A Concept for Efficient Scalability of Automated Driving Allowing for Technical, Legal, Cultural, and Ethical Differences

TL;DR

This paper proposes a two-stage fine-tuning process for scalable adaptation of automated driving systems, integrating technological and socio-political factors to enable deployment across diverse environments and regulations.

Contribution

It introduces a novel two-stage fine-tuning framework combining environment-specific reward models and transfer learning for scalable automated driving adaptation.

Findings

Effective adaptation across different countries and vehicle types.

Integration of socio-political requirements into technical system tuning.

Enhanced flexibility and scalability of automated driving systems.

Abstract

Efficient scalability of automated driving (AD) is key to reducing costs, enhancing safety, conserving resources, and maximizing impact. However, research focuses on specific vehicles and context, while broad deployment requires scalability across various configurations and environments. Differences in vehicle types, sensors, actuators, but also traffic regulations, legal requirements, cultural dynamics, or even ethical paradigms demand high flexibility of data-driven developed capabilities. In this paper, we address the challenge of scalable adaptation of generic capabilities to desired systems and environments. Our concept follows a two-stage fine-tuning process. In the first stage, fine-tuning to the specific environment takes place through a country-specific reward model that serves as an interface between technological adaptations and socio-political requirements. In the second stage, vehicle-specific transfer learning facilitates system adaptation and governs the validation of design decisions. In sum, our concept offers a data-driven process that integrates both technological and socio-political aspects, enabling effective scalability across technical, legal, cultural, and ethical differences.