Cross-cultural Deployment of Autonomous Vehicles Using Data-light Inverse Reinforcement Learning
On the map: End-to-end autonomous driving → Reinforcement learning for driving
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Reinforcement learning for driving, one of 9 directions in End-to-end autonomous driving. Builds on 10 of its 35 references that are in the map. First posted on arXiv 15 Apr 2025.
Builds on
10 of 35 references.
References · 10
- 2019A Comprehensive Survey on Transfer Learningcited by 1,160
- 2018The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systemscited by 277
- 2022A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunitiescited by 163
- 2023Machine Culturecited by 55
- 2015The social dilemma of autonomous vehiclescited by 48
- 2019Human-Like Autonomous Car-Following Model with Deep Reinforcement Learningcited by 48
- 2019Liability, Ethics, and Culture-Aware Behavior Specification using Rulebookscited by 43
- 2022Social Interactions for Autonomous Driving: A Review and Perspectivescited by 27
- 2021PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learningcited by 23
- 2020Socially-Compatible Behavior Design of Autonomous Vehicles with Verification on Real Human Datacited by 18
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- 2021DriverGym: Democratising Reinforcement Learning for Autonomous Driving
- 2026GeoDrive-Bench: Benchmarking Region-Specific Multimodal Reasoning in Autonomous Driving
- 2026Aligning Microscopic Vehicle and Macroscopic Traffic Statistics: Reconstructing Driving Behavior from Partial Data
- 2021Quick Learner Automated Vehicle Adapting its Roadmanship to Varying Traffic Cultures with Meta Reinforcement Learning
- 2022Driving in Real Life with Inverse Reinforcement Learning
- 2025AD-R1: Closed-Loop Reinforcement Learning for End-to-End Autonomous Driving with Impartial World Models
- 2020A Survey on Autonomous Vehicle Control in the Era of Mixed-Autonomy: From Physics-Based to AI-Guided Driving Policy Learning
- 2025A Comprehensive Review of Reinforcement Learning for Autonomous Driving in the CARLA Simulator