Towards a Cutting-Edge Criteria for Aggressive Driving Behavior Recognition
Resumo
Driver behavior (DB) plays an important role in both road safety and vehicle energy management. Better understanding of how drivers respond to actual driving environments, as well as how their driving style affects road traffic safety, fosters the development of driver behavior monitoring systems that enhance Intelligent Transportation Systems (ITS). However, a literature review shows a lack of integrated and robust systems capable of providing reliable driving assistance in the field that accounts for the uniqueness in driving style and risk profiling assessments. This work aims to showcase a research taxonomy1 for aggressive driving behavior recognition, which relies on a context-aware, personalized, and real-time assessment. These findings will be used to highlight the importance of incorporating contextual and personalized assessments into driver behavior monitoring systems, paving the way for more intelligent and proactive driving assistance solutions.
Palavras-chave:
Aggressive Driving Behavior, Connected Vehicle Data, Machine Learning, Road Safety, Systematic Review Mapping, Intelligent Transportation Systems, Contextual-Awareness, Personalized Feedback, Real-time Prediction
Referências
Sohail, A., Cheema, M., Ali, M., Toosi, A. & Rakha, H. Data-driven approaches for road safety: A comprehensive systematic literature review. Safety Science. 158 pp. 105949, 2023.
WHO Decade of Action for Road Safety 2021-2030. World Health Organization, 2022.
Tarlochan, F. & Mohammed, S. Intelligent Transportation System: Application of Telematics Data for Road Safety. 2023 International Conference On Information Management (ICIM). pp. 66-71, 2023.
Elamrani Abou Elassad, Z., Mousannif, H., Al Moatassime, H. & Karkouch, A. The application of machine learning techniques for driving behavior analysis: A conceptual framework and a systematic literature review. Engineering Applications Of Artificial Intelligence. 87 pp. 103312, 2020.
Marina Martinez, C., Heucke, M., Wang, F., Gao, B. & Cao, D. Driving Style Recognition for Intelligent Vehicle Control and Advanced Driver Assistance: A Survey. IEEE Transactions On Intelligent Transportation Systems. 19, 666-676, 2018.
Gheni, H. & Abdul-Rahaim, L. A Survey on Driver Behavior Detection-Based Internet of Vehicle: Issues, Challenges, Motivation and Recommendations. 2022 2nd International Conference On Advances In Engineering Science And Technology (AEST). pp. 345-350, 2022.
Kitchenham, B. & Charters, S. Guidelines for performing Systematic Literature Reviews in Software Engineering. EBSE Technical Report. 2, 2007.
Petersen, K., Vakkalanka, S. & Kuzniarz, L. Guidelines for conducting systematic mapping studies in software engineering: An update. Information And Software Technology. 64 pp. 1-18, 2015.
Lee, J. & Jang, K. A framework for evaluating aggressive driving behaviors based on in-vehicle driving records. Transportation Research Part F: Traffic Psychology And Behaviour. 65 pp. 610-619 (2019,8)
Liu, J., Liu, Y., Tian, C., Wei, D., Zhao, M., Ni, W., Zeng, X. & Song, L. A Survey of Recent Advances in Driving Behavior Analysis. 2021 3rd International Symposium On Smart And Healthy Cities (ISHC). pp. 145-157, 2021.
Menegazzo, J. & Wangenheim, A. Vehicular Perception Based on Inertial Sensing: A Structured Mapping of Approaches and Methods. SN Computer Science. 1, 2020.
Chu, H., Zhuang, H., Wang, W., Na, X., Guo, L., Zhang, J., Gao, B. & Chen, H. A Review of Driving Style Recognition Methods From Short-Term and Long-Term Perspectives. IEEE Transactions On Intelligent Vehicles. 8, 4599-4612, 2023.
Fugiglando, U., Massaro, E., Santi, P., Milardo, S., Abida, K., Stahlmann, R., Netter, F. & Ratti, C. Driving Behavior Analysis through CAN Bus Data in an Uncontrolled Environment. IEEE Transactions On Intelligent Transportation Systems. 20, 737-748, 2019.
Moukafih, Y., Hafidi, H. & Ghogho, M. Aggressive Driving Detection Using Deep Learning-based Time Series Classification. 2019 IEEE International Symposium On INnovations In Intelligent SysTems And Applications (INISTA). pp. 1-5, 2019.
Chen, J., Wu, Z. & Zhang, J. Driving Safety Risk Prediction Using Cost-Sensitive With Nonnegativity-Constrained Autoencoders Based on Imbalanced Naturalistic Driving Data. IEEE Transactions On Intelligent Transportation Systems. 20, 4450-4465, 2019.
Vyas, J., Bhardwaj, N., Bhumika & Das, D. TransDBC: Transformer for Multivariate Time-Series based Driver Behavior Classification. 2022 International Joint Conference On Neural Networks (IJCNN). pp. 1-8, 2022.
Abdelrahman, A., Hassanein, H. & Ali, N. A Robust Environment-Aware Driver Profiling Framework Using Ensemble Supervised Learning. IEEE Transactions On Intelligent Transportation Systems. 23, 14456-14469, 2022.
Zhu, Y., Jiang, M., Yamamoto, T., Ding, N., Shinkai, H., Aoki, H. & Shimazaki, K. A Framework for Combining Lateral and Longitudinal Acceleration to Assess Driving Styles Using Unsupervised Approach. IEEE Transactions On Intelligent Transportation Systems. 25, 638-656, 2024.
Chen, Z., Zhang, Y., Wu, C. & Ran, B. Understanding Individualization Driving States via Latent Dirichlet Allocation Model. IEEE Intelligent Transportation Systems Magazine. 11, 41-53, 2019.
Eftekhari, H. & Ghatee, M. A similarity-based neuro-fuzzy modeling for driving behavior recognition applying fusion of smartphone sensors. Journal Of Intelligent Transportation Systems. 23, 72-83, 2019.
Navneeth, S., Prithvil, K., Sri Hari, N., Thushar, R. & Rajeswari, M. On-Board Diagnostics and Driver Profiling. 2020 5th International Conference On Computing, Communication And Security (ICCCS). pp. 1-6, 2020.
Castignani, G., Derrmann, T., Frank, R. & Engel, T. Driver Behavior Profiling Using Smartphones: A Low-Cost Platform for Driver Monitoring. IEEE Intelligent Transportation Systems Magazine. 7, 91-102, 2015.
Martinelli, F., Mercaldo, F., Nardone, V., Orlando, A. & Santone, A. Context-Awareness Mobile Devices for Traffic Incident Prevention. 2018 IEEE International Conference On Pervasive Computing And Communications Workshops (PerCom Workshops). pp. 143-148, 2018.
Mohammadnazar, A., Arvin, R. & Khattak, A. Classifying travelers’ driving style using basic safety messages generated by connected vehicles: Application of unsupervised machine learning. Transportation Research Part C: Emerging Technologies. 122 pp. 102917, 2021.
Matousek, M., EL-Zohairy, M., Al-Momani, A., Kargl, F. & Bosch, C. Detecting Anomalous Driving Behavior using Neural Networks. 2019 IEEE Intelligent Vehicles Symposium (IV). pp. 2229-2235, 2019.
Yi, D., Su, J., Liu, C., Quddus, M. & Chen, W. A machine learning based personalized system for driving state recognition. Transportation Research Part C: Emerging Technologies. 105 pp. 241-261, 2019.
Marafie, Z., Lin, K., Wang, D., Lyu, H., Liu, Y., Meng, Y. & Ma, J. AutoCoach: An Intelligent Driver Behavior Feedback Agent with Personality-Based Driver Models. Electronics. 10, 1361, 2021.
He, B., Chen, X., Zhang, D., Liu, S., Han, D. & Ni, L. PBE: Driver Behavior Assessment Beyond Trajectory Profiling. Machine Learning And Knowledge Discovery In Databases. pp. 507-523, 2019.
WHO Decade of Action for Road Safety 2021-2030. World Health Organization, 2022.
Tarlochan, F. & Mohammed, S. Intelligent Transportation System: Application of Telematics Data for Road Safety. 2023 International Conference On Information Management (ICIM). pp. 66-71, 2023.
Elamrani Abou Elassad, Z., Mousannif, H., Al Moatassime, H. & Karkouch, A. The application of machine learning techniques for driving behavior analysis: A conceptual framework and a systematic literature review. Engineering Applications Of Artificial Intelligence. 87 pp. 103312, 2020.
Marina Martinez, C., Heucke, M., Wang, F., Gao, B. & Cao, D. Driving Style Recognition for Intelligent Vehicle Control and Advanced Driver Assistance: A Survey. IEEE Transactions On Intelligent Transportation Systems. 19, 666-676, 2018.
Gheni, H. & Abdul-Rahaim, L. A Survey on Driver Behavior Detection-Based Internet of Vehicle: Issues, Challenges, Motivation and Recommendations. 2022 2nd International Conference On Advances In Engineering Science And Technology (AEST). pp. 345-350, 2022.
Kitchenham, B. & Charters, S. Guidelines for performing Systematic Literature Reviews in Software Engineering. EBSE Technical Report. 2, 2007.
Petersen, K., Vakkalanka, S. & Kuzniarz, L. Guidelines for conducting systematic mapping studies in software engineering: An update. Information And Software Technology. 64 pp. 1-18, 2015.
Lee, J. & Jang, K. A framework for evaluating aggressive driving behaviors based on in-vehicle driving records. Transportation Research Part F: Traffic Psychology And Behaviour. 65 pp. 610-619 (2019,8)
Liu, J., Liu, Y., Tian, C., Wei, D., Zhao, M., Ni, W., Zeng, X. & Song, L. A Survey of Recent Advances in Driving Behavior Analysis. 2021 3rd International Symposium On Smart And Healthy Cities (ISHC). pp. 145-157, 2021.
Menegazzo, J. & Wangenheim, A. Vehicular Perception Based on Inertial Sensing: A Structured Mapping of Approaches and Methods. SN Computer Science. 1, 2020.
Chu, H., Zhuang, H., Wang, W., Na, X., Guo, L., Zhang, J., Gao, B. & Chen, H. A Review of Driving Style Recognition Methods From Short-Term and Long-Term Perspectives. IEEE Transactions On Intelligent Vehicles. 8, 4599-4612, 2023.
Fugiglando, U., Massaro, E., Santi, P., Milardo, S., Abida, K., Stahlmann, R., Netter, F. & Ratti, C. Driving Behavior Analysis through CAN Bus Data in an Uncontrolled Environment. IEEE Transactions On Intelligent Transportation Systems. 20, 737-748, 2019.
Moukafih, Y., Hafidi, H. & Ghogho, M. Aggressive Driving Detection Using Deep Learning-based Time Series Classification. 2019 IEEE International Symposium On INnovations In Intelligent SysTems And Applications (INISTA). pp. 1-5, 2019.
Chen, J., Wu, Z. & Zhang, J. Driving Safety Risk Prediction Using Cost-Sensitive With Nonnegativity-Constrained Autoencoders Based on Imbalanced Naturalistic Driving Data. IEEE Transactions On Intelligent Transportation Systems. 20, 4450-4465, 2019.
Vyas, J., Bhardwaj, N., Bhumika & Das, D. TransDBC: Transformer for Multivariate Time-Series based Driver Behavior Classification. 2022 International Joint Conference On Neural Networks (IJCNN). pp. 1-8, 2022.
Abdelrahman, A., Hassanein, H. & Ali, N. A Robust Environment-Aware Driver Profiling Framework Using Ensemble Supervised Learning. IEEE Transactions On Intelligent Transportation Systems. 23, 14456-14469, 2022.
Zhu, Y., Jiang, M., Yamamoto, T., Ding, N., Shinkai, H., Aoki, H. & Shimazaki, K. A Framework for Combining Lateral and Longitudinal Acceleration to Assess Driving Styles Using Unsupervised Approach. IEEE Transactions On Intelligent Transportation Systems. 25, 638-656, 2024.
Chen, Z., Zhang, Y., Wu, C. & Ran, B. Understanding Individualization Driving States via Latent Dirichlet Allocation Model. IEEE Intelligent Transportation Systems Magazine. 11, 41-53, 2019.
Eftekhari, H. & Ghatee, M. A similarity-based neuro-fuzzy modeling for driving behavior recognition applying fusion of smartphone sensors. Journal Of Intelligent Transportation Systems. 23, 72-83, 2019.
Navneeth, S., Prithvil, K., Sri Hari, N., Thushar, R. & Rajeswari, M. On-Board Diagnostics and Driver Profiling. 2020 5th International Conference On Computing, Communication And Security (ICCCS). pp. 1-6, 2020.
Castignani, G., Derrmann, T., Frank, R. & Engel, T. Driver Behavior Profiling Using Smartphones: A Low-Cost Platform for Driver Monitoring. IEEE Intelligent Transportation Systems Magazine. 7, 91-102, 2015.
Martinelli, F., Mercaldo, F., Nardone, V., Orlando, A. & Santone, A. Context-Awareness Mobile Devices for Traffic Incident Prevention. 2018 IEEE International Conference On Pervasive Computing And Communications Workshops (PerCom Workshops). pp. 143-148, 2018.
Mohammadnazar, A., Arvin, R. & Khattak, A. Classifying travelers’ driving style using basic safety messages generated by connected vehicles: Application of unsupervised machine learning. Transportation Research Part C: Emerging Technologies. 122 pp. 102917, 2021.
Matousek, M., EL-Zohairy, M., Al-Momani, A., Kargl, F. & Bosch, C. Detecting Anomalous Driving Behavior using Neural Networks. 2019 IEEE Intelligent Vehicles Symposium (IV). pp. 2229-2235, 2019.
Yi, D., Su, J., Liu, C., Quddus, M. & Chen, W. A machine learning based personalized system for driving state recognition. Transportation Research Part C: Emerging Technologies. 105 pp. 241-261, 2019.
Marafie, Z., Lin, K., Wang, D., Lyu, H., Liu, Y., Meng, Y. & Ma, J. AutoCoach: An Intelligent Driver Behavior Feedback Agent with Personality-Based Driver Models. Electronics. 10, 1361, 2021.
He, B., Chen, X., Zhang, D., Liu, S., Han, D. & Ni, L. PBE: Driver Behavior Assessment Beyond Trajectory Profiling. Machine Learning And Knowledge Discovery In Databases. pp. 507-523, 2019.
Publicado
24/11/2025
Como Citar
COSTA, Lucas; BRANCO, Kalinka.
Towards a Cutting-Edge Criteria for Aggressive Driving Behavior Recognition. In: WORKSHOP LATINOAMERICANO DE DEPENDABILIDADE E SEGURANÇA EM SISTEMAS VEICULARES (SSV), 2. , 2025, Campinas/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 41-44.
