提出两阶段微调框架，实现自动驾驶跨技术与文化差异的高效部署。
A Concept for Efficient Scalability of Automated Driving Allowing for Technical, Legal, Cultural, and Ethical Differences
- 两阶段微调：先用国家专属奖励模型适配社会规则，再通过车辆迁移学习完成系统适配。
- 支持不同车辆配置、法规环境及伦理标准下的自动化能力灵活迁移。
- 适合关注自动驾驶全球化落地的科研与产业团队参考。
自动驾驶的高效可扩展性对降低成本、提升安全、节约资源和扩大影响至关重要。然而，现有研究多聚焦特定车辆与场景，而大规模部署需在多种配置与环境下实现可扩展性。车辆类型、传感器、执行器差异，以及交通法规、法律要求、文化动态乃至伦理范式的不同，均要求数据驱动能力具备高度灵活性。本文提出一种通用能力到目标系统与环境的可扩展适应概念。该方法采用两阶段微调：第一阶段通过国家专属奖励模型实现环境适配，作为技术调整与社会政治需求间的接口；第二阶段通过车辆特定迁移学习完成系统适配并验证设计决策。整体框架实现技术与社会政治因素的数据驱动融合，有效支持跨技术、法律、文化与伦理差异的可扩展部署。
原文摘要 · Abstract (English)
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.
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