Real-time driving risk assessment based on the psycho-physical field

References

• Araki, H., Yamada, K., Hiroshima, Y., & Ito, T. (1996). Development of rear-end collision avoidance system. In Proceedings of Conference on Intelligent Vehicles. doi:10.1109/IVS.1996.566382
   Google Scholar
• Chen, C., Zhao, X., Liu, H., Ren, G., & Liu, X. (2019). Influence of adverse weather on drivers’ perceived risk during car following based on driving simulations. Journal of Modern Transportation, 27(4), 282–292. doi:10.1007/s40534-019-00197-4
   Web of Science ®Google Scholar
• Choi, D., & Lee, S. (2021). Comparison of machine learning algorithms for predicting lane changing intent. International Journal of Automotive Technology, 22(2), 507–518. doi:10.1007/s12239-021-0047-x
   Web of Science ®Google Scholar
• Deng, T.-M., Fu, J.-H., Shao, Y.-M., Peng, J.-S., & Xu, J. (2019). Pedal operation characteristics and driving workload on slopes of mountainous road based on naturalistic driving tests. Safety Science, 119, 40–49. doi:10.1016/j.ssci.2018.10.011
   Web of Science ®Google Scholar
• Dingus, T. A., Klauer, S. G., & Neale, V. L. (2006). The 100-car naturalistic driving study, Phase II-results of the 100-car field experiment. Tech Report. Department of Transportation. National Highway Traffic Safety Administration.
   Google Scholar
• Evans, L. (1991). Traffic safety and the driver. Science Serving Society.
   Google Scholar
• Fernandes, A., & Neves, J. (2014). Threshold values of pavement surface properties for maintenance purposes based on accidents modelling. International Journal of Pavement Engineering, 15(10), 917–924. doi:10.1080/10298436.2014.893324
   Web of Science ®Google Scholar
• Fu, R., Guo, Y., Yuan, W., Feng, H., & Ma, Y. (2011). The correlation between gradients of descending roads and accident rates. Safety Science, 49(3), 416–423. doi:10.1016/j.ssci.2010.10.006
   Web of Science ®Google Scholar
• Gibson, J. J., & Crooks, L. E. (1938). A theoretical field-analysis of automobile-driving. The American Journal of Psychology, 51(3), 453–471. doi:10.2307/1416145
   Google Scholar
• Guo, F., & Fang, Y. (2013). Individual driver risk assessment using naturalistic driving data. Accident; Analysis and Prevention, 61, 3–9. doi:10.1016/j.aap.2012.06.014
   PubMed Web of Science ®Google Scholar
• Hayward, J. C. (1972). Near miss determination through use of a scale of danger. Highway Research Record (384).
   Google Scholar
• Hojjati-Emami, K., Dhillon, B. S., & Jenab, K. (2014). Stochastic risk assessment methodology and modeling as an in-vehicle safety enhancing tool for younger drivers on roads. Journal of Transportation Safety & Security, 6(4), 301–320. doi:10.1080/19439962.2013.874388
   Web of Science ®Google Scholar
• Jiang, K., Yang, D., Xie, S., Xiao, Z., Victorino, A. C., & Charara, A. (2019). Real-time estimation and prediction of tire forces using digital map for driving risk assessment. Transportation Research Part C: Emerging Technologies, 107, 463–489. doi:10.1016/j.trc.2019.08.016
   Web of Science ®Google Scholar
• Katrakazas, C., Quddus, M., Chen, W.-H., & Deka, L. (2015). Real-time motion planning methods for autonomous on-road driving: State-of-the-art and future research directions. Transportation Research Part C: Emerging Technologies, 60, 416–442. doi:10.1016/j.trc.2015.09.011
   Web of Science ®Google Scholar
• Khan, A., Bacchus, A., & Erwin, S. (2014). Surrogate safety measures as aid to driver assistance system design of the cognitive vehicle. IET Intelligent Transport Systems, 8(4), 415–424. doi:10.1049/iet-its.2013.0022
   Web of Science ®Google Scholar
• Kiefer, R. J., LeBlanc, D. J., & Flannagan, C. A. (2005). Developing an inverse time-to-collision crash alert timing approach based on drivers’ last-second braking and steering judgments. Accident; Analysis and Prevention, 37(2), 295–303. doi:10.1016/j.aap.2004.09.003
   PubMed Web of Science ®Google Scholar
• Kolekar, S., de Winter, J., & Abbink, D. (2020). Human-like driving behaviour emerges from a risk-based driver model. Nature Communications, 11(1), 1–13. doi:10.1038/s41467-020-18353-4
   PubMed Web of Science ®Google Scholar
• Kotseruba, I., & Tsotsos, J. K. (2021). Behavioral research and practical models of drivers’ attention.
   Google Scholar
• Liao, Y., Wang, M., Duan, L., & Chen, F. (2018). Cross-regional driver–vehicle interaction design: An interview study on driving risk perceptions, decisions, and ADAS function preferences. IET Intelligent Transport Systems, 12(8), 801–808. doi:10.1049/iet-its.2017.0241
   Web of Science ®Google Scholar
• Liu, H., Wei, H., Zuo, T., Li, Z., & Yang, Y. J. (2017). Fine-tuning ADAS algorithm parameters for optimizing traffic safety and mobility in connected vehicle environment. Transportation Research. Part C, Emerging Technologies, 76, 132–149. doi:10.1016/j.trc.2017.01.003
   PubMed Web of Science ®Google Scholar
• Lu, G., Cheng, B., Lin, Q., & Wang, Y. (2012). Quantitative indicator of homeostatic risk perception in car following. Safety Science, 50(9), 1898–1905. doi:10.1016/j.ssci.2012.05.007
   Web of Science ®Google Scholar
• Lu, C., He, X., van Lint, H., Tu, H., Happee, R., & Wang, M. (2021). Performance evaluation of surrogate measures of safety with naturalistic driving data. Accident; Analysis and Prevention, 162, 106403. doi:10.1016/j.aap.2021.106403
   PubMed Web of Science ®Google Scholar
• Lyu, N., Wen, J., Duan, Z., & Wu, C. (2020). Vehicle trajectory prediction and cut-in collision warning model in a connected vehicle environment. IEEE Transactions on Intelligent Transportation Systems, 23(2), 966–981.
   Web of Science ®Google Scholar
• Mammar, S., Glaser, S., & Netto, M. (2006). Time to line crossing for lane departure avoidance: A theoretical study and an experimental setting. IEEE Transactions on Intelligent Transportation Systems, 7(2), 226–241. doi:10.1109/TITS.2006.874707
   Web of Science ®Google Scholar
• Mobileye. (2010). Mobileye, 2010. C2-270 collision prevention system user manual. http://www.c2sec.com.sg/Files/Mobileye%20C2-270%20UserManual.pdf.
   Google Scholar
• Mullakkal-Babu, F. A., Wang, M., Farah, H., van Arem, B., & Happee, R. (2017). Comparative assessment of safety indicators for vehicle trajectories on highways. Transportation Research Record: Journal of the Transportation Research Board, 2659(1), 127–136. doi:10.3141/2659-14
   Google Scholar
• Mullakkal-Babu, F. A., Wang, M., He, X., van Arem, B., & Happee, R. (2020). Probabilistic field approach for motorway driving risk assessment. Transportation Research Part C: Emerging Technologies, 118, 102716. doi:10.1016/j.trc.2020.102716
   Web of Science ®Google Scholar
• Näätänen, R., & Summala, H. (1974). A model for the role of motivational factors in drivers' decision-making∗. Accident Analysis & Prevention, 6(3-4), 243–261. doi:10.1016/0001-4575(74)90003-7
   Google Scholar
• Ozbay, K., Yang, H., Bartin, B., & Mudigonda, S. (2008). Derivation and validation of new simulation-based surrogate safety measure. Transportation Research Record: Journal of the Transportation Research Board, 2083(1), 105–113. doi:10.3141/2083-12
   Web of Science ®Google Scholar
• Park, H., Oh, C., Moon, J., & Kim, S. (2018). Development of a lane change risk index using vehicle trajectory data. Accident; Analysis and Prevention, 110, 1–8. doi:10.1016/j.aap.2017.10.015
   PubMed Web of Science ®Google Scholar
• Peng, Y., Abdel-Aty, M., Shi, Q., & Yu, R. (2017). Assessing the impact of reduced visibility on traffic crash risk using microscopic data and surrogate safety measures. Transportation Research Part C: Emerging Technologies, 74, 295–305. doi:10.1016/j.trc.2016.11.022
   Web of Science ®Google Scholar
• Shalev-Shwartz, S., Shammah, S., & Shashua, A. (2017). On a formal model of safe and scalable self-driving cars. arXiv preprint arXiv:1708.06374.
   Google Scholar
• Sjöberg, L., Moen, B.-E., & Rundmo, T. (2004). Explaining risk perception. An evaluation of the psychometric paradigm in risk perception research. Rotunde publikasjoner Rotunde, 10(2), 665–612.
   Google Scholar
• Stahl, P., Donmez, B., & Jamieson, G. A. (2014). Anticipation in driving: The role of experience in the efficacy of pre-event conflict cues. IEEE Transactions on Human-Machine Systems, 44(5), 603–613. doi:10.1109/THMS.2014.2325558
   Web of Science ®Google Scholar
• Sun, J., Ouyang, J., & Yang, J. (2014). Modeling and analysis of merging behavior at expressway on-ramp bottlenecks. Transportation Research Record: Journal of the Transportation Research Board, 2421(1), 74–81. doi:10.3141/2421-09
   Google Scholar
• Sun, Q., Zhang, H., Li, Z., Wang, C., & Du, K. (2019). ADAS acceptability improvement based on self-learning of individual driving characteristics: A case study of lane change warning system. IEEE Access 7, 81370–81381. doi:10.1109/ACCESS.2019.2923822
   Web of Science ®Google Scholar
• Takahashi, R., Kobayashi, M., Sasaki, T., Yokokawa, Y., Momose, H., & Ohhashi, T. (2017). Driving simulation test for evaluating hazard perception: Elderly driver response characteristics. Transportation Research Part F: Traffic Psychology and Behaviour, 49, 257–270. doi:10.1016/j.trf.2017.07.003
   Web of Science ®Google Scholar
• Trick, L. M., & Enns, J. T. (2009). A two-dimensional framework for understanding the role of attentional selection in driving (Human factors of visual and cognitive performance in driving (pp. 63–73). CRC Press.
   Google Scholar
• Wang, X., Alonso-Mora, J., & Wang, M. (2022). Probabilistic risk metric for highway driving leveraging multi-modal trajectory predictions. IEEE Transactions on Intelligent Transportation Systems, 23(10), 19399–19412. doi:10.1109/TITS.2022.3164469
   Web of Science ®Google Scholar
• Wang, J., Huang, H., Li, Y., Zhou, H., Liu, J., & Xu, Q. (2020). Driving risk assessment based on naturalistic driving study and driver attitude questionnaire analysis. Accident; Analysis and Prevention, 145, 105680. doi:10.1016/j.aap.2020.105680
   PubMed Web of Science ®Google Scholar
• Wang, J., Wu, J., & Li, Y. (2015). The driving safety field based on driver–vehicle–road interactions. IEEE Transactions on Intelligent Transportation Systems, 16(4), 2203–2214. doi:10.1109/TITS.2015.2401837
   Web of Science ®Google Scholar
• Wang, J., Wu, J., Zheng, X., Ni, D., & Li, K. (2016). Driving safety field theory modeling and its application in pre-collision warning system. Transportation Research Part C: Emerging Technologies, 72, 306–324. doi:10.1016/j.trc.2016.10.003
   Web of Science ®Google Scholar
• Wang, C., Xie, Y., Huang, H., & Liu, P. (2021). A review of surrogate safety measures and their applications in connected and automated vehicles safety modeling. Accident; Analysis and Prevention, 157, 106157. doi:10.1016/j.aap.2021.106157
   PubMed Web of Science ®Google Scholar
• Yan, L., Huang, Z., Zhang, Y., Zhang, L., Zhu, D., & Ran, B. (2017). Driving risk status prediction using Bayesian networks and logistic regression. IET Intelligent Transport Systems, 11(7), 431–439. doi:10.1049/iet-its.2016.0207
   Web of Science ®Google Scholar
• Ye, Y. (2018). The accelerated test methods and system for automated vehicle based on simulation environment [Master thesis, Tongji University].
   Google Scholar
• Zhang, D., Chen, X., Wang, J., Wang, Y., & Sun, J. (2021). A comprehensive comparison study of four classical car-following models based on the large-scale naturalistic driving experiment. Simulation Modelling Practice and Theory, 113, 102383. doi:10.1016/j.simpat.2021.102383
   Web of Science ®Google Scholar
• Zhang, H., Qian, D., Yang, X., & Shao, C. (2019). The impact of speech-based texting on drivers’ braking reaction and deceleration duration in the car-following scenario. Journal of Transportation Safety & Security, 11(5), 520–543. doi:10.1080/19439962.2018.1436106
   Web of Science ®Google Scholar
• Zhao, X., He, R., & Wang, J. (2020). How do drivers respond to driving risk during car-following? Risk-response driver model and its application in human-like longitudinal control. Accident; Analysis and Prevention, 148, 105783. doi:10.1016/j.aap.2020.105783
   PubMed Web of Science ®Google Scholar