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.
Abstract (translated)
自动驾驶（AD）的高效可扩展性是降低成本、增强安全、节约资源和最大化影响的关键。然而，目前的研究主要集中在特定车辆和情境上，而广泛的部署则需要在各种配置和环境中实现可扩展性。不同类型的车辆、传感器、执行器以及交通法规、法律要求、文化动态甚至伦理范式都对数据驱动开发的能力提出了高度的灵活性需求。本文旨在解决将通用能力向所需系统和环境进行可扩展适应性的挑战。我们的概念遵循一个两阶段微调过程。 在第一阶段，通过国家特定的奖励模型进行具体的环境微调，该模型充当技术适应与社会政治要求之间的接口。第二阶段则是车辆特异性的迁移学习，以促进系统的适应性，并管理设计决策的有效验证。 总而言之，我们提出的概念提供了一种数据驱动的过程，将技术和社会政治方面相结合，使跨技术、法律、文化和伦理差异的高效扩展成为可能。
URL
https://arxiv.org/abs/2507.18326
