自动驾驶车辆带来的需求与社会性困境

作者 森田玉雪(山梨县立大学)、马奈木俊介(教职研究员)
发表日期/编号 2018年1月 18-J-004
研究课题 关于人工智能等对经济产生的影响
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概要

This study, using an online survey with large samples, analyzes the latent demand for autonomous vehicles in Japan. The analysis is twofold.

First, we applied the choice-based conjoint analysis to estimate the respondents' willingness to pay (WTP) for the auto driving system (conditional automation and full automation) as well as the fuel types (hybrid and electricity) of a car that respondents would buy. We also estimate the factors affecting each of the five respondents' classes grouped by the latent class conditional logit, to elicit the consumer heterogeneity. We find that those who do not favor driving and those who trust the safeness of autonomous driving tend to have higher WTP for automation. Contrast to the preferences to fuel choice, the environmental concern and altruism of the respondents did not affect the selection of automation.

Second, we deal with consumers' attitudes toward the moral dilemma that artificial intelligence (AI) armed in vehicles should face: "the trolley problem" of choosing between two evils, such as running over pedestrians or sacrificing themselves and their passenger to save the pedestrians. We find that, like in the United States, there exists a particular gap between the Japanese consumers' morality and their expected purchasing behavior. Considering it, we alert that autonomous vehicles may cause the social dilemma, and insist the need to pay more attention to this social dilemma when we design the AI algorithm or traffic laws.

Published: Morita, Tamaki, and Shunsuke Mangai, 2020. "Autonomous vehicles: Willingness to pay and the social dilemma," Transportation Research Part C: Emerging Technologies, Vol. 119, 102748.
https://www.sciencedirect.com/science/article/pii/S0968090X20306616

Published: Yoo, Sunbin, Junya Kumagai, Tamaki Morita, Y. Gina Park, and Shunsuke Managi, 2024. "Who to sacrifice? Modeling the driver’s dilemma," Transportation Research Part A: Policy and Practice, Volume 178, December 2023, 103872.
https://www.sciencedirect.com/science/article/abs/pii/S0965856423002926