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

2025-07-24

AI Summary

To address scalability challenges in deploying autonomous driving systems across diverse countries, vehicle platforms, regulatory regimes, and cultural contexts, this paper proposes a two-stage fine-tuning framework. In the first stage, country-specific reward modeling decouples and incorporates sociopolitical, legal, and ethical constraints into the reinforcement learning objective. In the second stage, vehicle-specific transfer learning adapts the policy to heterogeneous sensor configurations, actuator characteristics, and vehicle dynamics. This work is the first to achieve modular, data-driven decoupling of sociopolitical considerations from technical adaptation—enabling plug-and-play deployment across regions and platforms. Experiments demonstrate rapid adaptation to multi-country traffic regulations and heterogeneous vehicle fleets, significantly reducing customization effort while improving regulatory compliance, safety, and generalization performance.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Search and Optimization: Learning to Search
• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search

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.

Problem

• Scalable adaptation of automated driving across diverse configurations
• Addressing technical, legal, cultural, and ethical differences in AD
• Data-driven integration of socio-political and technological requirements

Innovation

• Two-stage fine-tuning for scalable adaptation
• Country-specific reward model for socio-political integration
• Vehicle-specific transfer learning for system adaptation