Deep Learning (DL) will be an effective approach for AI-based vehicular networks and can deliver a powerful set of tools for such vehicular network dynamics. In various domains of vehicular networks, DL can be used for learning-based channel estimation, traffic flow prediction, vehicle trajectory prediction, location-prediction-based scheduling and routing, intelligent network congestion control mechanism, smart load balancing and vertical handoff control, intelligent network security strategies, virtual smart & efficient resource allocation and intelligent distributed resource allocation methods.

This book is based on the work from world-famous experts on the application of DL for vehicle networks. It consists of the following five parts: (1) DL for vehicle safety and security: In this part, we have a few chapters to cover the use of DL algorithms for vehicle safety or security. (2) DL for effective vehicle communications: Vehicle networks consist of vehicle-to-vehicle and vehicle-to-roadside communications. Intelligent vehicle networks require the flexible selection of the best path across all vehicles, the adaptive sending rate control based on bandwidth availability, timely data downloading from roadside base-station, etc. (3) DL for vehicle control: For each individual vehicle, many operations require intelligent control: the emission is controlled based on the road traffic situation; the charging pile load is predicted through DL; the vehicle speed is adjusted based on the camera-captured image analysis. (4) DL for information management: This part covers some intelligent information collection and understanding. We can use DL for energy-saving vehicle trajectory control based on the road traffic situation and given destination information; we can also natural language processing based on DL algorithm for automatic internet of things (IoT) search during driving. (5) Other applications. This part introduces the use of DL models for other vehicle controls.

Autonomous vehicles are becoming more and more popular in the society. The DL and its variants will play more and more important roles in cognitive vehicle communications and control. Other machine learning models such as deep reinforcement learning will also facilitate the intelligent vehicle behavior understanding and adjustment. We expect that this book will become a valuable reference to your understanding of this critical field.

Part I. Deep Learning for Vehicle Safety and Security

1. Deep Learning for Vehicle Safety. 2. Deep Learning for Driver Drowsiness Classification for a Safe Vehicle Application. 3. A Deep Learning Perspective on Connected Automated Vehicle (CAV) Cybersecurity and Threat Intelligence..

Part II. Deep Learning for Vehicle Communications

4. Deep Learning for UAV Network Optimization. 5. State-of-the-Art in PHY Layer Deep Learning for Future Wireless Communication Systems and Networks. 6. Deep Learning-based Index Modulation Systems for Vehicle Communications. 7. Deep Reinforcement Learning Applications in Connected-Automated Transportation Systems.

Part III. Deep Learning for Vehicle Control

8. Vehicle emission control on road with temporal traffic information using deep reinforcement learning. 9. Load Prediction of Electric Vehicle Charging Pile. 10. Deep learning for autonomous vehicles: a vision-based approach to self-adapted robust control.

Part IV. DL for Information Management

11. A Natural Language Processing Based Approach for Automating IoT Search. 12. Towards Incentive-Compatible Vehicular Crowdsensing: A Reinforcement Learning-Based Approach. 13. Sub-Signal Detection from Noisy Complex Signals Using Deep Learning and Mathematical Morphology.

Part V. Miscellaneous

14. The basics of Deep learning algorithms and their effect on driving behavior and vehicle communications. 15. Integrated Simulation of Deep Learning, Computer Vision and Physical Layer of UAV and Ground Vehicle Networks.