用少量数据让自动驾驶车跨文化适应不同驾驶习惯。
Cross-cultural Deployment of Autonomous Vehicles Using Data-light Inverse Reinforcement Learning
- 基于逆强化学习，仅需少量本地数据重校自动驾驶行为。
- 实测超5.6万公里，本地数据依赖降低98.67%。
- 适合数据匮乏地区，推动全球公平部署自动驾驶。
除了交通法规，驾驶文化还包含司机之间隐含的、非正式的集体行为模式，这种差异在国家、地区甚至城市间显著存在，已成为自动驾驶汽车跨区域部署的主要挑战。当前数据驱动方法虽有望通过学习实现文化适配，但许多欠发达地区缺乏足够本地数据。为此，本文提出一种轻数据量的跨文化自动驾驶部署方案——数据轻量逆强化学习，旨在重新校准特定文化的自动驾驶系统，并使其融入其他文化环境。首先，通过对比德国、中国和美国高速公路自然驾驶数据集，系统分析了驾驶文化的差异。随后，在三国间进行快速跨文化部署测试，累计里程超过56,084公里。结果表明，该方法在本地数据稀缺时表现尤为优越，最高可减少98.67%的本地数据依赖。本研究有助于构建更广泛、更公平的全球自动驾驶市场，尤其惠及缺乏足够本地数据以开发文化适配自动驾驶的地区。
原文摘要 · Abstract (English)
More than the adherence to specific traffic regulations, driving culture touches upon a more implicit part - an informal, conventional, collective behavioral pattern followed by drivers - that varies across countries, regions, and even cities. Such cultural divergence has become one of the biggest challenges in deploying autonomous vehicles (AVs) across diverse regions today. The current emergence of data-driven methods has shown a potential solution to enable culture-compatible driving through learning from data, but what if some underdeveloped regions cannot provide sufficient local data to inform driving culture? This issue is particularly significant for a broader global AV market. Here, we propose a cross-cultural deployment scheme for AVs, called data-light inverse reinforcement learning, designed to re-calibrate culture-specific AVs and assimilate them into other cultures. First, we report the divergence in driving cultures through a comprehensive comparative analysis of naturalistic driving datasets on highways from three countries: Germany, China, and the USA. Then, we demonstrate the effectiveness of our scheme by testing the expeditious cross-cultural deployment across these three countries, with cumulative testing mileage of over 56084 km. The performance is particularly advantageous when cross-cultural deployment is carried out without affluent local data. Results show that we can reduce the dependence on local data by a margin of 98.67% at best. This study is expected to bring a broader, fairer AV global market, particularly in those regions that lack enough local data to develop culture-compatible AVs.
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