In order to enhance the neural network component of FusionAssurance for improved handling of out-of-distribution scenarios and better generalization, several strategies can be implemented:
Data Augmentation: Increasing the diversity of the training dataset through data augmentation techniques can help expose the neural network to a wider range of scenarios. This can include variations in weather conditions, lighting, road layouts, and traffic patterns.
Transfer Learning: Leveraging pre-trained models or knowledge from related tasks can provide a head start for the neural network in learning new scenarios. Fine-tuning the network on specific data related to out-of-distribution scenarios can help it adapt more effectively.
Uncertainty Estimation: Implementing methods to estimate uncertainty in predictions can help the neural network identify when it is encountering unfamiliar situations. This can trigger appropriate responses, such as deferring to the safety controller or requesting human intervention.
Ensemble Learning: Training multiple neural networks with different architectures or initializations and combining their outputs can improve robustness and generalization. Ensemble methods can help mitigate the risk of overfitting to specific scenarios.
Continual Learning: Implementing mechanisms for continual learning can enable the neural network to adapt and learn from new data over time. This can help it stay updated with evolving driving scenarios and improve performance in out-of-distribution situations.