Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
102 tokens/sec
GPT-4o
59 tokens/sec
Gemini 2.5 Pro Pro
43 tokens/sec
o3 Pro
6 tokens/sec
GPT-4.1 Pro
50 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

A merging interaction model explains human drivers' behaviour from input signals to decisions (2312.09776v1)

Published 15 Dec 2023 in cs.HC

Abstract: One of the bottlenecks of automated driving technologies is safe and socially acceptable interactions with human-driven vehicles, for example during merging. Driver models that provide accurate predictions of joint and individual driver behaviour of high-level decisions, safety margins, and low-level control inputs are required to improve the interactive capabilities of automated driving. Existing driver models typically focus on one of these aspects. Unified models capturing all aspects are missing which hinders understanding of the principles that govern human traffic interactions. This in turn limits the ability of automated vehicles to resolve merging interactions. Here, we present a communication-enabled interaction model based on risk perception with the potential to capture merging interactions on all three levels. Our model accurately describes human behaviour in a simplified merging scenario, addressing both individual actions (such as velocity adjustments) and joint actions (such as the order of merging). Contrary to other interaction models, our model does not assume humans are rational and explicitly accounts for communication between drivers. Our results demonstrate that communication and risk-based decision-making explain observed human interactions on multiple levels. This explanation improves our understanding of the underlying mechanisms of human traffic interactions and poses a step towards interaction-aware automated driving.

PDF HTML Abstract
Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Bookmark Chat Bubble Oval Streamline Icon: https://streamlinehq.com Chat (Pro)
Definition Search Book Streamline Icon: https://streamlinehq.com
References (66)
  1. C. D. Harper, C. T. Hendrickson, S. Mangones, and C. Samaras, “Estimating potential increases in travel with autonomous vehicles for the non-driving, elderly and people with travel-restrictive medical conditions,” Transportation Research Part C: Emerging Technologies, vol. 72, pp. 1–9, nov 2016.
  2. L. M. Clements and K. M. Kockelman, “Economic Effects of Automated Vehicles,” Transportation Research Record: Journal of the Transportation Research Board, vol. 2606, pp. 106–114, jan 2017.
  3. S. Pettigrew, “Why public health should embrace the autonomous car,” Australian and New Zealand Journal of Public Health, vol. 41, pp. 5–7, feb 2017.
  4. B. Brown, M. Broth, and E. Vinkhuyzen, “The Halting problem: Video analysis of self-driving cars in traffic,” Conference on Human Factors in Computing Systems - Proceedings, 2023.
  5. D. Sadigh, N. Landolfi, S. S. Sastry, S. A. Seshia, and A. D. Dragan, “Planning for cars that coordinate with people: leveraging effects on human actions for planning and active information gathering over human internal state,” Autonomous Robots, vol. 42, pp. 1405–1426, oct 2018.
  6. W. Schwarting, A. Pierson, J. Alonso-Mora, S. Karaman, and D. Rus, “Social behavior for autonomous vehicles,” Proceedings of the National Academy of Sciences, vol. 116, pp. 24972–24978, dec 2019.
  7. O. Siebinga, A. Zgonnikov, and D. Abbink, “A Human Factors Approach to Validating Driver Models for Interaction-aware Automated Vehicles,” ACM Transactions on Human-Robot Interaction, vol. 11, pp. 1–21, dec 2022.
  8. O. Siebinga, A. Zgonnikov, and D. A. Abbink, “Modelling communication-enabled traffic interactions,” Royal Society Open Science, vol. 10, may 2023.
  9. B. Brown, E. Laurier, and E. Vinkhuyzen, “Designing Motion: Lessons for Self-driving and Robotic Motion from Human Traffic Interaction,” Proceedings of the ACM on Human-Computer Interaction, vol. 7, no. GROUP, pp. 1–21, 2023.
  10. H. Kita, “A merging-giveway interaction model of cars in a merging section: A game theoretic analysis,” Transportation Research Part A: Policy and Practice, vol. 33, no. 3-4, pp. 305–312, 1999.
  11. O. Siebinga, A. Zgonnikov, and D. A. Abbink, “Merging in a Coupled Driving Simulator: How do drivers resolve conflicts?,” pp. 1–18, aug 2023.
  12. M. Treiber, A. Hennecke, and D. Helbing, “Congested traffic states in empirical observations and microscopic simulations,” Physical Review E - Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics, vol. 62, no. 2, pp. 1805–1824, 2000.
  13. N. Kauffmann, F. Winkler, F. Naujoks, and M. Vollrath, ““What Makes a Cooperative Driver?” Identifying parameters of implicit and explicit forms of communication in a lane change scenario,” Transportation Research Part F: Traffic Psychology and Behaviour, vol. 58, pp. 1031–1042, 2018.
  14. Q. Zhang, R. Langari, H. E. Tseng, D. Filev, S. Szwabowski, and S. Coskun, “A Game Theoretic Model Predictive Controller with Aggressiveness Estimation for Mandatory Lane Change,” IEEE Transactions on Intelligent Vehicles, vol. 5, no. 1, pp. 75–89, 2020.
  15. A. Kondyli and L. Elefteriadou, “Modeling driver behavior at freeway-ramp merges,” Transportation Research Record, no. 2249, pp. 29–37, 2011.
  16. J. A. Laval and L. Leclercq, “Microscopic modeling of the relaxation phenomenon using a macroscopic lane-changing model,” Transportation Research Part B: Methodological, vol. 42, no. 6, pp. 511–522, 2008.
  17. C. F. Choudhury, “Modeling Driving Decision with Latent Plans (PhD Dissertation),” no. 2002, 2007.
  18. R. M. Michaels and J. Fazio, “Driver behavior model of merging,” Transportation Research Record, no. 1213, pp. 4–10, 1989.
  19. Kazi Iftekhar Ahmed, Modeling Drivers’ Acceleration and Lane Changing Behavior. PhD thesis, 1999.
  20. A. Kesting, M. Treiber, and D. Helbing, “General lane-changing model MOBIL for car-following models,” Transportation Research Record, no. 1999, pp. 86–94, 2007.
  21. Q. Yang and H. N. Koutsopoulos, “A microscopic traffic simulator for evaluation of dynamic traffic management systems,” Transportation Research Part C: Emerging Technologies, vol. 4, no. 3 PART C, pp. 113–129, 1996.
  22. P. Hidas, “Modelling lane changing and merging in microscopic traffic simulation,” Transportation Research Part C: Emerging Technologies, vol. 10, no. 5-6, pp. 351–371, 2002.
  23. X. Wan, P. J. Jin, F. Yang, J. Zhang, and B. Ran, “Modeling Vehicle Interactions during Merge in Congested Weaving Section of Freeway Ramp,” Transportation Research Record: Journal of the Transportation Research Board, vol. 2421, no. 1, pp. 82–92, 2014.
  24. W. Daamen, M. Loot, and S. P. Hoogendoorn, “Empirical analysis of merging behavior at freeway on-ramp,” Transportation Research Record, no. 2188, pp. 108–118, 2010.
  25. F. Marczak, W. Daamen, and C. Buisson, “Merging behaviour: Empirical comparison between two sites and new theory development,” Transportation Research Part C: Emerging Technologies, vol. 36, pp. 530–546, 2013.
  26. C. Dong, J. M. Dolan, and B. Litkouhi, “Smooth Behavioral Estimation for Ramp Merging Control in Autonomous Driving,” IEEE Intelligent Vehicles Symposium, Proceedings, vol. 2018-June, no. Iv, pp. 1692–1697, 2018.
  27. H. Liu, W. Xin, Z. Adam, and J. Ban, “A game theoretical approach for modelling merging and yielding behaviour at freeway on-ramp section,” Transportation and Traffic Theory, no. January, pp. 1–15, 2007.
  28. S. Coskun, Q. Zhang, and R. Langari, “Receding Horizon Markov Game Autonomous Driving Strategy,” in 2019 American Control Conference (ACC), vol. 2019-July, pp. 1367–1374, IEEE, jul 2019.
  29. N. Li, D. W. Oyler, M. Zhang, Y. Yildiz, I. Kolmanovsky, and A. R. Girard, “Game theoretic modeling of driver and vehicle interactions for verification and validation of autonomous vehicle control systems,” IEEE Transactions on Control Systems Technology, vol. 26, no. 5, pp. 1782–1797, 2018.
  30. A. Ji and D. Levinson, “A review of game theory models of lane changing,” Transportmetrica A: Transport Science, vol. 9935, no. May, pp. 1–19, 2020.
  31. R. Krajewski, J. Bock, L. Kloeker, and L. Eckstein, “The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems,” in 2018 21st International Conference on Intelligent Transportation Systems (ITSC), vol. 2018-Novem, pp. 2118–2125, IEEE, nov 2018.
  32. J. A. Michon, “A Critical View of Driver Behavior Models: What Do We Know, What Should We Do?,” in Human Behavior and Traffic Safety, pp. 485–524, Boston, MA: Springer US, 1985.
  33. A. D. Dragan, K. C. Lee, and S. S. Srinivasa, “Legibility and predictability of robot motion,” ACM/IEEE International Conference on Human-Robot Interaction, vol. 1, pp. 301–308, 2013.
  34. O. Guest and A. E. Martin, “How Computational Modeling Can Force Theory Building in Psychological Science,” Perspectives on Psychological Science, vol. 16, no. 4, pp. 789–802, 2021.
  35. T. Salzmann, B. Ivanovic, P. Chakravarty, and M. Pavone, “Trajectron++: Dynamically-Feasible Trajectory Forecasting With Heterogeneous Data,” 2020.
  36. B. Brito, H. Zhu, W. Pan, and J. Alonso-Mora, “Social-VRNN: One-Shot Multi-modal Trajectory Prediction for Interacting Pedestrians,” no. CoRL, 2020.
  37. A. Mészáros, J. Alonso-Mora, and J. Kober, “TrajFlow: Learning the Distribution over Trajectories,” 2023.
  38. O. Siebinga, A. Zgonnikov, and D. Abbink, “Interactive merging behavior in a coupled driving simulator: Experimental framework and case study,” Human Factors in Transportation, vol. 60, pp. 516–525, 2022.
  39. Y. M. Lee, R. Madigan, O. Giles, L. Garach-Morcillo, G. Markkula, C. Fox, F. Camara, M. Rothmueller, S. A. Vendelbo-Larsen, P. H. Rasmussen, A. Dietrich, D. Nathanael, V. Portouli, A. Schieben, and N. Merat, “Road users rarely use explicit communication when interacting in today’s traffic: implications for automated vehicles,” Cognition, Technology & Work, vol. 23, pp. 367–380, may 2021.
  40. D. Premack and G. Woodruff, “Premack and Woodruff : Chimpanzee theory of mind,” Behavioral and Brain Sciences, vol. 4, pp. 515–526, 1978.
  41. G. Markkula, Y.-s. Lin, A. R. Srinivasan, J. Billington, M. Leonetti, A. H. Kalantari, Y. Yang, Y. M. Lee, R. Madigan, and N. Merat, “Explaining human interactions on the road by large-scale integration of computational psychological theory,” PNAS Nexus, vol. 2, pp. 1–13, may 2023.
  42. R. Tian, M. Tomizuka, and L. Sun, “Learning Human Rewards by Inferring Their Latent Intelligence Levels in Multi-Agent Games: A Theory-of-Mind Approach with Application to Driving Data,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4560–4567, IEEE, sep 2021.
  43. S. Kolekar, J. de Winter, and D. Abbink, “Human-like driving behaviour emerges from a risk-based driver model,” Nature Communications, vol. 11, p. 4850, dec 2020.
  44. V. Gabler, T. Stahl, G. Huber, O. Oguz, and D. Wollherr, “A game-theoretic approach for adaptive action selection in close proximity human-robot-collaboration,” Proceedings - IEEE International Conference on Robotics and Automation, pp. 2897–2903, 2017.
  45. F. Camara, N. Bellotto, S. Cosar, F. Weber, D. Nathanael, M. Althoff, J. Wu, J. Ruenz, A. Dietrich, G. Markkula, A. Schieben, F. Tango, N. Merat, and C. Fox, “Pedestrian Models for Autonomous Driving Part II: High-Level Models of Human Behaviour,” IEEE Transactions on Intelligent Transportation Systems, pp. 1–20, 2020.
  46. S. Nikolaidis, S. Nath, A. D. Procaccia, and S. Srinivasa, “Game-Theoretic Modeling of Human Adaptation in Human-Robot Collaboration,” ACM/IEEE International Conference on Human-Robot Interaction, vol. Part F1271, pp. 323–331, 2017.
  47. A. Turnwald, W. Olszowy, D. Wollherr, and M. Buss, “Interactive navigation of humans from a game theoretic perspective,” IEEE International Conference on Intelligent Robots and Systems, no. Iros, pp. 703–708, 2014.
  48. S. Kolekar, J. de Winter, and D. Abbink, “Which parts of the road guide obstacle avoidance? Quantifying the driver’s risk field,” Applied Ergonomics, vol. 89, p. 103196, nov 2020.
  49. E. Ward, N. Evestedt, D. Axehill, and J. Folkesson, “Probabilistic Model for Interaction Aware Planning in Merge Scenarios,” IEEE Transactions on Intelligent Vehicles, vol. 2, no. 2, pp. 1–1, 2017.
  50. R. Queiroz, D. Sharma, R. Caldas, K. Czarnecki, S. Garcia, T. Berger, and P. Pelliccione, “A Driver-Vehicle Model for ADS Scenario-based Testing,” pp. 1–16, 05 2022.
  51. F. Martinez-Gil, M. Lozano, I. García-Fernández, and F. Fernández, “Modeling, evaluation, and scale on artificial pedestrians: A literature review,” ACM Computing Surveys, vol. 50, no. 5, 2017.
  52. A. De Santis, B. Siciliano, A. De Luca, and A. Bicchi, “An atlas of physical human-robot interaction,” Mechanism and Machine Theory, vol. 43, no. 3, pp. 253–270, 2008.
  53. B. Sadrfaridpour and Y. Wang, “Collaborative Assembly in Hybrid Manufacturing Cells: An Integrated Framework for Human-Robot Interaction,” IEEE Transactions on Automation Science and Engineering, vol. 15, no. 3, pp. 1178–1192, 2018.
  54. G. Markkula, “Modeling driver control behavior in both routine and near-accident driving,” Proceedings of the Human Factors and Ergonomics Society, vol. 2014-Janua, pp. 879–883, 2014.
  55. G. Markkula, E. Boer, R. Romano, and N. Merat, “Sustained sensorimotor control as intermittent decisions about prediction errors: computational framework and application to ground vehicle steering,” Biological Cybernetics, vol. 112, no. 3, pp. 181–207, 2018.
  56. O. Siebinga, A. Zgonnikov, and D. Abbink, “Uncovering variability in human driving behavior through automatic extraction of similar traffic scenes from large naturalistic datasets,” in The 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2023), 2023.
  57. A. Zgonnikov, D. Abbink, and G. Markkula, “Should I Stay or Should I Go? Cognitive Modeling of Left-Turn Gap Acceptance Decisions in Human Drivers,” Human Factors, 2022.
  58. A. J. Fath, M. Lind, and G. P. Bingham, “Perception of time to contact of slow- and fast-moving objects using monocular and binocular motion information,” Attention, Perception, and Psychophysics, vol. 80, no. 6, pp. 1584–1590, 2018.
  59. B. Jörges and L. R. Harris, “Object speed perception during lateral visual self-motion,” Attention, Perception, and Psychophysics, vol. 84, no. 1, pp. 25–46, 2022.
  60. L. L. Hoberock, “A survey of longitudinal acceleration comfort studies in ground transportation vehicles,” Journal of Dynamic Systems, Measurement and Control, Transactions of the ASME, vol. 99, no. 2, pp. 76–84, 1977.
  61. J. A. E. Andersson, J. Gillis, G. Horn, J. B. Rawlings, and M. Diehl, “CasADi – A software framework for nonlinear optimization and optimal control,” Mathematical Programming Computation, 2018.
  62. S. Seabold and J. Perktold, “statsmodels: Econometric and statistical modeling with python,” in 9th Python in Science Conference, 2010.
  63. O. Siebinga, “simple-merging-experiment [Software],” 2022.
  64. O. Siebinga, A. Zgonnikov, and D. A. D. Abbink, “Data underlying the publication: Interactive merging behavior in a coupled driving simulator: Experimental framework and case study,” 2022.
  65. O. Siebinga, A. Zgonnikov, and D. A. D. Abbink, “Data underlying the publication: A model of dyadic merging interactions explains human drivers’ behaviour from input signals to decisions,” 2023.
  66. O. Siebinga, A. Zgonnikov, and D. Abbink, “Supplementary materials to the paper ”Merging in a Coupled Driving Simulator: How do drivers resolve conflicts?”.”
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (3)
  1. Olger Siebinga (6 papers)
  2. Arkady Zgonnikov (36 papers)
  3. David Abbink (7 papers)