The physics-informed safety controller in FusionAssurance, while effective, may have some limitations that could be addressed for handling more complex environmental interactions and dynamic obstacles:
Limited Model Accuracy: The simplified vehicle dynamics model used in the safety controller may not capture all real-world complexities, leading to inaccuracies in trajectory predictions. Enhancing the model with more sophisticated dynamics could improve its performance.
Static Obstacle Handling: The controller's reliance on potential field functions for obstacle avoidance may struggle with dynamic or unpredictable obstacles. Introducing dynamic obstacle prediction and avoidance strategies could enhance its capability in handling such scenarios.
Scalability: As the complexity of environmental interactions increases, the scalability of the safety controller may become a concern. Implementing hierarchical control structures or adaptive control strategies could help manage complex scenarios more effectively.
Adaptability: The safety controller may struggle with rapidly changing environments or scenarios outside its training data distribution. Incorporating reinforcement learning or adaptive control mechanisms could enable it to adapt in real-time to novel situations.
Sensor Fusion: Enhancing the sensor fusion capabilities of the safety controller by integrating data from additional sensors, such as radar or ultrasonic sensors, could provide more comprehensive environmental awareness and improve obstacle detection and avoidance.