To enhance the model's capabilities and improve the accuracy and reliability of joint motion predictions, several extensions can be considered to incorporate additional contextual information:
Environmental Factors: Integrating environmental data such as weather conditions, road surface quality, lighting conditions, and traffic signs/signals can provide valuable context for predicting agent behaviors. By incorporating these factors into the model, it can adapt its predictions based on the specific environmental conditions, leading to more robust decision-making.
Driver Behavior Models: Including models of human driver behavior can offer insights into how human drivers interact with autonomous vehicles and other agents on the road. By incorporating driver behavior patterns, the model can anticipate human reactions and adjust its predictions and actions accordingly, improving overall safety and efficiency.
Object Detection and Tracking: Combining the motion prediction model with advanced object detection and tracking algorithms can enhance the understanding of the scene dynamics. By accurately detecting and tracking objects in the environment, the model can improve the prediction of agent trajectories and interactions, leading to more precise joint motion predictions.
Temporal Context: Considering the temporal context of interactions between agents can further refine the predictions. By analyzing historical data and trends in agent behaviors, the model can anticipate future movements more accurately, especially in dynamic and evolving traffic scenarios.
By incorporating these additional contextual factors into the model, it can achieve a more comprehensive understanding of the driving environment, leading to more accurate and reliable joint motion predictions for autonomous driving applications.