AI-Based Mobile Coaching Architectures for Driver Behaviour and Road Safety: A Systematic Mapping and Design Framework
DOI:
https://doi.org/10.3991/ijim.v20i08.59843Keywords:
AI (Artificial Intelligence), mobile driver coaching, road safety, telematics, dependability, PRISMAAbstract
A systematic mapping of artificial intelligence (AI)-based mobile driver coaching for behavior change and road safety in land transportation has been conducted following PRISMA, resulting in nine empirical studies (n = 9). The evidence is organized through a taxonomy covering intervention goals, AI techniques, data sources, deployment patterns, outcome and engagement metrics, and privacy/dependability safeguards. Across the mapped interventions, a substantial share targets specific risky behaviors, particularly phone-related distraction, reporting relative reductions of up to 21% versus controls in controlled evaluations. Deployment designs are predominantly hybrid, combining on-device inference for timely detection with server-side components for aggregation and program features. Based on the extracted evidence, a system-oriented design framework and practical guidelines are proposed to support researchers and mobile system designers in selecting architectures and evaluation metrics for interactive driver coaching. Key limitations include heterogeneous outcome definitions and predominantly short- to medium-term follow-up, which constrain comparability and generalization across settings.
References
[1] R. Goel, “Effectiveness of road safety interventions: An evidence and gap map,” Campbell Systematic Reviews, 2024. https://doi.org/10.1002/cl2.1367
[2] K. Bhalla, D. Mohan, and B. O’Neill, “How much would low- and middle-income countries benefit from addressing the key risk factors of road traffic injuries?,” International Journal of Injury Control and Safety Promotion, vol. 27, no. 1, pp. 83–90, 2020. https://doi.org/10.1080/17457300.2019.1708411
[3] S. Waring, “Examining the effectiveness of an education-based road safety intervention,” Transportation Research Part F: Traffic Psychology and Behaviour, 2024. https://doi.org/10.1016/j.trf.2024.07.019
[4] S. Von and A. Bresges, “Effectiveness of educational interventions in minimizing reactance in traffic accident prevention,” Frontiers in Education, 2023. https://doi.org/10.3389/feduc.2023.1276380
[5] E. Mantouka, E. Barmpounakis, E. Vlahogianni, and J. Golias, “Smartphone sensing for understanding driving behavior: Current practice and challenges,” International Journal of Transportation Science and Technology, 2020. https://doi.org/10.1016/j.ijtst.2020.07.001
[6] A. Kashevnik, I. Lashkov, and A. Gurtov, “Methodology and mobile application for driver behavior analysis and accident prevention,” IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 6, pp. 2427–2436, 2020. https://doi.org/10.1109/TITS.2019.2918328
[7] D. Levshun, A. Chechulin, and I. Kotenko, “Security and privacy analysis of smartphone-based driver monitoring systems from the developer’s point of view,” Sensors, vol. 22, no. 13, 2022. https://doi.org/10.3390/s22135063
[8] W. Feng, S. Yang, Y. Gao, N. Zhang, R. Ning, and S. Lin,“Reverse offloading for latency minimization in vehicular edge computing,” in Proc. IEEE Int. Conf. Communications (ICC), 2021, pp. 1–6. https://doi.org/10.1109/ICC42927.2021.9500532
[9] J. Yu and X. Yu, “Assessing the user acceptance of mobility-as-a-service platforms: A case study of Shenzhen, China,” Industrial Management & Data Systems, 2025. https://doi.org/10.1108/imds-10-2024-1017
[10] S. Haghshenas, V. Astarita, S. Shaffiee Haghshenas, and G. Guido, “Artificial intelligence-powered driver behavior analysis for fuel consumption optimization: A pathway to greener roads,” in Proc. 10th Int. Conf. Control, Decision and Information Technologies (CoDIT), 2024, pp. 116–122. https://doi.org/10.1109/CoDIT62066.2024.10708512
[11] K. Yuan, Y. Huang, L. Guo, H. Chen, and J. Chen, “Human feedback enhanced autonomous intelligent systems: A perspective from intelligent driving,” Autonomous Intelligent Systems, vol. 4, 9, 2024. https://doi.org/10.1007/s43684-024-00071-z
[12] Z. Marafie et al., “AutoCoach: An intelligent driver behavior feedback agent with personality-based driver models,” Electronics, vol. 10, no. 11, 1361, 2021. https://doi.org/10.3390/electronics10111361
[13] Y. Takefuji, M. Tano, M. Shigehara, and S. Sato, “Artificial intelligence abnormal driving behavior detection for mitigating traffic accidents,” Computers & Industrial Engineering, vol. 198, 110667, 2024. https://doi.org/10.1016/j.cie.2024.110667
[14] W. Wu, L. He, W. Lin, and R. Mao, “Accelerating federated learning over reliability-agnostic clients in mobile edge computing systems,” IEEE Transactions on Parallel and Distributed Systems, vol. 32, no. 6, pp. 1539–1551, 2021. https://doi.org/10.1109/TPDS.2020.3040867
[15] H. Hu, L. Wang, Q. Hu, Y. Bu, and Y. Zhang, “Deep-learning-based mobile group intelligence perception mechanism oriented to user privacy and data security in the Internet of Things,” IEEE Wireless Communications, vol. 29, no. 3, pp. 60–67, 2022. https://doi.org/10.1109/MWC.007.2100444
[16] Y. Peng, G. Song, M. Guo, L. Wu, and L. Yu, “Investigating the impact of environmental and temporal features on mobile phone distracted driving behavior using phone use data,” Accident Analysis & Prevention, vol. 180, 106925, 2023. https://doi.org/10.1016/j.aap.2022.106925
[17] A. Siddiqua, S. Sabeer, R. S. Rao, S. Ahuja, S. Aggarwal, and M. Lourens, “Machine learning-driven educational ethics considerations: Striking a balance between privacy and personalization,” in Proc. 10th IEEE Uttar Pradesh Section Int. Conf. Electrical, Electronics and Computer Engineering (UPCON), 2023, pp. 1748–1753. https://doi.org/10.1109/UPCON59197.2023.10434880
[18] W. Ejaz, M. Sajjad, S. Ali, P. Singh, M. A. Jan, A. Qayyum, and A. Almogren, “Generative AI driven edge cloud system for intelligent road infrastructure inspection,” ICT Express, vol. 11, no. 3, pp. 325–333, 2025. https://doi.org/10.1016/j.icte.2025.06.004
[19] K. Hussain, C. Moreira, J. Pereira, S. Jardim, and J. Jorge, “A comprehensive literature review on modular approaches to autonomous driving: Deep learning for road and racing scenarios,” Smart Cities, vol. 8, no. 3, 79, 2025. https://doi.org/10.3390/smartcities8030079
[20] J. Li, R. Xu, J. Ma, Q. Zou, J. Ma, and H. Yu, “Domain adaptive object detection for autonomous driving under foggy weather,” in Proc. 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2023, pp. 612–622. https://doi.org/10.1109/WACV56688.2023.00068
[21] T. Zhang, K. Luo, C. P. Phoo, Y. You, W.-L. Chao, B. Hariharan, M. Campbell, and K. Q. Weinberger, “Unsupervised Domain Adaptation for Self-Driving from Past Traversal Features,” in Proc. IEEE/CVF Int. Conf. Comput. Vis. Workshops (ICCVW), 2023, pp. 4040–4046. https://doi.org/10.48550/arXiv.2309.12140
[22] T. Fonseca and S. Ferreira, “Monitoring technologies for truck drivers: A systematic review of safety and driving behavior,” Applied Sciences, vol. 15, no. 12, 6513, 2025. https://doi.org/10.3390/app15126513
[23] V. Shirole, A. K. Shahade, and P. V. Deshmukh, “A comprehensive review on data-driven driver behaviour scoring in vehicles: Technologies, challenges and future directions,” Discover Artificial Intelligence, 2025. https://doi.org/10.1007/s44163-025-00244-6
[24] L. Martinengo et al., “Spaced digital education for health professionals: Systematic review and meta-analysis,” Journal of Medical Internet Research, vol. 26, e57760, 2024. https://doi.org/10.2196/57760
[25] W. K. Monib, A. Qazi, and R. A. Apong, “Microlearning beyond boundaries: A systematic review and a novel framework for improving learning outcomes,” Heliyon, vol. 11, no. 2, 2025. https://doi.org/10.1016/j.heliyon.2024.e41413
[26] Q. Zhong, J. Zhi, Y. Xu, P. Gao, and S. Feng, “Assessing driver distraction from in-vehicle information system: An on-road study exploring the effects of input modalities and secondary task types,” Scientific Reports, vol. 14, Article 20289, 2024. https://doi.org/10.1038/s41598-024-71226-4
[27] L. Y. Tan, S. Hu, D. J. Yeo, and K. H. Cheong, “Artificial intelligence-enabled adaptive learning platforms: A review,” Computers and Education: Artificial Intelligence, 2025, Article 100429. https://doi.org/10.1016/j.caeai.2025.100429
[28] W. Strielkowski et al., “AI-driven adaptive learning for sustainable educational transformation,” Sustainable Development, vol. 33, no. 5, pp. 1–12, 2025. https://doi.org/10.1002/sd.3221
[29] M. Martínez, X. Rigueira, A. Ana, J. Martínez, I. Ocarranza, and D. Kreibel, “Impacto de la inteligencia artificial en los métodos de evaluación en la educación primaria y secundaria: Revisión sistemática de la literatura,” Revista de Psicodidáctica, vol. 28, no. 2, 2023. https://doi.org/10.1186/s41239-023-00320-2
[30] Y. Yan, H. Liu, and T. Chau, “A systematic review of AI ethics in education: Challenges, policy gaps, and future directions,” Journal of Global Information Management, vol. 33, no. 1, 2025. https://doi.org/10.4018/JGIM.386381
[31] Y. R. Marín et al., “Ethical challenges associated with the use of artificial intelligence in university education,” Journal of Academic Ethics, pp. 1–25, 2025. https://doi.org/10.1007/s10805-025-09660-w
[32] C. Zhai, S. Wibowo, and D. Li, “The effects of over-reliance on AI dialogue systems on students’ cognitive abilities: A systematic review,” Smart Learning Environments, vol. 11, Article 28, 2024. https://doi.org/10.1186/s40561-024-00316-7
[33] A. Arouj and A. M. Abdelmoniem, “Towards energy-aware federated learning via collaborative computing approach,” Computer Communications, vol. 221, pp. 131–141, 2024. Available: https://doi.org/10.1016/j.comcom.2024.04.012
[34] C. Chen et al., “Advances in robust federated learning: Heterogeneity, attacks and aggregations,” IEEE Transactions on Neural Networks and Learning Systems, 2025. https://arxiv.org/html/2405.09839v1
[35] N. Saeed et al., “Comprehensive review of federated learning challenges: Data, systems, fairness,” Journal of Big Data, vol. 12, Article 195, 2025. https://doi.org/10.1186/s40537-025-01195-6
[36] C. K. Y. Chan and W. Hu, “Students voices on generative AI: perceptions, benefits, and challenges in higher education,” International Journal of Educational Technology in Higher Education, vol. 20, no. 43, 2023. https://doi.org/10.1186/s41239-023-00411-8
[37] S. Wang, F. Wang, Z. Zhu, J. Wang, T. Tran, and Z. Du, “Artificial intelligence in education: A systematic literature review,” Expert Systems with Applications, vol. 252, 124167, 2024. https://doi.org/10.1016/j.eswa.2024.124167
[38] J. Passmore, B. Olafsson, and D. Tee, “A systematic literature review of artificial intelligence (AI) in coaching: Insights for future research and product development,” Journal of Work-Applied Management, 2025. https://doi.org/10.1108/JWAM-11-2024-0164
[39] M. Siami, M. Naderpour, and J. Lu, “A mobile telematics pattern recognition framework for driving behavior extraction,” IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 2, pp. 1459–1472, 2021. https://doi.org/10.1109/TITS.2020.2971214
[40] Y. Adeoye, E. F. Onotole, T. Ogunyankinnu, G. Aipoh, A. A. Osunkanmibi, and J. Egbemhenghe, “Artificial intelligence in logistics and distribution: The function of AI in dynamic route planning for transportation, including self-driving trucks and drone delivery systems,” World Journal of Advanced Research and Reviews, vol. 25, no. 2, pp. 155–167, 2025. https://doi.org/10.30574/wjarr.2025.25.2.0214
[41] N. C. Tiglao, N. M. Tiglao, E. Sanciangco, and M. A. Y. Tacderas, “Crowdsourcing and bus telematics for promoting fuel efficiency and eco-driving practices on the EDSA Busway,” Transportation Research Procedia, vol. 82, pp. 450–458, 2025. https://doi.org/10.1016/j.trpro.2025.03.045
[42] P. Ruer, S. Sénécal, M. Brodeur, F. Bouvier, A. Karran, M. Fredette, T. Châtel, and P.-M. Léger, “Improving driving behavior with an insurance telematics mobile application,” in Lecture Notes in Computer Science, 2020. https://doi.org/10.1007/978-3-030-59506-7_27
[43] T. Bobrovskaya, Y. A. Vasilev, N. Nikitin, A. V. Vladzimirskyy, O. Omelyanskaya, S. Chetverikov, and K. Arzamasov, “Sample size for assessing a diagnostic accuracy of AI-based software in radiology,” Siberian Journal of Clinical and Experimental Medicine, vol. 39, no. 4, pp. 122–130, 2024. https://doi.org/10.29001/2073-8552-2024-39-3-188-198
[44] C. Van Vliet, P. Bautrenaite, M. Yavari, A. McCracken, N. Palmbachs, B. Fontaine, and C. Put, “The Safer Driver App decreases mobile phone-induced distracted driving: Evidence from a randomized controlled trial,” Journal of Road Safety, vol. 35, no. 2, pp. 3–13, 2024. https://doi.org/10.33492/JRS-D-24-2-2133200
[45] L. Meuleners, M. Fraser, M. Stevenson, and P. Roberts, “Personalized driving safety: Using telematics to reduce risky driving behaviour among young drivers,” Journal of Safety Research, vol. 86, pp. 164–173, 2023. https://doi.org/10.1016/j.jsr.2023.05.007
[46] M. Stevenson et al., “FEEDBACK trial: A randomized control trial to investigate the effect of personalized feedback and financial incentives on reducing the incidence of road crashes,” BMC Public Health, vol. 23, Article 2035, 2023. https://doi.org/10.1186/s12889-023-16886-z
[47] D. Vankov, R. Schroeter, and A. Rakotonirainy, “Provisional drivers intend to speed less: The positive outcome for young drivers of a safe-driving app randomized trial,” Transportation Research Interdisciplinary Perspectives, vol. 21, Article 100877, 2023. https://doi.org/10.1016/j.trip.2023.100877
[48] M. K. Delgado, J. P. Ebert, R. A. Xiong, et al., “Feedback and financial incentives for reducing cell phone use while driving: A randomized clinical trial,” JAMA Network Open, vol. 7, no. 7, pp. 1–13, 2024. https://doi.org/10.1001/jamanetworkopen.2024.20218
[49] E. Papatheocharous, C. Kaiser, J. Moser, and A. Stocker, “Monitoring distracted driving behaviours with smartphones: An extended systematic literature review,” Sensors, vol. 23, no. 17, 7505, 2023. https://doi.org/10.3390/s23177505
[50] M. S. Horswill, A. Hill, L. Silapurem, and M. O. Watson, “A thousand years of crash experience in three hours: An online hazard perception training course for drivers,” Accident Analysis & Prevention, vol. 152, 2021. https://doi.org/10.1016/j.aap.2020.105969
[51] Z. Yang, Z. Zheng, J. Kim, and H. Rakha, “Eco-driving strategies using reinforcement learning for mixed traffic in the vicinity of signalized intersections,” Transportation Research Part C: Emerging Technologies, vol. 165, 104683, 2024. https://doi.org/10.1016/j.trc.2024.104683
[52] X. Shi, J. Zhang, X. Jiang, J. Chen, W. Hao, and B. Wang, “Learning eco-driving strategies from human driving trajectories,” Physica A: Statistical Mechanics and Its Applications, vol. 633, Article 129353, 2024. https://doi.org/10.1016/j.physa.2023.129353
[53] M. Hilario, P. Paredes, J. Mayhuasca, M. Liendo, and S. Martínez, “Evaluation of the impact of artificial intelligence on the systems audit process,” Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, vol. 15, no. 3, pp. 184–202, 2024. https://doi.org/10.58346/JOWUA.2024.I3.013
[54] A. Arouj and A. M. Abdelmoniem, “A systematic literature review of driver inattention and distraction,” International Journal of Interactive Mobile Technologies, vol. 16, no. 18, pp. 4–22, 2022. https://doi.org/10.3991/ijim.v16i18.33075
[55] T.-K. Nguyen, V. T. Pham, D. V. Nguyen, and L. Do, “Development of a smartphone application for safe car driving using Google API and built-in sensor,” International Journal of Interactive Mobile Technologies, vol. 14, no. 2, pp. 178–191, 2020. https://doi.org/10.3991/ijim.v14i02.11118
[56] Y. A. Alqudah, B. Sababha, E. Qaralleh, and T. Yousseff, “Machine learning to classify driving events using mobile phone sensors data,” International Journal of Interactive Mobile Technologies, vol. 15, no. 2, pp. 124–136, 2021. https://doi.org/10.3991/ijim.v15i02.18303
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