Overview:
- Companies developing artificial intelligence models for autonomous driving are making improvements on rules-based systems to make autonomous vehicle behaviour more human like while at the same time improving safety and performance.
- End-to-End and Visual-Language-Action models are optimizing autonomous driving, from perception to controls, using a single neural network, enabling faster scalability by training on diverse datasets that require minimal adaptability for new/ different environments.
- The lack of interpretability and explainability of the decision-making processes of End-to-End and VLA models will require autonomous driving players to invest in a multi-layered safety ecosystem to convert the opaqueness of neural network policies to accountable systems.
