Driving Lit
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Díaz-Álvarez, A., Clavijo, M., Jiménez, F., Talavera, E., & Serradilla, F. (2018). Modelling the human lane-change execution behaviour through Multilayer Perceptrons and Convolutional Neural Networks. Transportation Research Part F: Traffic Psychology and Behaviour, 56, 134–148. https://doi.org/10.1016/j.trf.2018.04.004
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Huo, D., Ma, J., & Chang, R. (2020). Lane-changing-decision characteristics and the allocation of visual attention of drivers with an angry driving style. Transportation Research Part F: Traffic Psychology and Behaviour, 71, 62–75. https://doi.org/10.1016/j.trf.2020.03.008
Jiang, L., Chen, D., Li, Z., & Wang, Y. (2022). Risk Representation, Perception, and Propensity in an Integrated Human Lane-Change Decision Model. IEEE Transactions on Intelligent Transportation Systems, 23(12), 23474–23487. IEEE Transactions on Intelligent Transportation Systems. https://doi.org/10.1109/TITS.2022.3207182
Kamaruddin, N., Abdul Rahman, A. W., Mohamad Halim, K. I., & Mohd Noh, M. H. I. (2018). Driver Behaviour State Recognition based on Speech. TELKOMNIKA (Telecommunication Computing Electronics and Control), 16(2), 852. https://doi.org/10.12928/telkomnika.v16i2.8416
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Kolekar, S., de Winter, J., & Abbink, D. (2020). Human-like driving behaviour emerges from a risk-based driver model. Nature Communications, 11(1), Article 1. https://data.4tu.nl/articles/dataset/Driver_s_Risk_Fields_DRF_-_Model_data/12705950/1. https://doi.org/10.1038/s41467-020-18353-4
Kujanpää, K., Baimukashev, D., Zhu, S., Azam, S., Munir, F., Alcan, G., & Kyrki, V. (2024). Challenges of Data-Driven Simulation of Diverse and Consistent Human Driving Behaviors (arXiv:2401.03236). arXiv. http://arxiv.org/abs/2401.03236
Lappi, O. (2022). Gaze Strategies in Driving–An Ecological Approach. Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.821440
Liang, Y., Reyes, M. L., & Lee, J. D. (2007). Real-Time Detection of Driver Cognitive Distraction Using Support Vector Machines. IEEE Transactions on Intelligent Transportation Systems, 8(2), 340–350. IEEE Transactions on Intelligent Transportation Systems. https://doi.org/10.1109/TITS.2007.895298
Liu, R., Zhao, X., Yuan, T., Li, H., Bu, T., Zhu, X., & Ma, J. (2024). A human-like response model for following vehicles in lane-changing scenario. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 238(4), 760–773. https://doi.org/10.1177/09544070221135384
Lu, C., Lu, H., Chen, D., Wang, H., Li, P., & Gong, J. (2023). Human-like decision making for lane change based on the cognitive map and hierarchical reinforcement learning. Transportation Research Part C: Emerging Technologies, 156, 104328. https://doi.org/10.1016/j.trc.2023.104328
Mamo, W. G., Alhajyaseen, W. K. M., Brijs, K., Dirix, H., Vanroelen, G., Hussain, Q., Brijs, T., & Ross, V. (2024). The impact of cognitive load on a lane change task (LCT) among male autistic individuals: A driving simulator study. Transportation Research Part F: Traffic Psychology and Behaviour, 106, 27–43. https://doi.org/10.1016/j.trf.2024.07.030
Markkula, G., Boer, E., Romano, R., & Merat, N. (2018a). Sustained sensorimotor control as intermittent decisions about prediction errors: Computational framework and application to ground vehicle steering. Biological Cybernetics, 112(3), 181–207. https://doi.org/10.1007/s00422-017-0743-9
Markkula, G., Boer, E., Romano, R., & Merat, N. (2018b). Sustained sensorimotor control as intermittent decisions about prediction errors: Computational framework and application to ground vehicle steering. Biological Cybernetics, 112(3), 181–207. https://doi.org/10.1007/s00422-017-0743-9
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Muslim, H., Itoh, M., Liang, C. K., Antona-Makoshi, J., & Uchida, N. (2021). Effects of gender, age, experience, and practice on driver reaction and acceptance of traffic jam chauffeur systems. Scientific Reports, 11(1), 17874. https://doi.org/10.1038/s41598-021-97374-5
Pekkanen, J., Lappi, O., Rinkkala, P., Tuhkanen, S., Frantsi, R., & Summala, H. (2018). A computational model for driver’s cognitive state, visual perception and intermittent attention in a distracted car following task. Royal Society Open Science, 5(9), 180194. https://doi.org/10.1098/rsos.180194
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Rondora, M. E. S., Pirdavani, A., & Larocca, A. P. C. (2022). Driver Behavioral Classification on Curves Based on the Relationship between Speed, Trajectories, and Eye Movements: A Driving Simulator Study. Sustainability, 14(10), Article 10. https://doi.org/10.3390/su14106241
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Salvucci, D. D., Mandalia, H. M., Kuge, N., & Yamamura, T. (2007). Lane-Change Detection Using a Computational Driver Model. Human Factors, 49(3), 532–542. https://doi.org/10.1518/001872007X200157
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Shimada, H., Uemura, K., Makizako, H., Doi, T., Lee, S., & Suzuki, T. (2016). Performance on the flanker task predicts driving cessation in older adults. International Journal of Geriatric Psychiatry, 31(2), 169–175. https://doi.org/10.1002/gps.4308
Srinivasan, A. R., Hasan, M., Lin, Y.-S., Leonetti, M., Billington, J., Romano, R., & Markkula, G. (2021). Comparing merging behaviors observed in naturalistic data with behaviors generated by a machine learned model. 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), 3787–3792. https://doi.org/10.1109/ITSC48978.2021.9564791
Tan, X., & Zhang, Y. (2024). A Computational Cognitive Model of Driver Response Time for Scheduled Freeway Exiting Takeovers in Conditionally Automated Vehicles. Human Factors, 66(5), 1583–1599. https://doi.org/10.1177/00187208221143028
Tango, F., & Botta, M. (2013). Real-Time Detection System of Driver Distraction Using Machine Learning. IEEE Transactions on Intelligent Transportation Systems, 14(2), 894–905. IEEE Transactions on Intelligent Transportation Systems. https://doi.org/10.1109/TITS.2013.2247760
Tawari, A., & Kang, B. (2017). A computational framework for driver’s visual attention using a fully convolutional architecture. 2017 IEEE Intelligent Vehicles Symposium (IV), 887–894. https://doi.org/10.1109/IVS.2017.7995828
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Wang, B., Duan, H., Feng, Y., Chen, X., Fu, Y., Mo, Z., & Di, X. (2024). Can LLMs Understand Social Norms in Autonomous Driving Games? (arXiv:2408.12680). arXiv. http://arxiv.org/abs/2408.12680
Wang, W., Zhao, D., Han, W., & Xi, J. (2018). A Learning-Based Approach for Lane Departure Warning Systems With a Personalized Driver Model. IEEE Transactions on Vehicular Technology, 67(10), 9145–9157. IEEE Transactions on Vehicular Technology. https://doi.org/10.1109/TVT.2018.2854406
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Xing, Y., Lv, C., Cao, D., & Hang, P. (2021). Toward human-vehicle collaboration: Review and perspectives on human-centered collaborative automated driving. Transportation Research Part C: Emerging Technologies, 128, 103199. https://doi.org/10.1016/j.trc.2021.103199
Xing, Y., Lv, C., Wang, H., Wang, H., Ai, Y., Cao, D., Velenis, E., & Wang, F.-Y. (2019). Driver Lane Change Intention Inference for Intelligent Vehicles: Framework, Survey, and Challenges. IEEE Transactions on Vehicular Technology, 68(5), 4377–4390. IEEE Transactions on Vehicular Technology. https://doi.org/10.1109/TVT.2019.2903299
Yue, J., Manocha, D., & Wang, H. (2022). Human Trajectory Prediction via Neural Social Physics. In S. Avidan, G. Brostow, M. Cissé, G. M. Farinella, & T. Hassner (Eds.), Computer Vision – ECCV 2022 (Vol. 13694, pp. 376–394). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-19830-4_22
Zgonnikov, A., Abbink, D., & Markkula, G. (2024). Should I Stay or Should I Go? Cognitive Modeling of Left-Turn Gap Acceptance Decisions in Human Drivers. Human Factors, 66(5), 1399–1413. https://doi.org/10.1177/00187208221144561
Zhao, D., Lam, H., Peng, H., Bao, S., LeBlanc, D. J., Nobukawa, K., & Pan, C. S. (2017). Accelerated Evaluation of Automated Vehicles Safety in Lane-Change Scenarios Based on Importance Sampling Techniques. IEEE Transactions on Intelligent Transportation Systems, 18(3), 595–607. IEEE Transactions on Intelligent Transportation Systems. https://doi.org/10.1109/TITS.2016.2582208