I. INTRODUCTION  
A. Motivation  
B. Google Trends  
C. Key Contributions  
D. Paper Organization  

II. PROBLEM DESCRIPTION  
A. Terminology  
B. Techniques applied for Trajectory-Prediction Methods  

III. CONVENTIONAL TRAJECTORY PREDICTION METHOD  
A. Physics based Models  
B. Sampling Methods  
C. Probabilistic Models  

IV. DEEP LEARNING-BASED PREDICTION METHODS  
A. Sequential Modelling  
B. Vision Based Modelling  
C. Combination of CNNs and RNNs  
D. Generative Model  

V. REINFORCEMENT LEARNING-BASED METHODS  
A. Inverse Reinforcement Learning  
B. Deep Inverse Reinforcement Learning  
C. Imitation Learning  

VI. TRAINING AND EVALUATION  
A. Datasets  
B. Evaluation Metrics  

VII. DISCUSSION  

VIII. CHALLENGES AND FUTURE RESEARCH DIRECTIONS  
A. Challenges  
B. Future Research Directions  

IX. CONCLUSION