> For the complete documentation index, see [llms.txt](https://qiqiqi.gitbook.io/mixed-traffic/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://qiqiqi.gitbook.io/mixed-traffic/overview/relevant-publications.md). # Relevant Publications ## Relevant Publications ### Review and a Conceptual Framework Dong, Y., Van Arem, B., & Farah, H. (2025). [Towards Developing Socially-Compliant Automated Vehicles: Advances, Expert Insights, and a Conceptual Framework](https://doi.org/10.1016/j.commtr.2025.100207). *Communications in Transportation Research*, *5*, 1-23. ### Publication regarding datasets and data processing Bock, J., Krajewski, R., Moers, T., Runde, S., Vater, L., & Eckstein, L. (2020). The inD dataset: A drone dataset of naturalistic road user trajectories at German intersections. *IEEE Intelligent Vehicles Symposium, Proceedings*. Chang, M. F., Lambert, J., Sangkloy, P., Singh, J., Bak, S., Hartnett, A., Wang, D., Carr, P., Lucey, S., Ramanan, D., & Hays, J. (2019). Argoverse: 3D tracking and forecasting with rich maps. *Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition*. Ettinger, S., Cheng, S., Caine, B., Liu, C., Zhao, H., Pradhan, S., Chai, Y., Sapp, B., Qi, C., Zhou, Y., Yang, Z., Chouard, A., Sun, P., Ngiam, J., Vasudevan, V., McCauley, A., Shlens, J., & Anguelov, D. (2021). Large scale interactive motion forecasting for autonomous driving: The Waymo Open Motion Dataset. *Proceedings of the IEEE International Conference on Computer Vision*. 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. Krajewski, R., Bock, J., Kloeker, L., & Eckstein, L. (2018). The highD dataset: A drone dataset of naturalistic vehicle trajectories on German highways for validation of highly automated driving systems. *IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC*, *2018*-*Novem*, 2118–2125. Krajewski, R., Moers, T., Bock, J., Vater, L., & Eckstein, L. (2020). The rounD dataset: A drone dataset of road user trajectories at roundabouts in Germany. *2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020*. 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. Moers, T., Vater, L., Krajewski, R., Bock, J., Zlocki, A., & Eckstein, L. (2022). The exiD dataset: A real-world trajectory dataset of highly interactive highway scenarios in Germany. *IEEE Intelligent Vehicles Symposium, Proceedings*. Wang, Xueyang, Zhang, X., Zhu, Y., Guo, Y., Yuan, X., Xiang, L., Wang, Z., Ding, G., Brady, D., Dai, Q., & Fang, L. (2020). Panda: A gigapixel-level human-centric video dataset. *Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition*. Wilson, B., Qi, W., Agarwal, T., Lambert, J., Singh, J., Khandelwal, S., Pan, B., Kumar, R., Hartnett, A., Pontes, J. K., Ramanan, D., Carr, P., & Hays, J. (2023). *Argoverse 2: next generation datasets for self-driving perception and forecasting*. *NeurIPS*. 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. Xu, Y., Shao, W., Li, J., Yang, K., Wang, W., Huang, H., Lv, C., & Wang, H. (2022). SIND: A drone dataset at signalized intersection in China. *IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC*, *2022*, 2471–2478. ; Zhan, W., Sun, L., Wang, D., Shi, H., Clausse, A., Naumann, M., Kummerle, J., Konigshof, H., Stiller, C., de La Fortelle, A., & Tomizuka, M. (2019). INTERACTION dataset: An INTERnational, Adversarial and Cooperative moTION dataset in interactive driving scenarios with semantic maps. ### 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. Talebpour, A., & Mahmassani, H. S. (2016). Influence of connected and autonomous vehicles on traffic flow stability and throughput. *Transportation Research Part C: Emerging Technologies*. 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 socially compliant 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. *Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition*. Benrachou, D. E., Glaser, S., Elhenawy, M., & Rakotonirainy, A. (2022). Use of social interaction and intention to improve motion prediction within automated vehicle framework: A review. *IEEE Transactions on Intelligent Transportation Systems*, *23*(12), 22807–22837. Bhatt, N. P., Khajepour, A., & Hashemi, E. (2022). MPC-PF: Social interaction aware trajectory prediction of dynamic objects for autonomous driving using potential fields. *2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)*, *i*, 9837–9844. Brown, B., Broth, M., & Vinkhuyzen, E. (2023). The Halting problem: Video analysis of self-driving cars in traffic. *Conference on Human Factors in Computing Systems - Proceedings*. Buckman, N., Pierson, A., Schwarting, W., Karaman, S., & Rus, D. (2019). Sharing is caring: Socially-compliant autonomous intersection negotiation. *IEEE International Conference on Intelligent Robots and Systems*. Chang, W. J., Tang, C., Li, C., Hu, Y., Tomizuka, M., & Zhan, W. (2023). Editing driver character: Socially-controllable behavior generation for interactive traffic simulation. *IEEE Robotics and Automation Letters*, *8*(9), 5432–5439. Chen, X., Zhang, W., Bai, H., Xu, C., Ding, H., & Huang, W. (2024). Two-dimensional following lane-changing (2DF-LC): A framework for dynamic decision-making and rapid behavior planning. *IEEE Transactions on Intelligent Vehicles*, *9*(1), 427–445. Crosato, L., Shum, H. P. H., Ho, E. S. L., & Wei, C. (2023). Interaction-aware decision-making for automated vehicles using social value orientation. *IEEE Transactions on Intelligent Vehicles*, *8*(2), 1339–1349. Crosato, L., Tian, K., Shum, H. P. H., Ho, E. S. L., Wang, Y., & Wei, C. (2023). Social interaction-aware dynamical models and decision-making for autonomous vehicles. *Advanced Intelligent Systems*, *2300575*. Crosato, L., Wei, C., Ho, E. S. L., & Shum, H. P. H. (2021). Human-centric autonomous driving in an AV-pedestrian interactive environment using SVO. *Proceedings of the 2021 IEEE International Conference on Human-Machine Systems, ICHMS 2021*, 1–6. Da, L., & Wei, H. (2023). CrowdGAIL: A spatiotemporal aware method for agent navigation. *Electronic Research Archive*, *31*(2), 1134–1146. Ding, W., Zhang, L., Chen, J., & Shen, S. (2022). EPSILON: An efficient planning system for automated vehicles in highly interactive environments. *IEEE Transactions on Robotics*, *38*(2), 1118–1138. Dong, Y., Liu, C., Wang, Y., & Fu, Z. (2024). *Towards understanding worldwide cross-cultural differences in implicit driving cues: Review, comparative analysis, and research roadmap*. 2024 *IEEE 27th International Conference on Intelligent Transportation Systems (ITSC)*, Edmonton, AB, Canada, 2024, pp. 1569-1575. Du, Y., Chen, J., Zhao, C., Liu, C., Liao, F., & Chan, C. Y. (2022). Comfortable and energy-efficient speed control of autonomous vehicles on rough pavements using deep reinforcement learning. *Transportation Research Part C: Emerging Technologies*, *134* (December 2021), 103489. ElSamadisy, O., Shi, T., Smirnov, I., & Abdulhai, B. (2024). Safe, efficient, and comfortable reinforcement-learning-based car-following for AVs with an analytic safety guarantee and dynamic target speed. *Transportation Research Record*, *2678*(1), 643–661. Ferrer, G., & Sanfeliu, A. (2014). Proactive kinodynamic planning using the Extended Social Force Model and human motion prediction in urban environments. *IEEE International Conference on Intelligent Robots and Systems*, *IROS*, 1730–1735. Fraedrich, E., Beiker, S., & Lenz, B. (2015). Transition pathways to fully automated driving and its implications for the sociotechnical system of automobility. *European Journal of Futures Research*, *3*(1). Galati, G., Primatesta, S., Grammatico, S., Macrì, S., & Rizzo, A. (2022). Game theoretical trajectory planning enhances social acceptability of robots by humans. *Scientific Reports*, *12*(1), 1–18. Geng, M., Cai, Z., Zhu, Y., Chen, X., & Lee, D. H. (2023). Multimodal vehicular trajectory prediction with inverse reinforcement learning and risk aversion at urban unsignalized intersections. *IEEE Transactions on Intelligent Transportation Systems*, *24*(11), 12227–12240. Gupta, A., Johnson, J., Fei-Fei, L., Savarese, S., & Alahi, A. (2018). Social GAN: Socially acceptable trajectories with generative adversarial networks. *Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition*, 2255–2264. Hang, P., Huang, C., Hu, Z., & Lv, C. (2022a). Decision making for connected automated vehicles at urban intersections considering social and individual benefits. *IEEE Transactions on Intelligent Transportation Systems*, *23*(11), 22549–22562. Hang, P., Huang, C., Hu, Z., & Lv, C. (2022b). Driving conflict resolution of autonomous vehicles at unsignalized intersections: A differential game approach. *IEEE/ASME Transactions on Mechatronics*, *27*(6), 5136–5146. 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 Transactions on Vehicular Technology*, *69*(12), 14458–14469. Hang, P., Lv, C., Huang, C., Xing, Y., & Hu, Z. (2022). Cooperative decision making of connected automated vehicles at multi-lane merging zone: A coalitional game approach. *IEEE Transactions on Intelligent Transportation Systems*, *23*(4), 3829–3841. Hang, P., Lv, C., Huang, C., Xing, Y., Hu, Z., & Cai, J. (2020). Human-like lane-change decision making for automated driving with a game theoretic approach. *2020 4th CAA International Conference on Vehicular Control and Intelligence, CVCI 2020*, *Cvci*, 708–713. Hang, P., Lv, C., Xing, Y., Huang, C., & Hu, Z. (2021). Human-like decision making for autonomous driving: A noncooperative game theoretic approach. *IEEE Transactions on Intelligent Transportation Systems*, *22*(4), 2076–2087. Hirose, N., Shah, D., Sridhar, A., & Levine, S. (2024). SACSoN: Scalable autonomous control for social navigation. *IEEE Robotics and Automation Letters*, *9*(1), 49–56. Huang, B., & Sun, P. (2023). Social occlusion inference with vectorized representation for autonomous driving. *Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics*, 2634–2639. Huang, Z., Liu, H., Wu, J., & Lv, C. (2023). Conditional predictive behavior planning with inverse reinforcement learning for human-like autonomous driving. *IEEE Transactions on Intelligent Transportation Systems*, *24*(7), 7244–7258. Huang, Z., Wu, J., & Lv, C. (2023). Efficient deep reinforcement learning with imitative expert priors for autonomous driving. *IEEE Transactions on Neural Networks and Learning Systems*, *34*(10), 7391–7403. Joo, Y. K., & Kim, B. (2023). Selfish but socially approved: The effects of perceived collision algorithms and social approval on attitudes toward autonomous vehicles. *International Journal of Human-Computer Interaction*, *39*(19), 3717–3727. Kolekar, S., de Winter, J., & Abbink, D. (2020). 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The identified methods adopted

The identified involved maneuvers and use cases

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