To extend the proposed two-head network with uncertainty to handle more complex road scenarios like intersections or merging lanes, several enhancements can be implemented.
Multi-Head Expansion: Introducing additional heads specialized in detecting specific road features like intersection points, lane merging areas, or traffic signs can provide more detailed information for decision-making.
Dynamic Uncertainty Thresholding: Implementing dynamic uncertainty thresholds based on the complexity of the road scenario can help the model adapt its decision-making process. Higher uncertainty thresholds can trigger more cautious actions in intricate situations.
Geometric Context Integration: Incorporating advanced geometric context awareness modules that can detect and analyze complex road structures like roundabouts, diverging lanes, or complex intersections can enhance the model's understanding of challenging scenarios.
Behavior Prediction: Integrating predictive modules that anticipate the behavior of other vehicles in complex road scenarios can aid in making informed decisions, especially in situations like merging lanes or congested intersections.
Real-Time Feedback Loop: Implementing a real-time feedback loop mechanism that adjusts the model's predictions based on immediate feedback from the environment can improve adaptability in dynamic and complex road scenarios.