Cross-cultural Deployment of Autonomous Vehicles Using Data-light Inverse Reinforcement Learning

TL;DR

A cross-cultural deployment scheme for AVs is proposed, called data-light inverse reinforcement learning, designed to re-calibrate culture-specific AVs and assimilate them into other cultures, to bring a broader, fairer AV global market, particularly in those regions that lack enough local data to develop culture-compatible AVs.

Abstract

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.

Figures (7)

• Figure 1: Cross-cultural deployment framework for autonomous vehicles. The proposed data-light inverse reinforcement learning approach enables cross-cultural autonomous vehicle (AV) deployment. Localized deployment (green) relies heavily on abundant local driving data, while direct deployment (black) attempts to deploy AVs without adapting to local cultures, often leading to incompatibility. Cross-cultural deployment (red and blue) transfers a driving archetype ($\Psi$) extracted from one culture (e.g., Culture A) and calibrates it to another culture (e.g., Culture B) using only a small amount of local data. The driving culture ($w_A$) from Culture A is first decoupled from the archetype ($\Psi$), and the latter is transferred to Culture B. Subsequently, $w_B$ is calibrated based on a small amount of local data in Culture B to produce a culture-compatible driving model. This framework addresses data scarcity challenges and ensures seamless integration of AVs into diverse driving cultures globally. (The source of world map: http://bzdt.ch.mnr.gov.cn/)
• Figure 2: Cross-cultural and localized deployment comparisons for Germany-to-China and Germany-to-the-USA. a, Scenario for Germany-to-China, where the cross-cultural deployment path closely follows the original data, while the localized deployment deviates. b, The close-ups at $T_1$ and $T_2$, indicate that both deployments align at $T_1$, but the localized deployment shifts upward at $T_2$. c, TTC curves show that the cross-cultural deployment maintains closer consistency with the original than the localized deployment. d, $a_x$ curves indicate that the cross-cultural deployment better preserves the original data, while the localized deployment deviates. e, $a_y$ curves further confirm that the cross-cultural deployment retains a driving behavior closer to the original. f, Scenario for Germany-to-the-USA, illustrating that the cross-cultural deployment remains well aligned with the original data, while the localized deployment lags. g, The close-ups at $T_1$ and $T_2$, show that the localized deployment exhibits noticeable lag at $T_2$. h, TTC curves indicate that the cross-cultural deployment stays closer to the original data, while the localized deployment exhibits larger deviations. i, $a_x$ curves show that the cross-cultural deployment more accurately follows the original data, whereas the localized deployment drifts. j, $a_y$ curves exhibit minimal differences among the deployments, indicating similar lateral driving behavior in this scenario.
• Figure 3: Comparison of localized and cross-cultural deployment results in driving behavior distributions. a–d, Density distributions of $a_x$, $a_y$, $v_x$, and $v_y$ for the localized deployment trained on 100% German data. The red lines indicate density errors between the localized deployment and the original data, showing strong alignment. e–h, Cross-cultural deployment results for Germany-to-China. The distributions demonstrate adaptation to China's driving behavior, with distribution deviations shown in red lines. i–l, Cross-cultural deployment results for Germany-to-the-USA. The density distributions indicate the model's adaptation to the USA's driving behavior, with distribution deviations shown in red lines.
• Figure 4: Lane-changing frequency across localized and cross-cultural deployments. Box plots show the lane-changing frequency distributions for Germany-to-China (green) and Germany-to-the-USA (blue). The cross-cultural deployment closely aligns with the original data, while localized deployment exhibits increasing deviations as training data decreases. The shaded region at the bottom represents the standard error (STD), highlighting the growing instability in lane-changing frequency with reduced local data.
• Figure 5: Comparison of cross-cultural and direct deployment in capturing driving styles. a, b, Velocity-acceleration distributions for Germany-to-China under cross-cultural deployment (a) and direct deployment (b). Scatter points represent individual driving styles, with density distributions shown along the axes. c, RMSE of velocity and acceleration for Germany-to-China, showing lower errors in cross-cultural deployment. d, e, Velocity-acceleration distributions for Germany-to-the-USA under cross-cultural deployment (d) and direct deployment (e). f, RMSE for Germany-to-the-USA, indicating that cross-cultural deployment better preserves driving styles.