systems in Crops Investigation System
1 Introduction
2 Purpose of Chapter Description
3 Mobile Robot Navigation in Farmland Environments
4 Navigation Sensors Improve the Quality of Crops
4.1 Surveillance Navigation Technology
4.2 Frame Land Operation Through Global Positioning System (GPS)
4.3 Laser Scanning and Navigating Dependent on Perception
4.4 Other Sensors (Ultrasonic and RFID)
4.5 Laser Scanner-Based Navigation
5 Advanced Computational Techniques
5.1 Hough Transform
5.2 Information Fusion in Sensor Provide by Kalman Filter
5.3 Collection of Digital Image Segmentation Through Autonomous Vehicle
6 Strategies of Navigation Control Unit
6.1 Neural Network (NN) and/or Genetic Algorithm (GA)
6.2 Robotic Navigational Based on Proportional-Integral-Derivative (PID)
6.3 Robotic System Control by Fuzzy Logic (FL) Technology
7 Smart Agricultural Monitoring to Optimize Farming Productivity
8 Smart Agriculture Monitoring Solutions
9 Conclusions
References
AR/VR Technology for Autonomous Vehicles and Knowledge-Based Risk Assessment
1 Introduction
2 Background
3 Literature Survey
4 AR/VR in Autonomous Vehicles
5 Risk Assessment of AR/VR in Autonomous Vehicles
5.1 Knowledge-Based Risk Assessment
5.2 Risk at the Vehicle Level
5.3 Risk Avoidance
6 Discussion
7 Conclusion
References
Optimal Stacked Sparse Autoencoder Based Traffic Flow Prediction in Intelligent Transportation Systems
1 Introduction
2 The Proposed Model
2.1 Data Used
2.2 SSAE Based Prediction Model
2.3 WWO Based Hyperparameter Optimization
3 Experimental Validation
4 Conclusion
References
Leverage Computer Vision for Cost-Effective Learning Paradigm
1 Introduction
2 Literature Review
3 Proposed Method
3.1 Socket Programming
3.2 Filtration Process
3.3 Object Detection Process
3.4 Object Movement Process
3.5 Plane Detection and Object Placing
4 Application and Features
5 Conclusion and Future Work
References
Hand Gesture Recognition for Real-Time Game Play Using Background Elimination and Deep Convolution Neural Network
1 Introduction
1.1 Research Motivation
1.2 Research Work Direction and Contribution
1.3 Paper Organization
2 Related Work
2.1 Augmented Reality and Virtual Reality for Game Play
2.2 Hand Gesture Recognition Literature
3 Preliminaries and Dataset
4 Proposed Solution
5 Methodology
5.1 Background Elimination
5.2 Deep Convolution Neural Network
5.3 Human–Computer Interaction
6 Experiments Setup and Results
7 Conclusion and Future Scope
References
Modeling of Optimal Bidirectional LSTM Based Human Motion Recognition for Virtual Reality Environment
1 Introduction
2 The Proposed Human Motion Recognition Model
2.1 Kernel LDA Based Feature Extraction
2.2 BiLSTM-FCN Based Classification
2.3 Adam Based Hyperparameter Optimization
3 Experimental Validation
4 Conclusion
References
