I. INTRODUCTION  
II. MOTIVATION AND BACKGROUND  
A. Autonomous Driving  
B. Approximate Techniques  
C. Edge AI  
D. Communications in Autonomous Vehicles  
E. Taxonomy of Edge AI Technologies for CAV  
F. Motivation and Methodology of Choosing Literature  

III. AI & AUTONOMOUS DRIVING  
A. Perception  
B. HD Map  
C. SLAM  
D. Vehicular Communication  
E. Energy Efficient Approaches in Autonomous Driving  
F. An Overview of Dataset for Autonomous Driving  

IV. EDGE AI WITH AUTONOMOUS DRIVING  
A. Edge Computing and Intelligence  
B. Edge Training and Optimization  
C. Edge Inference  
D. Federated Learning and Autonomous Driving  

CHALLENGES FOR VEHICULAR SERVICES  
A. Autonomous Driving Framework  
C. Energy-Efficient Edge Frameworks  

VI. RESEARCH OUTLOOK AND OPEN PROBLEMS  
A. Connected Vehicle Service and Case-Study  
B. Enablers for Edge Application in Autonomous Driving  
C. Energy Efficiency Evaluation of DNN Implementation on Embedded Devices  
D. Outlook of Edge AI Pipeline  

VII. CONCLUSION