The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
The Virtual Driving Instructor: Multi-Agent System Collaborating via Knowledge Graph for Scalable Driver Education
Open accessAbstract
This paper introduces the design, development, and deployment of a Virtual Driving Instructor (VDI) for enhanced driver education. The VDI provides personalized, real-time feedback to students in a driving simulator, addressing some of the limitations of traditional driver instruction. Employing a hybrid AI system, the VDI combines rule-based agents, learning-based agents, knowledge graphs, and Bayesian networks to assess and monitor student performance in a comprehensive manner. Implemented in multiple simulators at a driving school in Norway, the system aims to leverage AI and driving simulation to improve both the learning experience and the efficiency of instruction. Initial feedback from students has been largely positive, highlighting the effectiveness of this integration while also pointing to areas for further improvement. This work marks a significant stride in infusing technology into driver education, offering a scalable and efficient approach to instruction.
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“When used with LLMs, they can simulate complex learning and assessment scenarios requiring critical thinking and problem-solving skills, such as in driving education [63], programming education [64], and laboratory safety courses [65].”
From Promises and challenges of generative artificial intelligence for human learning (2024) · cites this paper as [63]
Citations
Cites
Language Models are Few-Shot Learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372
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Promises and challenges of generative artificial intelligence for human learning
Yan, L., Greiff, S., Teuber, Z., & Gašević, D. (2024) · Nature Human Behaviour · doi:10.1038/s41562-024-02004-5 · Cited by 499
“When used with LLMs, they can simulate complex learning and assessment scenarios requiring critical thinking and problemsolving skills, such as in driving education 63 , programming education 64 and laboratory safety courses 65 .”
Metadata
- Authors
- Rehm, J., Reshodko, I., Børresen, S. Z., & Gundersen, O. E.
- Affiliations
- Norwegian University of Science and Technology (NO)
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2024-03-24
- Volume
- 38
- Issue
- 21
- Pages
- 22806-22814
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W4393157183
- Cited by
- 9
- References
- 1
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 5
- FWCI
- 2.66
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Proceedings of the AAAI Conference on Artificial Intelligence (publishedVersion)
- Funders
- Norges Forskningsråd (296640)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Traffic and Road Safety
- Keywords
- driver training, driving simulator, multi-agent systems, knowledge graphs, Bayesian networks, rule-based agents, personalized feedback, student assessment, hybrid AI models
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- CoChat. (2026). Citation check: The Virtual Driving Instructor: Multi-Agent System Collaborating via Knowledge Graph for Scalable Driver Education. https://app.cochat.ai/papers/10.1609/aaai.v38i21.30315
- The paper
- Rehm, J., Reshodko, I., Børresen, S. Z., & Gundersen, O. E. (2024). The Virtual Driving Instructor: Multi-Agent System Collaborating via Knowledge Graph for Scalable Driver Education. Proceedings of the AAAI Conference on Artificial Intelligence. https://doi.org/10.1609/aaai.v38i21.30315
