I. Introduction  
II. Preliminaries of Reinforcement Learning  
A. Problem Formulation  
B. Value-based Methods  
C. Policy-based Methods  
D. Actor-Critic Methods  
III. Preliminaries of Imitation Learning  
A. Problem Formulation  
B. Behavior Clone  
C. Inverse Reinforcement Learning  
D. Generative Adversarial Imitation Learning  
IV. Architectures of DRL/DIL Integrated AD Systems  
A. Mode 1. DRL/DIL Integrated Control  
B. Mode 2. Extension of Mode 1 with High-level Command  
C. Mode 3. DRL/DIL Integrated Motion Planning  
D. Mode 4. DRL/DIL Integrated Behavior Planning  
E. Mode 5. DRL/DIL Integrated Hierarchical Planning and Control  
F. Statistical Comparison  
V. Task-driven Methods  
A. State Space and Input Design  
B. Action Space and Output Design  
VI. Problem-driven Methods  
A. Safety-enhanced DRL/DIL for AD  
B. Interaction-aware DRL/DIL for AD  
C. Uncertainty-aware DRL/DIL for AD  
VII. Discussion  
A. System Architecture  
B. Formulation of Driving Tasks  
C. Safe Driving Policy  
D. Interaction with Traffic Participants  
E. Uncertainty of the Environment  
F. Validation and Benchmarks  
VIII. Conclusions