📘Relevant Publications
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. Communications in Transportation Research, 5, 1-23. https://doi.org/10.1016/j.commtr.2025.100207
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. https://doi.org/10.1109/IV47402.2020.9304839
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. https://doi.org/10.1109/CVPR.2019.00895
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. https://doi.org/10.1109/ICCV48922.2021.00957
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
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. https://doi.org/10.1109/ITSC.2018.8569552
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. https://doi.org/10.1109/ITSC45102.2020.9294728
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. https://doi.org/10.1109/IV51971.2022.9827305
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. https://doi.org/10.1109/CVPR42600.2020.00333
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. http://arxiv.org/abs/2301.00493
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
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. https://doi.org/10.1109/ITSC55140.2022.9921959
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. http://arxiv.org/abs/1910.03088
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. https://doi.org/10.1016/j.trc.2016.07.007
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. https://doi.org/10.1109/CVPR.2016.110
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. https://doi.org/10.1109/TITS.2022.3207347
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. https://doi.org/10.1109/iros47612.2022.9981046
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. https://doi.org/10.1145/3544548.3581045
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. https://doi.org/10.1109/IROS40897.2019.8967997
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. https://doi.org/10.1109/LRA.2023.3291897
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. https://doi.org/10.1109/TIV.2023.3324305
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. https://doi.org/10.1109/TIV.2022.3189836
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. https://doi.org/10.1002/aisy.202300575
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. https://doi.org/10.1109/ICHMS53169.2021.9582640
Da, L., & Wei, H. (2023). CrowdGAIL: A spatiotemporal aware method for agent navigation. Electronic Research Archive, 31(2), 1134–1146. https://doi.org/10.3934/era.2023057
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. https://doi.org/10.1109/TRO.2021.3104254
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. http://dx.doi.org/10.1109/ITSC58415.2024.10919561
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. https://doi.org/10.1016/j.trc.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. https://doi.org/10.1177/03611981231171899
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. https://doi.org/10.1109/IROS.2014.6942788
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). https://doi.org/10.1007/s40309-015-0067-8
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. https://doi.org/10.1038/s41598-022-25438-1
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. https://doi.org/10.1109/TITS.2023.3285891
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. https://doi.org/10.1109/CVPR.2018.00240
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. https://doi.org/10.1109/TITS.2022.3209607
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. https://doi.org/10.1109/TMECH.2022.3174273
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. https://doi.org/10.1109/TVT.2020.3040398
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. https://doi.org/10.1109/TITS.2021.3069463
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. https://doi.org/10.1109/CVCI51460.2020.9338614
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. https://doi.org/10.1109/TITS.2020.3036984
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. https://doi.org/10.1109/LRA.2023.3329626
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. https://doi.org/10.1109/SMC53992.2023.10394619
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. https://doi.org/10.1109/TITS.2023.3254579
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. https://doi.org/10.1109/TNNLS.2022.3142822
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. https://doi.org/10.1080/10447318.2022.2102716
Kolekar, S., de Winter, J., & Abbink, D. (2020). Human-like driving behaviour emerges from a risk-based driver model. Nature Communications, 11(1). https://doi.org/10.1038/s41467-020-18353-4
Kothari, P., & Alahi, A. (2023). Safety-compliant generative adversarial networks for human trajectory forecasting. IEEE Transactions on Intelligent Transportation Systems, 24(4), 4251–4261. https://doi.org/10.1109/TITS.2022.3233906
Kothari, P., Kreiss, S., & Alahi, A. (2022). Human trajectory forecasting in crowds: A deep learning perspective. IEEE Transactions on Intelligent Transportation Systems. https://doi.org/10.1109/TITS.2021.3069362
Kothari, P., Sifringer, B., & Alahi, A. (2021). Interpretable social anchors for human trajectory forecasting in crowds. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 15551–15561. https://doi.org/10.1109/CVPR46437.2021.01530
Landolfi, N. C., & Dragan, A. D. (2018). Social cohesion in autonomous driving. IEEE International Conference on Intelligent Robots and Systems, 8118–8125. https://doi.org/10.1109/IROS.2018.8593682
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. Communications in Transportation Research, 1(September). https://doi.org/10.1016/j.commtr.2021.100019
Li, C., Trinh, T., Wang, L., Liu, C., Tomizuka, M., & Zhan, W. (2022). Efficient game-theoretic planning with prediction heuristic for socially-compliant autonomous driving. IEEE Robotics and Automation Letters, 7(4), 10248–10255. https://doi.org/10.1109/LRA.2022.3191241
Li, Q., Peng, Z., Feng, L., Zhang, Q., Xue, Z., & Zhou, B. (2023). MetaDrive: composing diverse driving scenarios for generalizable reinforcement learning. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/TPAMI.2022.3190471
Liu, J., Qi, X., Hang, P., & Sun, J. (2024). Enhancing social decision-making of autonomous vehicles: A mixed-strategy game approach with interaction orientation identification. IEEE Transactions on Vehicular Technology, PP, 1–14. https://doi.org/10.1109/TVT.2024.3385750
Liu, J., Zhou, D., Hang, P., Ni, Y., & Sun, J. (2024). Towards socially responsive autonomous vehicles: A reinforcement learning framework with driving priors and coordination awareness. IEEE Transactions on Intelligent Vehicles, 9(1), 827–838. https://doi.org/10.1109/TIV.2023.3332080
Liu, L., Dugas, D., Cesari, G., Siegwart, R., & Dube, R. (2020). Robot navigation in crowded environments using deep reinforcement learning. IEEE International Conference on Intelligent Robots and Systems, 5671–5677. https://doi.org/10.1109/IROS45743.2020.9341540
Liu, M., Tseng, H. E., Filev, D., Girard, A., & Kolmanovsky, I. (2024). Safe and human-like autonomous driving: A predictor-corrector potential game approach. IEEE Transactions on Control Systems Technology, 32(3), 834–848. https://doi.org/10.1109/TCST.2023.3332438
Lu, H., Lu, C., Yu, Y., Xiong, G., & Gong, J. (2022). Autonomous overtaking for intelligent vehicles considering social preference based on hierarchical reinforcement learning. Automotive Innovation, 5(2), 195–208. https://doi.org/10.1007/s42154-022-00177-1
Nan, J., Deng, W., Zhang, R., Wang, Y., Zhao, R., & Ding, J. (2024). Interaction-aware planning with deep inverse reinforcement learning for human-like autonomous driving in merge scenarios. IEEE Transactions on Intelligent Vehicles, 9(1), 2714–2726. https://doi.org/10.1109/TIV.2023.3298912
Oliveira, L., Proctor, K., Burns, C. G., & Birrell, S. (2019). Driving style: How should an automated vehicle behave? Information (Switzerland), 10(6), 1–20. https://doi.org/10.3390/INFO10060219
Pellegrini, S., Ess, A., Schindler, K., & Van Gool, L. (2009). You’ll never walk alone: Modeling social behavior for multi-target tracking. Proceedings of the IEEE International Conference on Computer Vision. https://doi.org/10.1109/ICCV.2009.5459260
Peng, Z., Li, Q., Hui, K. M., Liu, C., & Zhou, B. (2021). Learning to simulate self-driven particles system with coordinated policy optimization. Advances in Neural Information Processing Systems, 13(NeurIPS), 10784–10797.
Pérez-Dattari, R., Brito, B., de Groot, O., Kober, J., & Alonso-Mora, J. (2022). Visually-guided motion planning for autonomous driving from interactive demonstrations. Engineering Applications of Artificial Intelligence, 116(August), 105277. https://doi.org/10.1016/j.engappai.2022.105277
Qin, L., Huang, Z., Zhang, C., Guo, H., Ang, M., & Rus, D. (2021). Deep imitation learning for autonomous navigation in dynamic pedestrian environments. Proceedings - IEEE International Conference on Robotics and Automation, 2021-May(Icra), 4108–4115. https://doi.org/10.1109/ICRA48506.2021.9561220
Reddy, A. K., Malviya, V., & Kala, R. (2021). Social cues in the autonomous navigation of indoor mobile robots. International Journal of Social Robotics, 13(6), 1335–1358. https://doi.org/10.1007/s12369-020-00721-1
Robicquet, A., Sadeghian, A., Alahi, A., & Savarese, S. (2016). Learning social etiquette: Human trajectory understanding in crowded scenes. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). https://doi.org/10.1007/978-3-319-46484-8_33
Sadeghian, A., Kosaraju, V., Sadeghian, A., Hirose, N., Rezatofighi, H., & Savarese, S. (2019). SoPhie: An attentive GAN for predicting paths compliant to social and physical constraints. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019-June, 1349–1358. https://doi.org/10.1109/CVPR.2019.00144
Schneble, C. O., & Shaw, D. M. (2021). Driver’s views on driverless vehicles: Public perspectives on defining and using autonomous cars. Transportation Research Interdisciplinary Perspectives, 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. Proceedings of the National Academy of Sciences of the United States of America, 116(50), 2492–24978. https://doi.org/10.1073/pnas.1820676116
Shu, K., Mehrizi, R. V., Li, S., Pirani, M., & Khajepour, A. (2023). Human inspired autonomous intersection handling using game theory. IEEE Transactions on Intelligent Transportation Systems, 24(10), 11360–11371. https://doi.org/10.1109/TITS.2023.3281390
Singamaneni, P. T., Bachiller-Burgos, P., Manso, L. J., Garrell, A., Sanfeliu, A., Spalanzani, A., & Alami, R. (2024). A survey on socially aware robot navigation: Taxonomy and future challenges. International Journal of Robotics Research, 0(0), 1–40. https://doi.org/10.1177/02783649241230562
Song, W., Xiong, G., & Chen, H. (2016). Intention-aware autonomous driving decision-making in an uncontrolled intersection. Mathematical Problems in Engineering, 2016. https://doi.org/10.1155/2016/1025349
Sun, L., Zhan, W., Chan, C. Y., & Tomizuka, M. (2019). Behavior planning of autonomous cars with social perception. IEEE Intelligent Vehicles Symposium, Proceedings, 2019-June(Iv), 207–213. https://doi.org/10.1109/IVS.2019.8814223
Sun, L., Zhan, W., Tomizuka, M., & Dragan, A. D. (2018). Courteous autonomous cars. IEEE International Conference on Intelligent Robots and Systems, 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. Transport Reviews, 42(2), 245–267. https://doi.org/10.1080/01441647.2021.1971794
Taghavifar, H., & Mohammadzadeh, A. (2024). Integrating deep reinforcement learning and social-behavioral cues: A new human-centric cyber-physical approach in automated vehicle decision-making. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering. https://doi.org/10.1177/09544070241230126
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. In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021)-Workshop on Autonomous Driving: Perception, Prediction and Planning. IEEE/CVF, 1–8.
Toghi, B., Valiente, R., Sadigh, D., Pedarsani, R., & Fallah, Y. P. (2021b). Cooperative autonomous vehicles that sympathize with human drivers. IEEE International Conference on Intelligent Robots and Systems, 4517–4524. https://doi.org/10.1109/IROS51168.2021.9636151
Toghi, B., Valiente, R., Sadigh, D., Pedarsani, R., & Fallah, Y. P. (2022). Social coordination and altruism in autonomous driving. IEEE Transactions on Intelligent Transportation Systems, 23(12), 24791–24804. https://doi.org/10.1109/TITS.2022.3207872
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