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The Need for Location-based Machine Learning Models for Level 5 Automated Vehicles

Assuring safety of machine learning (ML) models in autonomous vehicles is a challenging task. This is because of the complex operational design domain (ODD) settings under which we need to validate ML models. In particular, autonomous vehicles with level 5 of driving automation need to operate under any ODD conditions, and should ensure safety of both road users and passengers. However, deploying common ML models across a fleet of vehicles to operate in multiple regions can complicate the safety assurance process.Even when an ML model is found to be causing a crash due to an ODD condition occurring in only one of the regions, we still should update it across the fleets of all regions. If we can limit its update within that particular region, we can reduce the complexity of safety assurance. In this paper, we propose the location-based machine learning models for level 5 automated vehicles to address this problem and how they will be helpful compared to deploying instances of global ML models which are same across a company’s fleets of vehicles.