迈向人机共生的人工智能：自动驾驶的乘客情感评估与图灵测试

库逸轩

doi:10.3981/j.issn.1000-7857.2023.09.01356

科技导报 >

2025 , Vol. 43 >Issue 3: 74 - 80

DOI: https://doi.org/10.3981/j.issn.1000-7857.2023.09.01356

特色专题：混合智能中的情感交互与协同决策

迈向人机共生的人工智能：自动驾驶的乘客情感评估与图灵测试

库逸轩 ^,¹^,²

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1. 中山大学心理学系, 广州 510275
2. 鹏城实验室, 深圳 518000

库逸轩，教授，研究方向为记忆与情感的神经机制，电子信箱：kuyixuan@sysu.edu.cn

收稿日期: 2023-09-05

网络出版日期: 2025-03-07

基金资助

教育部高等学校心理学类专业教指委教改项目(20221028)

中山大学教学改革项目(26000-12220011)

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Towards man-machine symbiosis in artificial intelligence: Passenger emotion assessment and Turing tests in autonomous driving

Yixuan KU ^,¹^,²

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1. Department of Psychology, Sun Yat-sen University, Guangzhou 510275, China
2. Peng Cheng Laboratory, Shenzhen 518000, China

Received date: 2023-09-05

Online published: 2025-03-07

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摘要

自动驾驶技术大规模部署面临技术、法规、信任等挑战。人机共生理念有助于推动自动驾驶技术发展。旨在通过深度合作和相互理解，克服技术、法规、信任等挑战，提高用户信任，优化系统设计，实现人机深度交互和共生。研究结果显示，SAE Level4的自动驾驶车辆未通过此图灵测试。人机交互设计和真实场景研究在增强用户对系统的信任和推动技术应用方面至关重要。未来研究应关注真实场景中的人机信任和用户体验，以优化自动驾驶系统设计，提高技术的接受度。技术的发展将进一步推动人机关系演变，而深度的人机交互和用户信任感将是实现人机共生的关键因素。

关键词： 自动驾驶; 图灵测试; 人机共生; 人机信任; 情感评估

本文引用格式

库逸轩 . 迈向人机共生的人工智能：自动驾驶的乘客情感评估与图灵测试[J]. 科技导报, 2025 , 43(3) : 74 -80 . DOI: 10.3981/j.issn.1000-7857.2023.09.01356

参考文献

原文顺序 | 文献年度倒序 | 文中引用次数倒序

1	Licklider J C R . Man-computer symbiosis[J]. IRE Transactions on Human Factors in Electronics, 1960, HFE-1 (1): 4- 11. DOI

2	Inga J , Ruess M , Robens J H , et al. Human-machine symbiosis: A multivariate perspective for physically coupled human-machine systems[J]. International Journal of Human-Computer Studies, 2023, 170: 102926. DOI

3	Hyder A A , Paichadze N , Toroyan T , et al. Monitoring the decade of action for global road safety 2011-2020: An update[J]. Global Public Health, 2017, 12 (12): 1492- 1505. DOI

4	Djahel S , Doolan R , Muntean G M , et al. A communications-oriented perspective on traffic management systems for smart cities: Challenges and innovative approaches[J]. IEEE Communications Surveys & Tutorials, 2015, 17 (1): 125- 151.

5	Taeihagh A , Lim H S M . Governing autonomous vehicles: Emerging responses for safety, liability, privacy, cybersecurity, and industry risks[J]. Transport Reviews, 2019, 39 (1): 103- 128. DOI

6	Rojas-Rueda D , Nieuwenhuijsen M J , Khreis H , et al. Autonomous vehicles and public health[J]. Annual Review of Public Health, 2020, 41: 329- 345. DOI

7	Lim H S M , Taeihagh A . Algorithmic decision-making in AVs: Understanding ethical and technical concerns for smart cities[J]. Sustainability, 2019, 11 (20): 5791. DOI

8	Dartmann G , Song H B , Schmeink A . Big data analytics for cyber-physical systems: Machine learning for the internet of things[M]. Amsterdam: Elsevier, 2019.

9	Fleetwood J . Public health, ethics, and autonomous vehicles[J]. American Journal of Public Health, 2017, 107 (4): 532- 537. DOI

10	Kato S, Tokunaga S, Maruyama Y, et al. Autoware on board: Enabling autonomous vehicles with embedded systems[C]//Proceedings of ACM/IEEE 9th International Conference on Cyber-Physical Systems (ICCPS). Piscataway, NJ: IEEE, 2018: 287-296.

11	Chishiro H, Suito K, Ito T, et al. Towards heterogeneous computing platforms for autonomous driving[C]//Proceedings of IEEE International Conference on Embedded Software and Systems (ICESS). Piscataway, NJ: IEEE, 2019: 1-8.

12	Auger J . Living with robots: A speculative design approach[J]. Journal of Human-Robot Interaction, 2014, 3 (1): 20- 42. DOI

13	Robert E , Gary R , Grace B . Parsing the turing test, philosophical and methodological issues in the quest for the thinking computer[M]. Berlin: Springer, 2009.

14	Moor J H . The status and future of the turing test[J]. Minds and Machines, 2001, 11 (1): 77- 93. DOI

15	Cascetta E , Cartenì A , Di Francesco L . Do autonomous vehicles drive like humans? A Turing approach and an application to SAE automation Level 2 cars[J]. Transportation Research Part C: Emerging Technologies, 2022, 134: 103499. DOI

16	Zhang Y R , Hang P , Huang C , et al. Human-like interactive behavior generation for autonomous vehicles: A Bayesian game-theoretic approach with turing test[J]. Advanced Intelligent Systems, 2022, 4 (5): 2100211. DOI

17	Li Z N , Jiang Q L , Wu Z M , et al. Towards human-compatible autonomous car: A study of non-verbal turing test in automated driving with affective transition modelling[J]. IEEE Transactions on Affective Computing, 2024, 15 (2): 478- 492. DOI

18	Marcum J . A statistical theory of target detection by pulsed radar[J]. IRE Transactions on Information Theory, 1960, 6 (2): 59- 267. DOI

19	Tanner W P Jr , Swets J A . A decision-making theory of visual detection[J]. Psychological Review, 1954, 61 (6): 401- 409. DOI

20	Li S, Zhao Z, Hu R, et al. Analogical reasoning on chinese morphological and semantic relations[J/OL]. arXiv, 2018, doi: 10.48550/arxiv.1805.06504.

21	Clark K, Luong M T, Le Q V, et al. ELECTRA: Pre-training text encoders as discriminators rather than generators[J/OL]. arXiv, 2020, doi: 10.48550/arxiv.2003.10555.

22	Raffel C , Shazeer N , Roberts A , et al. Exploring the limits of transfer learning with a unified text-to-text transformer[J]. Journal of Machine Learning Research, 2020, 21 (140): 1- 67.

23	Carroll J M . Human-Computer Interaction: Psychology as a science of design[J]. Annual Review of Psychology, 1997, 48: 61- 83. DOI

24	Naujoks F , Forster Y , Wiedemann K , et al. Advances in Human Aspects of Transportation: Proceedings of the AHFE 2016 International Conference on Human Factors in Transportation, July 27-31, 2016, Walt Disney World®, Florida, USA[M]. Cham: Springer International Publishing, 2017.

25	Merriman S E , Plant K L , Revell K M A , et al. Challenges for automated vehicle driver training: A thematic analysis from manual and automated driving[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2021, 76: 238- 268. DOI

26	Manchon J B , Bueno M , Navarro J . Calibration of trust in automated driving: A matter of initial level of trust and automated driving style?[J]. Human Factors, 2023, 65 (8): 1613- 1629. DOI

27	Yokoi R , Nakayachi K . Trust in autonomous cars: Exploring the role of shared moral values, reasoning, and emotion in safety-critical decisions[J]. Human Factors, 2021, 63 (8): 1465- 1484. DOI

28	Al-Shihabi T, Mourant R R. A framework for modeling human-like driving behaviors for autonomous vehicles in driving simulators[C]//Proceedings of the Fifth International Conference on Autonomous Agents. New York: ACM, 2001.

29	Al-Shihabi T , Mourant R R . Toward more realistic driving behavior models for autonomous vehicles in driving simulators[J]. Transportation Research Record: Journal of the Transportation Research Board, 2003, 1843 (1): 41- 49. DOI

30	Sun X , Li J P , Tang P Y , et al. Exploring personalised autonomous vehicles to influence user trust[J]. Cognitive Computation, 2020, 12 (6): 1170- 1186. DOI

31	de Beaucorps P, Streubel T, Verroust-Blondet A, et al. Decision-making for automated vehicles at intersections adapting human-like behavior[C]//Proceedings of IEEE Intelligent Vehicles Symposium (IV). Piscataway, NJ: IEEE, 2017: 212-217.

32	Oliveira L , Proctor K , Burns C G , et al. Driving style: How should an automated vehicle behave?[J]. Information, 2019, 10 (6): 219. DOI

33	Fu R , Li Z , Sun Q Y , et al. Human-like car-following model for autonomous vehicles considering the cut-in behavior of other vehicles in mixed traffic[J]. Accident Analysis & Prevention, 2019, 132: 105260.

34	Lehsing C, Jünger L, Bengler K. Don't drive me my way: Subjective perception of autonomous braking trajectories for pedestrian crossings[C]//Proceedings of the Tenth International Symposium on Information and Communication Technology-SoICT 2019. New York: ACM, 2019.

35	Schwarting W , Pierson A , Alonso-Mora J , et al. Social behavior for autonomous vehicles[J]. Proceedings of the National Academy of Sciences of the United States of America, 2019, 116 (50): 24972- 24978.

36	Kidwell B , Calhoun G L , Ruff H A , et al. Adaptable and adaptive automation for supervisory control of multiple autonomous vehicles[J]. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 2012, 56 (1): 428- 432. DOI

37	Hartwich F , Beggiato M , Krems J F . Driving comfort, enjoyment and acceptance of automated driving - effects of drivers' age and driving style familiarity[J]. Ergonomics, 2018, 61 (8): 1017- 1032. DOI

38	Stanton N A , Eriksson A , Banks V A , et al. Turing in the driver's seat: Can people distinguish between automated and manually driven vehicles?[J]. Human Factors and Ergonomics in Manufacturing & Service Industries, 2020, 30 (6): 418- 425.

39	Rossner P, Bullinger A C. HCI in Mobility, Transport, and Automotive Systems: First International Conference, Mobi-TAS 2019, Held as Part of the 21st HCI International Conference, HCII 2019, Orlando, FL, USA, July 26-31, 2019, Proceedings[C]. Cham: Springer International Publishing, 2019.

40	Dettmann A , Hartwich F , Roßner P , et al. Comfort or not? automated driving style and user characteristics causing human discomfort in automated driving[J]. International Journal of Human-Computer Interaction, 2021, 37 (4): 331- 339. DOI

41	Fuest T, Michalowski L, Träris L, et al. Using the driving behavior of an automated vehicle to communicate intentions-A wizard of oz study[C]//Proceedings of 21st International Conference on Intelligent Transportation Systems (ITSC). Piscataway, NJ: IEEE, 2018: 3596-3601.

42	Bazilinskyy P , Sakuma T , de Winter J . What driving style makes pedestrians think a passing vehicle is driving automatically?[J]. Applied Ergonomics, 2021, 95: 103428. DOI

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