Human-centred design of next generation transportation infrastructure with connected and automated vehicles: a system-of-systems perspective

References

• Aksan, N., S. W. Anderson, J. Dawson, E. Uc, and M. Rizzo. 2015. “Cognitive Functioning Differentially Predicts Different Dimensions of Older Drivers’ on-Road Safety.” Accident; Analysis and Prevention 75: 236–244. doi:10.1016/j.aap.2014.12.007.
   PubMed Web of Science ®Google Scholar
• Alosco, M. L., M. B. Spitznagel, M. J. Cleveland, and J. Gunstad. 2013. “Cognitive Deficits Are Associated with Poorer Simulated Driving in Older Adults with Heart Failure.” BMC Geriatrics 13 (1): 58. doi:10.1186/1471-2318-13-58.
   PubMedGoogle Scholar
• Altché, F., and A. de La Fortelle. 2018. “An LSTM Network for Highway Trajectory Prediction.” In 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC). IEEE. http://arxiv.org/abs/1801.07962
   Google Scholar
• Amini, M. R., Y. Feng, Z. Yang, I. Kolmanovsky, and J. Sun. 2020. “Long-Term Vehicle Speed Prediction via Historical Traffic Data Analysis for Improved Energy Efficiency of Connected Electric Vehicles.” Transportation Research Record: Journal of the Transportation Research Board 2674 (11): 17–29. doi:10.1177/0361198120941508.
   Web of Science ®Google Scholar
• Anstey, K. J., J. Wood, S. Lord, and J. G. Walker. 2005. “Cognitive, Sensory and Physical Factors Enabling Driving Safety in Older Adults.” Clinical Psychology Review 25 (1): 45–65. doi:10.1016/j.cpr.2004.07.008.
   PubMed Web of Science ®Google Scholar
• Aoki, S., C.-W. Lin, and R. Rajkumar. 2021. “Human-Robot Cooperation for Autonomous Vehicles and Human Drivers: Challenges and Solutions.” IEEE Communications Magazine 59 (8): 35–41. doi:10.1109/MCOM.001.2001241.
   Web of Science ®Google Scholar
• Arteaga, C., A. Paz, and J. Park. 2020. “Injury Severity on Traffic Crashes: A Text Mining with an Interpretable Machine-Learning Approach.” Safety Science 132: 104988. doi:10.1016/j.ssci.2020.104988.
   Web of Science ®Google Scholar
• Ashraf, M. T., Dey, K. Mishra, S. Rahman, and M. T. 2021. “Extracting Rules from Autonomous-Vehicle-Involved Crashes by Applying Decision Tree and Association Rule Methods.” Transportation Research Record: Journal of the Transportation Research Board 2675 (11): 522–533. doi:10.1177/03611981211018461.
   Web of Science ®Google Scholar
• Baddeley, A. 1998. “Recent Developments in Working Memory.” Current Opinion in Neurobiology 8 (2): 234–238. doi:10.1016/S0959-4388(98)80145-1.
   PubMed Web of Science ®Google Scholar
• Bahamonde-Birke, F. J., B. Kickhöfer, D. Heinrichs, and T. Kuhnimhof. 2018. “A Systemic View on Autonomous Vehicles.” disP - The Planning Review 54 (3): 12–25. doi:10.1080/02513625.2018.1525197.
   Web of Science ®Google Scholar
• Bai, Z., B. Cai, W. Shangguan, and L. Chai. 2019. “Deep Learning Based Motion Planning for Autonomous Vehicle Using Spatiotemporal LSTM Network.” In 2018 Chinese Automation Congress (pp. 1610–1614). IEEE.
   Google Scholar
• Baldwin, C. L., and J. May. 2011. “Loudness Interacts with Semantics in Auditory Warnings to Impact Rear-End Collisions.” Transportation Research Part F: Traffic Psychology and Behaviour 14 (1): 36–42. doi:10.1016/j.trf.2010.09.004.
   Web of Science ®Google Scholar
• Barth, A., and U. Franke. 2010. “Tracking Oncoming and Turning Vehicles at Intersections.” In 13th International IEEE Conference on Intelligent Transportation Systems, 861–868. doi:10.1109/ITSC.2010.5624969.
   Google Scholar
• Barth, M., S. Mandava, K. Boriboonsomsin, and H. Xia. 2011. “Dynamic ECO-Driving for Arterial Corridors.” In 2011 IEEE Forum on Integrated and Sustainable Transportation System (FISTS), 182–188. doi:10.1109/FISTS.2011.5973594.
   Google Scholar
• Binder, M. D., N. Hirokawa, and U. Windhorst. 2009. Peripheral Vision. Berlin: Springer.
   Google Scholar
• Boggs, A. M., B. Wali, and A. J. Khattak. 2020. “Exploratory Analysis of Automated Vehicle Crashes in California: A Text Analytics & Hierarchical Bayesian Heterogeneity-Based Approach.” Accident; Analysis and Prevention 135: 105354. doi:10.1016/j.aap.2019.105354.
   PubMed Web of Science ®Google Scholar
• Bossaerts, P., and C. Murawski. 2017. “Computational Complexity and Human Decision-Making.” Trends in Cognitive Sciences 21 (12): 917–929. doi:10.1016/j.tics.2017.09.005.
   PubMed Web of Science ®Google Scholar
• Brookshear, J. G. 2011. Computer Science: An Overview. 11th ed. Reading, MA: Addison-Wesley.
   Google Scholar
• Cabani, I., G. Toulminet, and A. Bensrhair. 2008. “Contrast-Invariant Obstacle Detection System Using Color Stereo Vision.” In 2008 11th International IEEE Conference on Intelligent Transpor­tation Systems, 1032–1037. doi:10.1109/ITSC.2008.4732657.
   Google Scholar
• Cai, Q., M. Abdel-Aty, and J. Lee. 2017. “Macro-Level Vulnerable Road Users Crash Analysis: A Bayesian Joint Modeling Approach of Frequency and Proportion.” Accident; Analysis and Prevention 107: 11–19. doi:10.1016/j.aap.2017.07.020.
   PubMed Web of Science ®Google Scholar
• Carmona, J., C. Guindel, F. Garcia, and A. de la Escalera. 2021. “eHMI: Review and Guidelines for Deployment on Autonomous Vehicles.” Sensors 21 (9): 2912. doi:10.3390/s21092912.
   PubMed Web of Science ®Google Scholar
• Castro, C., ed. 2008. Human Factors of Visual and Cognitive Performance in Driving. 1st ed. Boca Raton, FL: CRC Press.
   Google Scholar
• Ceder, A., and A. D. May. 1976. “Further Evaluation of Single- and Two-Regime Traffic Flow Models.” Transportation Research Record 567, 1-14. https://trid.trb.org/view/46992.
   Google Scholar
• Cheng, J., Z. Xiang, T. Cao, and J. Liu. 2014. “Robust Vehicle Detection Using 3D Lidar under Complex Urban Environment.” In 2014 IEEE International Conference on Robotics and Automation (ICRA), 691–696. doi:10.1109/ICRA.2014.6906929.
   Google Scholar
• Choi, D., T. Sato, T. Ando, T. Abe, M. Akamatsu, and S. Kitazaki. 2020. “Effects of Cognitive and Visual Loads on Driving Performance after Take-over Request (TOR) in Automated Driving.” Applied Ergonomics 85: 103074. doi:10.1016/j.apergo.2020.103074.
   PubMed Web of Science ®Google Scholar
• Chou, F.-C., S. E. Shladover, and G. Bansal. 2016. “Coordinated Merge Control Based on V2V Communication.” In 2016 IEEE Vehicular Networking Conference (VNC), 1–8. doi:10.1109/VNC.2016.7835933.
   Google Scholar
• Clements, L. M., and K. M. Kockelman. 2017. “Economic Effects of Automated Vehicles.” Transportation Research Record: Journal of the Transportation Research Board 2606 (1): 106–114. doi:10.3141/2606-14.
   Google Scholar
• Colley, M., M. Walch, J. Gugenheimer, and E. Rukzio. 2019. “Including People with Impairments from the Start: External Communication of Autonomous Vehicles. In Proceedings of the 11th International Conference on Automotive User Interfaces and Interactive Vehicular Applications: Adjunct Proceedings, 307–314. doi:10.1145/3349263.3351521.
   Google Scholar
• Dalipi, A. F., D. Liu, X. Guo, Y. Chen, and C. Mousas. 2020. “VR-PAVIB: The Virtual Reality Pedestrian-Autonomous Vehicle Interaction Benchmark.” In 12th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, 38–41. doi:10.1145/3409251.3411718.
   Google Scholar
• Danescu, R., F. Oniga, and S. Nedevschi. 2011. “Modeling and Tracking the Driving Environment with a Particle-Based Occupancy Grid.” IEEE Transactions on Intelligent Transportation Systems 12 (4): 1331–1342. doi:10.1109/TITS.2011.2158097.
   Web of Science ®Google Scholar
• Das, S., A. Dutta, T. Lindheimer, M. Jalayer, and Z. Elgart. 2019. “YouTube as a Source of Information in Understanding Autonomous Vehicle Consumers: Natural Language Processing Study.” Transportation Research Record: Journal of the Transportation Research Board 2673 (8): 242–253. doi:10.1177/0361198119842110.
   Web of Science ®Google Scholar
• Deb, S., L. J. Strawderman, and D. W. Carruth. 2018. “Investigating Pedestrian Suggestions for External Features on Fully Autonomous Vehicles: A Virtual Reality Experiment.” Transportation Research Part F: Traffic Psychology and Behaviour 59: 135–149. doi:10.1016/j.trf.2018.08.016.
   Web of Science ®Google Scholar
• Di, X., and R. Shi. 2021. “A Survey on Autonomous Vehicle Control in the Era of Mixed-Autonomy: From Physics-Based to AI-Guided Driving Policy Learning.” Transportation Research Part C: Emerging Technologies 125: 103008. doi:10.1016/j.trc.2021.103008.
   Web of Science ®Google Scholar
• Dolgov, D., S. Thrun, M. Montemerlo, and J. Diebel. 2010. “Path Planning for Autonomous Vehicles in Unknown Semi-Structured Environments.” The International Journal of Robotics Research 29 (5): 485–501. doi:10.1177/0278364909359210.
   Web of Science ®Google Scholar
• Doroudgar, S., H. M. Chuang, P. J. Perry, K. Thomas, K. Bohnert, and J. Canedo. 2017. “Driving Performance Comparing Older versus Younger Drivers.” Traffic Injury Prevention 18 (1): 41–46. doi:10.1080/15389588.2016.1194980.
   PubMed Web of Science ®Google Scholar
• Dresner, K., and P. Stone. 2008. “A Multiagent Approach to Autonomous Intersection Management.” Journal of Artificial Intelligence Research 31: 591–656. doi:10.1613/jair.2502.
   Web of Science ®Google Scholar
• Dupuy, S., A. Egges, V. Legendre, and P. Nugues. 2001. “Generating a 3D Simulation of a Car Accident from a Written Description in Natural Language: The CarSim System. In The Workshop on Temporal and Spatial Information Processing. Vol. 13, 1–8. doi:10.3115/1118238.1118239.
   Google Scholar
• Eisma, Y. B., S. van Bergen, S. M. ter Brake, M. T. T. Hensen, W. J. Tempelaar, and J. C. F. de Winter. 2019. “External Human–Machine Interfaces: The Effect of Display Location on Crossing Intentions and Eye Movements.” Information 11 (1): 13. doi:10.3390/info11010013.
   Web of Science ®Google Scholar
• Eriksson, A., and N. A. Stanton. 2018. Driver Reactions to Automated Vehicles: A Practical Guide for Design and Evaluation. Boca Raton, FL: CRC Press.
   Google Scholar
• Eykholt, K., I. Evtimov, E. Fernandes, B. Li, A. Rahmati, C. Xiao, A. Prakash, T. Kohno, and D. Song. 2018. “Robust Physical-World Attacks on Deep Learning Models.” ArXiv:1707.08945 [Cs]. http://arxiv.org/abs/1707.08945
   Google Scholar
• Farsimadan, E., F. Palmieri, L. Moradi, D. Conte, and B. Paternoster. 2021. “Vehicle-to-Everything (V2X) Communication Scenarios for Vehicular Ad-Hoc Networking (VANET): An Overview.” In Computational Science and Its Applications – ICCSA 2021, edited by O. Gervasi, B. Murgante, S. Misra, A. M. A. C. Rocha, C. Torre, D. Taniar, B. O. Apduhan, and E. Stankova, 15–30. Cagliari, Italy: Springer. doi:10.1007/978-3-030-87010-2_2.
   Google Scholar
• Federal Highway Administration. 2012. Manual on Uniform Traffic Control Devices for Streets and Highways. Washington, D.C.
   Google Scholar
• Feng, Y., C. Yu, and H. X. Liu. 2018. “Spatiotemporal Intersection Control in a Connected and Automated Vehicle Environment.” Transportation Research Part C: Emerging Technologies 89: 364–383. doi:10.1016/j.trc.2018.02.001.
   Web of Science ®Google Scholar
• Feng, Y., C. Yu, S. Xu, H. X. Liu, and H. Peng. 2018. “An Augmented Reality Environment for Connected and Automated Vehicle Testing and Evaluation.” In 2018 IEEE Intelligent Vehicles Symposium (IV), 1549–1554. doi:10.1109/IVS.2018.8500545.
   Google Scholar
• Feng, Y., K. L. Head, S. Khoshmagham, and M. Zamanipour. 2015. “A Real-Time Adaptive Signal Control in a Connected Vehicle Environment.” Transportation Research Part C: Emerging Technologies 55: 460–473. doi:10.1016/j.trc.2015.01.007.
   Web of Science ®Google Scholar
• Fisher, D. L., M. Rizzo, J. Caird, and J. D. Lee, eds. 2011. Handbook of Driving Simulation for Engineering, Medicine, and Psychology. 1st ed. Boca Raton, FL: CRC Press.
   Google Scholar
• Foley, J. M., A. L. Gooding, A. D. Thames, M. L. Ettenhofer, M. S. Kim, S. A. Castellon, T. D. Marcotte, et al. 2013. “Visuospatial and Attentional Abilities Predict Driving Simulator Performance among Older HIV-Infected Adults.” American Journal of Alzheimer’s Disease and Other Dementias 28 (2): 185–194. doi:10.1177/1533317512473192.
   PubMed Web of Science ®Google Scholar
• Friedrich, B. 2016. “The Effect of Autonomous Vehicles on Traffic.” In Autonomous Driving: Technical, Legal and Social Aspects, edited by M. Maurer, J. C. Gerdes, B. Lenz, and H. Winner, 317–334. Berlin: Springer. doi:10.1007/978-3-662-48847-8_16.
   Google Scholar
• Garcia, K. R., S. Mishler, Y. Xiao, C. Wang, B. Hu, J. D. Still, and J. Chen. 2022. “Drivers’ Understanding of Artificial Intelligence in Automated Driving Systems: A Study of a Malicious Stop Sign.” Journal of Cognitive Engineering and Decision Making 16 (4): 237–251. doi:10.1177/15553434221117001.
   Web of Science ®Google Scholar
• Ge, J. I., and G. Orosz. 2017. “Optimal Control of Connected Vehicle Systems with Communication Delay and Driver Reaction Time.” IEEE Transactions on Intelligent Transportation Systems 18 (8): 2056–2070. doi:10.1109/TITS.2016.2633164.
   Web of Science ®Google Scholar
• Ghiasi, A., O. Hussain, Z. S. Qian, and X. Li. 2017. “A Mixed Traffic Capacity Analysis and Lane Management Model for Connected Automated Vehicles: A Markov Chain Method.” Transportation Research Part B: Methodological 106: 266–292. doi:10.1016/j.trb.2017.09.022.
   Web of Science ®Google Scholar
• Gipps, P. G. 1981. “A Behavioural Car-following Model for Computer Simulation.” Transportation Research Part B: Methodological 15 (2): 105–111. doi:10.1016/0191-2615(81)90037-0.
   Web of Science ®Google Scholar
• Gopalswamy, S., and S. Rathinam. 2018. “Infrastructure Enabled Autonomy: A Distributed Intelligence Architecture for Autonomous Vehicles.” ArXiv:1802.04112 [Cs]. http://arxiv.org/abs/1802.04112
   Google Scholar
• Grigorescu, S., B. Trasnea, T. Cocias, and G. Macesanu. 2020. “A Survey of Deep Learning Techniques for Autonomous Driving.” Journal of Field Robotics 37 (3): 362–386. doi:10.1002/rob.21918.
   Web of Science ®Google Scholar
• Guo, Q., L. Li, and X. J. Ban. 2019. “Urban Traffic Signal Control with Connected and Automated Vehicles: A Survey.” Transportation Research Part C: Emerging Technologies 101: 313–334. doi:10.1016/j.trc.2019.01.026.
   Web of Science ®Google Scholar
• Han, J., D. Kim, M. Lee, and M. Sunwoo. 2012. “Enhanced Road Boundary and Obstacle Detection Using a Downward-Looking LIDAR Sensor.” IEEE Transactions on Vehicular Technology 61 (3): 971–985. doi:10.1109/TVT.2012.2182785.
   Web of Science ®Google Scholar
• Hancock, P. A., T. Kajaks, J. K. Caird, M. H. Chignell, S. Mizobuchi, P. C. Burns, J. Feng, et al. 2020. “Challenges to Human Drivers in Increasingly Automated Vehicles.” Human Factors 62 (2): 310–328. doi:10.1177/0018720819900402.
   PubMed Web of Science ®Google Scholar
• Heitz, R. P. 2014. “The Speed-Accuracy Tradeoff: History, Physiology, Methodology, and Behavior.” Frontiers in Neuroscience 8: 150. doi:10.3389/fnins.2014.00150.
   PubMed Web of Science ®Google Scholar
• Higgs, B., M. M. Abbas, and A. Medina. 2011. “Analysis of the Wiedemann Car–following Model over Different Speeds Using Naturalistic Data.” In 3rd International Conference on Road Safety and Simulation. https://trid.trb.org/view/1290431.
   Google Scholar
• Horrey, W. J., and C. D. Wickens. 2004. “Focal and Ambient Visual Contributions and Driver Visual Scanning in Lane Keeping and Hazard Detection.” Proceedings of the Human Factors and Ergonomics Society Annual Meeting 48 (19): 2325–2329. doi:10.1177/154193120404801926.
   Google Scholar
• Hsu, Y.-W., Y.-S. Ciou, and J.-W. Perng. 2017. “Object Recognition System Design in Regions of Interest Based on AdaBoost Algorithm.” In 2017 20th International Conference on Information Fusion (Fusion), 1–5. doi:10.23919/ICIF.2017.8009787.
   Google Scholar
• Hu, J., B. Park, and Y.-J. Lee. 2015. “Coordinated Transit Signal Priority Supporting Transit Progression under Connected Vehicle Technology.” Transportation Research Part C: Emerging Technologies 55: 393–408. doi:10.1016/j.trc.2014.12.005.
   Web of Science ®Google Scholar
• Hussain, Q., Alhajyaseen, W. K., Pirdavani, A., Reinolsmann, N., Brijs, K., and Brijs, T. (2019). Speed perception and actual speed in a driving simulator and real-world: A validation study. Transportation Research Part F: Traffic Psychology and Behaviour, 62, 637–650.
   Web of Science ®Google Scholar
• Huynh, T., A. Gambi, and G. Fraser. 2019. “AC3R: Automatically Reconstructing Car Crashes from Police Reports.” In 2019 IEEE/ACM 41st International Conference on Software Engineering: Companion Proceedings (ICSE-Companion), 31–34. doi:10.1109/ICSE-Companion.2019.00031.
   Google Scholar
• Imprialou, M., and M. Quddus. 2019. “Crash Data Quality for Road Safety Research: Current State and Future Directions.” Accident; Analysis and Prevention 130: 84–90. doi:10.1016/j.aap.2017.02.022.
   PubMed Web of Science ®Google Scholar
• Isermann, R., R. Schwarz, and S. Stolzl. 2002. “Drive-by-Wire Systems Recent Devlopments Drive-by- Wire Structures with and Drive-by-Wire Systems.” IEEE Control Systems Magazine 22: 64–81.
   Web of Science ®Google Scholar
• Ismail, A., I. S. Razelan Mohd, I. P. Mandiartha, and N. Arib. 2018. “Exploration on Drivers’ Perception towards Roadway Inventory Elements.” In Regional Conference on Science, Technology and Social Sciences (RCSTSS 2016), edited by N. A. Yacob, N. A. Mohd Noor, N. Y. Mohd Yunus, R. Lob Yussof, and S. A. K. Y. Zakaria, 507–516. Pahang, Malaysis: Springer. doi:10.1007/978-981-13-0074-5_49.
   Google Scholar
• Iwanaga, Takuya, Hsiao-Hsuan Wang, Serena H. Hamilton, Volker Grimm, Tomasz E. Koralewski, Alejandro Salado, Sondoss Elsawah, et al. 2021. “Socio-Technical Scales in Socio-Environmental Modeling: Managing a System-of-Systems Modeling Approach.” Environmental Modelling & Software : With Environment Data News 135: 104885. doi:10.1016/j.­envsoft.2020.104885.
   PubMed Web of Science ®Google Scholar
• Jamshidi, M., ed. 2017. Systems of Systems Engineering: Principles and Applications. Boca Raton, FL: CRC Press. doi:10.1201/9781420065893.
   Google Scholar
• Jazayeri, A., H. Cai, J. Y. Zheng, and M. Tuceryan. 2011. “Vehicle Detection and Tracking in Car Video Based on Motion Model.” IEEE Transactions on Intelligent Transportation Systems 12 (2): 583–595. doi:10.1109/TITS.2011.2113340.
   Web of Science ®Google Scholar
• Jung, T., C. Kaß, D. Zapf, and H. Hecht. 2019. “Effectiveness and User Acceptance of Infotainment-Lockouts: A Driving Simulator Study.” Transportation Research Part F: Traffic Psychology and Behaviour 60: 643–656. doi:10.1016/j.trf.2018.12.001.
   Web of Science ®Google Scholar
• Kahneman, D. 2013. Thinking, Fast and Slow. 1st ed. New York, NY: Farrar, Straus and Giroux.
   Google Scholar
• Kenney, J. B. 2011. “Dedicated Short-Range Communications (DSRC) Standards in the United States.” Proceedings of the IEEE 99 (7): 1162–1182. doi:10.1109/JPROC.2011.2132790.
   Web of Science ®Google Scholar
• Kesting, A., M. Treiber, and D. Helbing. 2007. “General Lane-Changing Model MOBIL for Car-Following Models.” Transportation Research Record: Journal of the Transportation Research Board 1999 (1): 86–94. doi:10.3141/1999-10.
   Web of Science ®Google Scholar
• Kim, H. J., and J. H. Yang. 2017. “Takeover Requests in Simulated Partially Autonomous Vehicles considering Human Factors.” IEEE Transactions on Human-Machine Systems 47 (5): 735–740. doi:10.1109/THMS.2017.2674998.
   Web of Science ®Google Scholar
• Kim, S.-W., W. Liu, M. H. Ang, E. Frazzoli, and D. Rus. 2015. “The Impact of Cooperative Perception on Decision Making and Planning of Autonomous Vehicles.” IEEE Intelligent Transportation Systems Magazine 7 (3): 39–50. doi:10.1109/MITS.2015.2409883.
   Google Scholar
• Kim, Seong-Woo, Baoxing Qin, Zhuang Jie Chong, Xiaotong Shen, Wei Liu, Marcelo H. Ang, Emilio Frazzoli, and Daniela Rus. 2015. “Multivehicle Cooperative Driving Using Cooperative Perception: Design and Experimental Validation.” IEEE Transactions on Intelligent Transportation Systems 16 (2): 663–680. doi:10.1109/TITS.2014.2337316.
   Web of Science ®Google Scholar
• Klein, B., and K. Sullivan. 2021. “Biden Signs Infrastructure Bill into Law at Rare Bipartisan Gathering.” CNN. https://www.cnn.com/2021/11/15/politics/biden-signing-ceremony-infrastructure-bill-white-house/index.html
   Google Scholar
• Konstantopoulos, P., P. Chapman, and D. Crundall. 2010. “Driver’s Visual Attention as a Function of Driving Experience and Visibility. Using a Driving Simulator to Explore Drivers’ Eye Movements in Day, Night and Rain Driving.” Accident; Analysis and Prevention 42 (3): 827–834. doi:10.1016/j.aap.2009.09.022.
   PubMed Web of Science ®Google Scholar
• Kopelias, P., E. Demiridi, K. Vogiatzis, A. Skabardonis, and V. Zafiropoulou. 2020. “Connected & Autonomous Vehicles – Environmental Impacts – A Review.” The Science of the Total Environment 712: 135237. doi:10.1016/j.scitotenv.2019.135237.
   PubMed Web of Science ®Google Scholar
• Kwayu, K. M., V. Kwigizile, J. Zhang, and J.-S. Oh. 2020. “Semantic N-Gram Feature Analysis and Machine Learning–Based Classification of Drivers’ Hazardous Actions at Signal-Controlled Intersections.” Journal of Computing in Civil Engineering 34 (4): 04020015. doi:10.1061/(ASCE)CP.1943-5487.0000895.
   Web of Science ®Google Scholar
• Kwayu, K. M., V. Kwigizile, K. Lee, and J.-S. Oh. 2021. “Discovering Latent Themes in Traffic Fatal Crash Narratives Using Text Mining Analytics and Network Topology.” Accident; Analysis and Prevention 150: 105899. doi:10.1016/j.aap.2020.105899.
   PubMed Web of Science ®Google Scholar
• Kyriakidis, M., J. C. F. de Winter, N. Stanton, T. Bellet, B. van Arem, K. Brookhuis, M. H. Martens, et al. 2019. “A Human Factors Perspective on Automated Driving.” Theoretical Issues in Ergonomics Science 20 (3): 223–249. doi:10.1080/1463922X.2017.1293187.
   Web of Science ®Google Scholar
• Langford, J., and S. Koppel. 2006. “Epidemiology of Older Driver Crashes – Identifying Older Driver Risk Factors and Exposure Patterns.” Transportation Research Part F: Traffic Psychology and Behaviour 9 (5): 309–321. doi:10.1016/j.trf.2006.03.005.
   Web of Science ®Google Scholar
• Ledger, S., J. M. Bennett, E. Chekaluk, and J. Batchelor. 2019. “Cognitive Function and Driving: Important for Young and Old Alike.” Transportation Research Part F: Traffic Psychology and Behaviour 60: 262–273. doi:10.1016/j.trf.2018.10.024.
   Web of Science ®Google Scholar
• Lee, K. H., J. H. Hong, and T. G. Kim. 2015. “System of Systems Approach to Formal Modeling of CPS for Simulation-Based Analysis.” ETRI Journal 37 (1): 175–185. doi:10.4218/etrij.15.0114.0863.
   Web of Science ®Google Scholar
• Lee, K., and D. Kum. 2019. “Collision Avoidance/Mitigation System: Motion Planning of Autonomous Vehicle via Predictive Occupancy Map.” IEEE Access 7: 52846–52857. doi:10.1109/ACCESS.2019.2912067.
   Web of Science ®Google Scholar
• Lee, Y. M., R. Madigan, J. Garcia, A. Tomlinson, A. Solernou, R. Romano, G. Markkula, et al. 2019. “Understanding the Messages Conveyed by Automated Vehicles.” In Proceedings of the 11th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, 134–143. doi:10.1145/3342197.3344546.
   Google Scholar
• Levinson, J., J. Askeland, J. Becker, J. Dolson, D. Held, S. Kammel, J. Z. Kolter, et al. 2011. “Towards Fully Autonomous Driving: Systems and Algorithms.” In 2011 IEEE Intelligent Vehicles Symposium (IV), 163–168. doi:10.1109/IVS.2011.5940562.
   Google Scholar
• Li, S. E., K. Deng, Y. Zheng, and H. Peng. 2015. “Effect of Pulse-and-Glide Strategy on Traffic Flow for a Platoon of Mixed Automated and Manually Driven Vehicles.” Computer-Aided Civil and Infrastructure Engineering 30 (11): 892–905. https://trid.trb.org/view/1371628. doi:10.1111/mice.12168.
   Web of Science ®Google Scholar
• Li, Z., L. Elefteriadou, and S. Ranka. 2014. “Signal Control Optimization for Automated Vehicles at Isolated Signalized Intersections.” Transportation Research Part C: Emerging Technologies 49: 1–18. doi:10.1016/j.trc.2014.10.001.
   Web of Science ®Google Scholar
• Liao, Y., J. Zhang, S. Wang, S. Li, and J. Han. 2018. “Study on Crash Injury Severity Prediction of Autonomous Vehicles for Different Emergency Decisions Based on Support Vector Machine Model.” Electronics 7 (12): 381. doi:10.3390/electronics7120381.
   Web of Science ®Google Scholar
• Lo, S.-Y. 2018. “Attention without Awareness: Attentional Modulation of Perceptual Grouping without Awareness.” Attention, Perception & Psychophysics 80 (3): 691–701. doi:10.3758/s13414-017-1474-7.
   PubMed Web of Science ®Google Scholar
• Lu, C. H., and R. W. Proctor. 1995. “The Influence of Irrelevant Location Information on Performance: A Review of the Simon and Spatial Stroop Effects.” Psychonomic Bulletin & Review 2 (2): 174–207. doi:10.3758/BF03210959.
   PubMed Web of Science ®Google Scholar
• Ma, Y., S. Soatto, J. Kosecká, and S. S. Sastry. 2003. An Invitation to 3-D Vision: From Images to Geometric Models. 1st ed. New York: NY: Springer.
   Google Scholar
• Mack, A. 2003. “Inattentional Blindness: Looking without Seeing.” Current Directions in Psychological Science 12 (5): 180–184. doi:10.1111/1467-8721.01256.
   Web of Science ®Google Scholar
• Mahmassani, H. S. 2016. “50th Anniversary Invited Article—Autonomous Vehicles and Connected Vehicle Systems: Flow and Operations Considerations.” Transportation Science 50 (4): 1140–1162. doi:10.1287/trsc.2016.0712.
   Web of Science ®Google Scholar
• Mannering, F. L. 2020. Principles of Highway Engineering and Traffic. 7th ed. Hoboken, NJ: Wiley.
   Google Scholar
• Mao, X., D. Inoue, S. Kato, and M. Kagami. 2012. “Amplitude-Modulated Laser Radar for Range and Speed Measurement in Car Applications.” IEEE Transactions on Intelligent Transportation Systems 13 (1): 408–413. doi:10.1109/TITS.2011.2162627.
   Web of Science ®Google Scholar
• McCarthy, R. L. 2022. “Autonomous Vehicle Accident Data Analysis: California OL 316 Reports: 2015–2020.” ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems 8 (3): 034502-(1-6). doi:10.1115/1.4051779.
   Web of Science ®Google Scholar
• Miller, I., M. Campbell, and D. Huttenlocher. 2011. “Efficient Unbiased Tracking of Multiple Dynamic Obstacles under Large Viewpoint Changes.” IEEE Transactions on Robotics 27 (1): 29–46. doi:10.1109/TRO.2010.2085490.
   Web of Science ®Google Scholar
• Millot, P. 2014. Designing Human-Machine Cooperation Systems. Hoboken, NJ: Wiley. https://www.wiley.com/en-us/Designing+Human+machine+Cooperation+Systems-p-9781848216853.
   Google Scholar
• Miyajima, C., Y. Nishiwaki, K. Ozawa, T. Wakita, K. Itou, K. Takeda, and F. Itakura. 2007. “Driver Modeling Based on Driving Behavior and Its Evaluation in Driver Identification.” Proceedings of the IEEE 95 (2): 427–437. doi:10.1109/JPROC.2006.888405.
   Web of Science ®Google Scholar
• Montella, A., D. Andreassen, A. P. Tarko, S. Turner, F. Mauriello, L. L. Imbriani, and M. A. Romero. 2013. “Crash Databases in Australasia, the European Union, and the United States: Review and Prospects for Improvement.” Transportation Research Record: Journal of the Transportation Research Board 2386 (1): 128–136. doi:10.3141/2386-15.
   Web of Science ®Google Scholar
• National Academies of Sciences. 2013. Initial Analyses from the SHRP 2 Naturalistic Driving Study: Addressing Driver Performance and Behavior in Traffic Safety. Washington, D.C. doi:10.17226/22621.
   Google Scholar
• Neale, V. L., M. A. Perez, S. E. Lee, and Z. R. Doerzaph. 2007. “Investigation of Driver-Infrastructure and Driver-Vehicle Interfaces for an Intersection Violation Warning System.” Journal of Intelligent Transportation Systems 11 (3): 133–142. doi:10.1080/15472450701410437.
   Web of Science ®Google Scholar
• NHSTA. 2022. Early Estimate of Motor Vehicle Traffic Fatalities in 2021. U.S. Department of Transportation. Washington, D.C. https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812966
   Google Scholar
• Niroumand, R., M. Tajalli, L. Hajibabai, and A. Hajbabaie. 2020. “Joint Optimization of Vehicle-Group Trajectory and Signal Timing: Introducing the White Phase for Mixed-Autonomy Traffic Stream.” Transportation Research Part C: Emerging Technologies 116: 102659. doi:10.1016/j.trc.2020.102659.
   Web of Science ®Google Scholar
• Oberauer, K. 2009. “Design for a Working Memory.” In B. H. Ross (Ed.), Psychology of Learning and Motivation. Vol. 51, 45–100. San Diego, CA: Academic Press. doi:10.1016/S0079-7421(09)51002-X.
   Google Scholar
• Olia, A., H. Abdelgawad, B. Abdulhai, and S. N. Razavi. 2016. “Assessing the Potential Impacts of Connected Vehicles: Mobility, Environmental, and Safety Perspectives.” Journal of Intelligent Transportation Systems 20 (3): 229–243. doi:10.1080/15472450.2015.1062728.
   Web of Science ®Google Scholar
• Parasuraman, R., T. B. Sheridan, and C. D. Wickens. 2000. “A Model for Types and Levels of Human Interaction with Automation.” IEEE Transactions on Systems, Man, and Cybernetics. Part A, Systems and Humans : A Publication of the IEEE Systems, Man, and Cybernetics Society 30 (3): 286–297. doi:10.1109/3468.844354.
   PubMed Web of Science ®Google Scholar
• Petermeijer, S. M., D. A. Abbink, M. Mulder, and J. C. F. de Winter. 2015. “The Effect of Haptic Support Systems on Driver Performance: A Literature Survey.” IEEE Transactions on Haptics 8 (4): 467–479. doi:10.1109/TOH.2015.2437871.
   PubMed Web of Science ®Google Scholar
• Petersen, C. M., and P. R. DeLucia. 2022. “Perception of Intention in Traffic Environments: A Systematic Review.” In Proceedings of the 2022 HFES Annual Meeting, 953–957. Sage. doi:10.1177/1071181322661396.
   Google Scholar
• Pope, C. N., L. A. Ross, and D. Stavrinos. 2016. “Association between Executive Function and Problematic Adolescent Driving.” Journal of Developmental and Behavioral Pediatrics : JDBP 37 (9): 702–711. doi:10.1097/DBP.0000000000000353.
   PubMed Web of Science ®Google Scholar
• Posner, M. I., and S. J. Boies. 1971. “Components of Attention.” Psychological Review 78 (5): 391–408. doi:10.1037/h0031333.
   Web of Science ®Google Scholar
• Premebida, C., G. Monteiro, U. Nunes, and P. Peixoto. 2007. “A Lidar and Vision-Based Approach for Pedestrian and Vehicle Detection and Tracking.” In 2007 IEEE Intelligent Transportation Systems Conference, 1044–1049. doi:10.1109/ITSC.2007.4357637.
   Google Scholar
• Proctor, R. W., and T. Van Zandt. 2018. Human Factors in Simple and Complex Systems. 3rd ed. Boca Raton, FL: CRC Press. doi:10.1201/9781315156811.
   Google Scholar
• Proctor, R. W., and K.-P. L. Vu. 2006. Stimulus-Response Compatibility Principles: Data, Theory, and Application. Boca Raton, FL: CRC Press. doi:10.1201/9780203022795.
   Google Scholar
• Proctor, R. W., and K.-P. L. Vu. 2021. “Selection and Control of Action.” In Handbook of Human Factors and Ergonomics, 91–113. Hoboken, NJ: Wiley. doi:10.1002/9781119636113.ch4.
   Google Scholar
• Qu, W., J. Xu, Y. Ge, X. Sun, and K. Zhang. 2019. “Development and Validation of a Questionnaire to Assess Public Receptivity toward Autonomous Vehicles and Its Relation with the Traffic Safety Climate in China.” Accident; Analysis and Prevention 128: 78–86. doi:10.1016/j.aap.2019.04.006.
   PubMed Web of Science ®Google Scholar
• Ren, R., and J. Zhang. 2021. “Semantic Rule-Based Construction Procedural Information Extraction to Guide Jobsite Sensing and Monitoring.” Journal of Computing in Civil Engineering 35 (6): 04021026. doi:10.1061/(ASCE)CP.1943-5487.0000971.
   Web of Science ®Google Scholar
• Rey, D., and M. W. Levin. 2019. “Blue Phase: Optimal Network Traffic Control for Legacy and Autonomous Vehicles.” Transportation Research Part B: Methodological 130: 105–129. doi:10.1016/j.trb.2019.11.001.
   Web of Science ®Google Scholar
• Robertson, R. D., S. R. Meister, and W. G. Vanlaar. 2016. Automated Vehicles: Driver Knowledge, Attitudes and Practices. Ottawa, Ontario: Traffic Injury Research Foundation. https://trid.trb.org/view/1440348
   Google Scholar
• Rosique, F., P. J. Navarro, C. Fernández, and A. Padilla. 2019. “A Systematic Review of Perception System and Simulators for Autonomous Vehicles Research.” Sensors 19 (3): 648. doi:10.3390/s19030648.
   PubMed Web of Science ®Google Scholar
• SAE International. 2009. Dedicated Short Range Communications (DSRC) Message Set Dictionary (Standard SAE J2735). Warrendale, PA.
   Google Scholar
• Salvendy, G., and W. Karwowski, eds. 2021. Handbook of Human Factors and Ergonomics. 5th ed. Hoboken, NJ: Wiley.
   Google Scholar
• Schieber, F., B. Schlorholtz, and R. McCall. 2009. “Visual Requirements of Vehicular Guidance.” In Human Factors of Visual and Cognitive Performance in Driving, 31–50. Boca Raton, FL: CRC Press.
   Google Scholar
• Schmidt, R. A., T. D. Lee, C. Winstein, G. Wulf, and H. N. Zelaznik. 2018. Motor Control and Learning: A Behavioral Emphasis. 6th ed. Champaign, IL: Human Kinetics.
   Google Scholar
• Schoettle, B., and M. Sivak. 2015. A Preliminary Analysis of Real-World Crashes Involving Self-Driving Vehicles (UMTRI-2015-34). Ann Arbor, MI: University of Michigan Transportation Research Institute. https://trid.trb.org/view/1480475
   Google Scholar
• Schultheis, Maria T., Valerie Weisser, Jocelyn Ang, Elie Elovic, Richard Nead, Nicole Sestito, Cassandra Fleksher, and Scott R. Millis. 2010. “Examining the Relationship between Cognition and Driving Performance in Multiple Sclerosis.” Archives of Physical Medicine and Rehabilitation 91 (3): 465–473. doi:10.1016/j.apmr.2009.09.026.
   PubMed Web of Science ®Google Scholar
• Schwall, M., T. Daniel, T. Victor, F. Favaro, and H. Hohnhold. 2020. “Waymo Public Road Safety Performance Data.” ArXiv:2011.00038 [Cs]. http://arxiv.org/abs/2011.00038
   Google Scholar
• Shangguan, Q., T. Fu, J. Wang, T. Luo, and S. Fang. 2021. “An Integrated Methodology for Real-Time Driving Risk Status Prediction Using Naturalistic Driving Data.” Accident; Analysis and Prevention 156: 106122. doi:10.1016/j.aap.2021.106122.
   PubMed Web of Science ®Google Scholar
• Shi, X. 2020. “More than Smart Pavements: Connected Infrastructure Paves the Way for Enhanced Winter Safety and Mobility on Highways.” Journal of Infrastructure Preservation and Resilience 1 (1): 13. doi:10.1186/s43065-020-00014-x.
   Google Scholar
• Sinha, A., S. Chand, K. P. Wijayaratna, N. Virdi, and V. Dixit. 2020. “Comprehensive Safety Assessment in Mixed Fleets with Connected and Automated Vehicles: A Crash Severity and Rate Evaluation of Conventional Vehicles.” Accident; Analysis and Prevention 142: 105567. doi:10.1016/j.aap.2020.105567.
   PubMed Web of Science ®Google Scholar
• Sinha, K. C., S. Labi, and B. R. D. K. Agbelie. 2017. “Transportation Infrastructure Asset Management in the New Millennium: Continuing Issues, and Emerging Challenges and Opportunities.” Transportmetrica A: Transport Science 13 (7): 591–606. doi:10.1080/23249935.2017.1308977.
   Web of Science ®Google Scholar
• Sivaraman, S., and M. M. Trivedi. 2013. “Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis.” IEEE Transactions on Intelligent Transportation Systems 14 (4): 1773–1795. doi:10.1109/TITS.2013.2266661.
   Web of Science ®Google Scholar
• Society of Automotive Engineers. 2021. J3016C: Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicle. Warrendale, PA.
   Google Scholar
• Soleimani, S., A. Mohammadi, J. Chen, and M. Leitner. 2019. “Mining the Highway-Rail Grade Crossing Crash Data: A Text Mining Approach.” In 2019 18th IEEE International Conference on Machine Learning and Applications (ICMLA), 1063–1068. doi:10.1109/ICMLA.2019.00179.
   Google Scholar
• Stanton, N. A. 2019. “Thematic Issue: Driving Automation and Autonomy.” Theoretical Issues in Ergonomics Science 20 (3): 215–222. doi:10.1080/1463922X.2018.1541112.
   Web of Science ®Google Scholar
• Stewart, E. E. M., M. Valsecchi, and A. C. Schütz. 2020. “A Review of Interactions between Peripheral and Foveal Vision.” Journal of Vision 20 (12): 2. doi:10.1167/jov.20.12.2.
   PubMed Web of Science ®Google Scholar
• Talebpour, A., and H. S. Mahmassani. 2016. “Influence of Connected and Autonomous Vehicles on Traffic Flow Stability and Throughput.” Transportation Research Part C: Emerging Technologies 71: 143–163. doi:10.1016/j.trc.2016.07.007.
   Web of Science ®Google Scholar
• Tankersley, J. 2021. “Biden Details $2 Trillion Plan to Rebuild Infrastructure and Reshape the Economy.” The New York Times, March 31. https://www.nytimes.com/2021/03/31/business/economy/biden-infrastructure-plan.html
   Google Scholar
• Telpaz, A., B. Rhindress, I. Zelman, and O. Tsimhoni. 2015. “Haptic Seat for Automated Driving: Preparing the Driver to Take Control Effectively.” In 7th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, 23–30. doi:10.1145/2799250.2799267.
   Google Scholar
• Titz, J., A. Scholz, and P. Sedlmeier. 2018. “Comparing Eye Trackers by Correlating Their Eye-Metric Data.” Behavior Research Methods 50 (5): 1853–1863. doi:10.3758/s13428-017-0954-y.
   PubMed Web of Science ®Google Scholar
• Transportation Research Board. 2015. Signal Timing Manual. 2nd ed. (T. Urbanik, A. Tanaka, B. Lozner, E. Lindstrom, K. Lee, S. Quayle, and D. Bullock, Eds.). Washington DC: The National Academies Press. doi:10.17226/22097.
   Google Scholar
• Treiber, M., A. Hennecke, and D. Helbing. 2000. “Congested Traffic States in Empirical Observations and Microscopic Simulations.” Physical Review. E, Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics 62 (2): 1805–1824. doi:10.1103/PhysRevE.62.1805.
   PubMed Web of Science ®Google Scholar
• Tuten, W. S., and W. M. Harmening. 2021. “Foveal Vision.” Current Biology : CB 31 (11): R701–R703. doi:10.1016/j.cub.2021.03.097.
   PubMed Web of Science ®Google Scholar
• van Loon, R. J., and M. H. Martens. 2015. “Automated Driving and Its Effect on the Safety Ecosystem: How Do Compatibility Issues Affect the Transition Period?” Procedia Manufacturing 3: 3280–3285. doi:10.1016/j.promfg.2015.07.401.
   Google Scholar
• Vu, K.-P L., and D. Chiappe. 2015. “Situation Awareness in Human Systems Integration.” In D. A. Boehm-Davis, F. T. Durso, & J. D. Lee (Eds.), APA Handbook of Human Systems Integration (pp. 293–308). Washington, DC: American Psychological Association. doi:10.1037/14528-019.
   Google Scholar
• Wan, G., X. Yang, R. Cai, H. Li, H. Wang, and S. Song. 2018. “Robust and Precise Vehicle Localization Based on Multi-Sensor Fusion in Diverse City Scenes.” In 2018 IEEE International Conference on Robotics and Automation (ICRA), 4670–4677. doi:10.1109/ICRA.2018.8461224.
   Google Scholar
• Wang, H., and X. Zhang. 2022. “Real-Time Vehicle Detection and Tracking Using 3D LiDAR.” Asian Journal of Control 24 (3): 1459–1469. doi:10.1002/asjc.2519.
   Web of Science ®Google Scholar
• Wang, M., Y. Liao, S. L. Lyckvi, and F. Chen. 2020. “How Drivers Respond to Visual vs. Auditory Information in Advisory Traffic Information Systems.” Behaviour & Information Technology 39 (12): 1308–1319. doi:10.1080/0144929X.2019.1667439.
   Web of Science ®Google Scholar
• Wang, S., and Z. Li. 2019. “Exploring the Mechanism of Crashes with Automated Vehicles Using Statistical Modeling Approaches.” PloS One 14 (3): e0214550. doi:10.1371/journal.pone.0214550.
   PubMed Web of Science ®Google Scholar
• Wetton, M. A., M. S. Horswill, C. Hatherly, J. M. Wood, N. A. Pachana, and K. J. Anstey. 2010. “The Development and Validation of Two Complementary Measures of Drivers’ Hazard Perception Ability.” Accident; Analysis and Prevention 42 (4): 1232–1239. doi:10.1016/j.aap.2010.01.017.
   PubMed Web of Science ®Google Scholar
• Wickens, C. D. 2008. “Multiple Resources and Mental Workload.” Human Factors 50 (3): 449–455. doi:10.1518/001872008X288394.
   PubMed Web of Science ®Google Scholar
• Wickens, C. D., and C. M. Carswell. 2021. “Information Processing.” In Handbook of Human Factors and Ergonomics. 5th ed, 114–158. Hoboken, NJ: Wiley. doi:10.1002/9781119636113.ch5.
   Google Scholar
• Wickens, C. D., J. G. Hollands, S. Banbury, and R. Parasuraman. 2012. Engineering Psychology and Human Performance. 4th ed. New York, NY: Psychology Press.
   Google Scholar
• Xiong, G., F. Zhu, X. Liu, X. Dong, W. Huang, S. Chen, and K. Zhao. 2015. “Cyber-Physical-Social System in Intelligent Transportation.” IEEE/CAA Journal of Automatica Sinica 2 (3): 320–333. doi:10.1109/JAS.2015.7152667.
   Google Scholar
• Yang, Z., Y. Feng, and H. X. Liu. 2021. “A Cooperative Driving Framework for Urban Arterials in Mixed Traffic Conditions.” Transportation Research Part C: Emerging Technologies 124: 102918. doi:10.1016/j.trc.2020.102918.
   Web of Science ®Google Scholar
• Yared, T., P. Patterson, and E. S. A. All. 2020. “Are Safety and Performance Affected by Navigation System Display Size, Environmental Illumination, and Gender When Driving in Both Urban and Rural Areas?” Accident; Analysis and Prevention 142: 105585. doi:10.1016/j.aap.2020.105585.
   PubMed Web of Science ®Google Scholar
• Ye, L., and T. Yamamoto. 2019. “Evaluating the Impact of Connected and Autonomous Vehicles on Traffic Safety.” Physica A: Statistical Mechanics and Its Applications 526: 121009. doi:10.1016/j.physa.2019.04.245.
   Web of Science ®Google Scholar
• Ying, J., and Y. Feng. 2022. “Full Vehicle Trajectory Planning Model for Urban Traffic Control Based on Imitation Learning.” Transportation Research Record: Journal of the Transportation Research Board 2676 (7): 186–198. doi:10.1177/03611981221077263.
   Web of Science ®Google Scholar
• Yu, C., W. Sun, H. X. Liu, and X. Yang. 2019. “Managing Connected and Automated Vehicles at Isolated Intersections: From Reservation- to Optimization-Based Methods.” Transportation Research Part B: Methodological 122: 416–435. doi:10.1016/j.trb.2019.03.002.
   Web of Science ®Google Scholar
• Yu, C., Y. Feng, H. X. Liu, W. Ma, and X. Yang. 2018. “Integrated Optimization of Traffic Signals and Vehicle Trajectories at Isolated Urban Intersections.” Transportation Research Part B: Methodological 112: 89–112. doi:10.1016/j.trb.2018.04.007.
   Web of Science ®Google Scholar
• Zamanakos, G., L. Tsochatzidis, A. Amanatiadis, and I. Pratikakis. 2021. “A Comprehensive Survey of LIDAR-Based 3D Object Detection Methods with Deep Learning for Autonomous Driving.” Computers & Graphics 99: 153–181. doi:10.1016/j.cag.2021.07.003.
   Web of Science ®Google Scholar
• Zhang, J., E. Vinkhuyzen, and M. Cefkin. 2018. In N. A. Stanton (Ed.), “Evaluation of an Autonomous Vehicle External Communication System Concept: A Survey Study.” In Advances in Human Aspects of Transportation (pp. 650–661). Berlin: Springer. doi:10.1007/978-3-319-60441-1_63.
   Google Scholar
• Zhang, J., V. Kwigizile, and J.-S. Oh. 2016. “Automated Hazardous Action Classification Using Natural Language Processing and Machine-Learning Techniques.” In CICTP 2016, 1579–1590. doi:10.1061/9780784479896.144.
   Google Scholar
• Zhang, X., E. Green, M. Chen, and R. R. Souleyrette. 2020. “Identifying Secondary Crashes Using Text Mining Techniques.” Journal of Transportation Safety & Security 12 (10): 1338–1358. doi:10.1080/19439962.2019.1597795.
   Web of Science ®Google Scholar
• Zicat, E., J. M. Bennett, E. Chekaluk, and J. Batchelor. 2018. “Cognitive Function and Young Drivers: The Relationship between Driving, Attitudes, Personality and Cognition.” Transportation Research Part F: Traffic Psychology and Behaviour 55: 341–352. doi:10.1016/j.trf.2018.03.013.
   Web of Science ®Google Scholar
• Zou, Xin., Steve O’Hern, Barrett Ens, Selby Coxon, Pascal Mater, Raymond Chow, Michael Neylan, and Hai L. Vu. 2021. “On-Road Virtual Reality Autonomous Vehicle (VRAV) Simulator: An Empirical Study on User Experience.” Transportation Research Part C: Emerging Technologies 126: 103090. doi:10.1016/j.trc.2021.103090.
   Web of Science ®Google Scholar