📘Relevant Publications
Relevant publications
Relevant Publications
Publication regarding data processing
Hu, X., Zheng, Z., Chen, D., Zhang, X. and Sun, J., 2022. Processing, assessing, and enhancing the Waymo autonomous vehicle open dataset for driving behavior research. Transportation Research Part C: Emerging Technologies, 134, p.103490. https://doi.org/10.1016/j.trc.2021.103490
Li, G., Jiao, Y., Knoop, V.L., Calvert, S.C. and van Lint, J.W.C., 2023. Large Car-following Data Based on Lyft level-5 Open Dataset: Following Autonomous Vehicles vs. Human-driven Vehicles. arXiv preprint arXiv:2305.18921.
Li, G., Jiao, Y., Calvert, S.C. and van Lint, J.W.C., 2023. A Comparative Conflict Resolution Dataset Derived from Argoverse-2: Scenarios with vs. without Autonomous Vehicles. arXiv preprint arXiv:2308.13839.
Xia, X., Meng, Z., Han, X., Li, H., Tsukiji, T., Xu, R., Zheng, Z. and Ma, J., 2023. An automated driving systems data acquisition and analytics platform. Transportation research part C: emerging technologies, 151, p.104120. https://doi.org/10.1016/j.trc.2023.104120
Publication regarding mixed traffic
Andreotti, E., Boyraz, P. and Selpi, S., 2020. Mathematical definitions of scene and scenario for analysis of automated driving systems in mixed-traffic simulations. IEEE Transactions on Intelligent Vehicles, 6(2), pp.366-375.
Ard, T., Dollar, R.A., Vahidi, A., Zhang, Y. and Karbowski, D., 2020. Microsimulation of energy and flow effects from optimal automated driving in mixed traffic. Transportation Research Part C: Emerging Technologies, 120, p.102806.
Ard, T., Guo, L., Dollar, R.A., Fayazi, A., Goulet, N., Jia, Y., Ayalew, B. and Vahidi, A., 2021. Energy and flow effects of optimal automated driving in mixed traffic: Vehicle-in-the-loop experimental results. Transportation Research Part C: Emerging Technologies, 130, p.103168.
Azam, M., Hassan, S.A. and Che Puan, O., 2022. Autonomous Vehicles in Mixed Traffic Conditions—A Bibliometric Analysis. Sustainability, 14(17), p.10743.
Calvert, S.C. and van Arem, B., 2020. A generic multi-level framework for microscopic traffic simulation with automated vehicles in mixed traffic. Transportation Research Part C: Emerging Technologies, 110, pp.291-311.
Chen, Z. and Park, B.B., 2022. Connected preceding vehicle identification for enabling cooperative automated driving in mixed traffic. Journal of transportation engineering, Part A: Systems, 148(5), p.04022013.
Farah, H., Postigo, I., Reddy, N., Dong, Y., Rydergren, C., Raju, N. and Olstam, J., 2022. Modeling Automated Driving in Microscopic Traffic Simulations for Traffic Performance Evaluations: Aspects to Consider and State of the Practice. IEEE Transactions on Intelligent Transportation Systems.
Jin, S., Sun, D.H., Zhao, M., Li, Y. and Chen, J., 2020. Modeling and stability analysis of mixed traffic with conventional and connected automated vehicles from cyber physical perspective. Physica A: Statistical Mechanics and its Applications, 551, p.124217.
Klimke, M., Völz, B. and Buchholz, M., 2023. Automatic Intersection Management in Mixed Traffic Using Reinforcement Learning and Graph Neural Networks. arXiv preprint arXiv:2301.12717.
Lee, S., Jeong, E., Oh, M. and Oh, C., 2019. Driving aggressiveness management policy to enhance the performance of mixed traffic conditions in automated driving environments. Transportation research part A: policy and practice, 121, pp.136-146.
Mullakkal-Babu, F.A., Wang, M., van Arem, B. and Happee, R., 2020. Comparative safety assessment of automated driving strategies at highway merges in mixed traffic. IEEE transactions on intelligent transportation systems, 23(4), pp.3626-3639.
Schwesinger, U., Versari, P., Broggi, A. and Siegwart, R., 2015. Vision-only fully automated driving in dynamic mixed-traffic scenarios. it-Information Technology, 57(4), pp.231-242.
Stange, V., Kühn, M. and Vollrath, M., 2022. Manual drivers’ experience and driving behavior in repeated interactions with automated Level 3 vehicles in mixed traffic on the highway. Transportation research part F: traffic psychology and behaviour, 87, pp.426-443.
Yao, S. and Friedrich, B., 2019, October. Managing connected and automated vehicles in mixed traffic by human-leading platooning strategy: A simulation study. In 2019 IEEE Intelligent Transportation Systems Conference (ITSC) (pp. 3224-3229). IEEE.
Zhao, X., Liao, X., Wang, Z., Wu, G., Barth, M., Han, K. and Tiwari, P., 2022. Co-simulation platform for modeling and evaluating connected and automated vehicles and human behavior in mixed traffic. SAE International Journal of Connected and Automated Vehicles, 5(12-05-04-0025), pp.313-326.
Ziehn, J.R., Baumann, M.V., Beyerer, J., Buck, H.S., Deml, B., Ehrhardt, S., Frese, C., Kleiser, D., Lauer, M., Roschani, M. and Ruf, M., 2023. Cooperative automated driving for bottleneck scenarios in mixed traffic. In 35th IEEE Intelligent Vehicles Symposium (IV 2023), Anchorage, AK, USA, June 4-7, 2023.
Publication regarding social-aware driving in mixed traffic
Alahi, A., Goel, K., Ramanathan, V., Robicquet, A., Fei-Fei, L., Savarese, S., 2016. Social LSTM: Human trajectory prediction in crowded spaces, in: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/CVPR.2016.110
Buckman, N., Pierson, A., Schwarting, W., Karaman, S., Rus, D., 2019. Sharing is Caring: Socially-Compliant Autonomous Intersection Negotiation. IEEE Int. Conf. Intell. Robot. Syst. 6136–6143. https://doi.org/10.1109/IROS40897.2019.8967997
Ferrer, G., Sanfeliu, A., 2014. Proactive kinodynamic planning using the Extended Social Force Model and human motion prediction in urban environments. IEEE Int. Conf. Intell. Robot. Syst. 1730–1735. https://doi.org/10.1109/IROS.2014.6942788
Garcia, R.S., Araujo, D., 2021. Driving in Roundabouts : Why a Different Theory of Expert Cognition in Social Driving Is Needed for Self-driving Cars.
Gupta, A., Johnson, J., Fei-Fei, L., Savarese, S., Alahi, A., 2018. Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks. Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. 2255–2264. https://doi.org/10.1109/CVPR.2018.00240
Hang, P., Huang, C., Hu, Z., Lv, C., 2022. Decision Making for Connected Automated Vehicles at Urban Intersections Considering Social and Individual Benefits 1–14.
Hang, P., Lv, C., Huang, C., Cai, J., Hu, Z., Xing, Y., 2020. An Integrated Framework of Decision Making and Motion Planning for Autonomous Vehicles Considering Social Behaviors. IEEE Trans. Veh. Technol. 69, 14458–14469. https://doi.org/10.1109/TVT.2020.3040398
Hang, P., Lv, C., Xing, Y., Huang, C., Hu, Z., 2021. Human-Like Decision Making for Autonomous Driving: A Noncooperative Game Theoretic Approach. IEEE Trans. Intell. Transp. Syst. 22, 2076–2087. https://doi.org/10.1109/TITS.2020.3036984
Jaques, N., Lazaridou, A., Hughes, E., Gulcehre, C., Ortega, P.A., Strouse, D.J., Leibo, J.Z., de Freitas, N., 2019. Social influence as intrinsic motivation for multi-agent deep reinforcement learning, in: 36th International Conference on Machine Learning, ICML 2019.
Kolekar, S., de Winter, J., Abbink, D., 2020. Human-like driving behaviour emerges from a risk-based driver model. Nat. Commun. 11. https://doi.org/10.1038/s41467-020-18353-4
Larsson, J., Keskin, M.F., Peng, B., Kulcsár, B., Wymeersch, H., 2021. Pro-social control of connected automated vehicles in mixed-autonomy multi-lane highway traffic. Commun. Transp. Res. 1, 100019. https://doi.org/10.1016/j.commtr.2021.100019
Oliveira, L., Proctor, K., Burns, C.G., Birrell, S., 2019. Driving style: How should an automated vehicle behave? Inf. 10, 1–20. https://doi.org/10.3390/INFO10060219
Othman, K., 2021. Public acceptance and perception of autonomous vehicles: a comprehensive review, AI and Ethics. Springer International Publishing. https://doi.org/10.1007/s43681-021-00041-8
Schneble, C.O., Shaw, D.M., 2021. Driver’s views on driverless vehicles: Public perspectives on defining and using autonomous cars. Transp. Res. Interdiscip. Perspect. 11, 100446. https://doi.org/10.1016/j.trip.2021.100446
Schwarting, W., Pierson, A., Alonso-Mora, J., Karaman, S., Rus, D., 2019. Social behavior for autonomous vehicles. Proc. Natl. Acad. Sci. U. S. A. 116, 2492–24978. https://doi.org/10.1073/pnas.1820676116
Sun, L., Zhan, W., Chan, C.Y., Tomizuka, M., 2019. Behavior planning of autonomous cars with social perception. IEEE Intell. Veh. Symp. Proc. 2019-June, 207–213. https://doi.org/10.1109/IVS.2019.8814223
Sun, L., Zhan, W., Tomizuka, M., Dragan, A.D., 2018. Courteous Autonomous Cars. IEEE Int. Conf. Intell. Robot. Syst. 663–670. https://doi.org/10.1109/IROS.2018.8593969
Tafidis, P., Farah, H., Brijs, T., Pirdavani, A., 2022. Safety implications of higher levels of automated vehicles: a scoping review. Transp. Rev. 42, 245–267. https://doi.org/10.1080/01441647.2021.1971794
Toghi, B., Valiente, R., Sadigh, D., Pedarsani, R., Fallah, Y.P., 2021a. Altruistic Maneuver Planning for Cooperative Autonomous Vehicles Using Multi-agent Advantage Actor-Critic 1–8.
Toghi, B., Valiente, R., Sadigh, D., Pedarsani, R., Fallah, Y.P., 2021b. Cooperative Autonomous Vehicles that Sympathize with Human Drivers. IEEE Int. Conf. Intell. Robot. Syst. 4517–4524. https://doi.org/10.1109/IROS51168.2021.9636151
Vemula, A., Muelling, K., Oh, J., 2018. Social Attention: Modeling Attention in Human Crowds. Proc. - IEEE Int. Conf. Robot. Autom. 4601–4607. https://doi.org/10.1109/ICRA.2018.8460504
Wang, L., Sun, L., Tomizuka, M., Zhan, W., 2021. Socially-Compatible Behavior Design of Autonomous Vehicles with Verification on Real Human Data. IEEE Robot. Autom. Lett. 6, 3421–3428. https://doi.org/10.1109/LRA.2021.3061350
Yoon, D.D., Ayalew, B., 2019. Social force aggregation control for autonomous driving with connected preview. Proc. Am. Control Conf. 2019-July, 1388–1393. https://doi.org/10.23919/acc.2019.8814725
Zhang, Q., Esterwood, C., Yang, J., Robert, L., 2019. An Automated Vehicle (AV) like Me? The Impact of Personality Similarities and Differences between Humans and AVs. SSRN Electron. J. https://doi.org/10.2139/ssrn.3446005
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