Realistic pedestrian-driver interaction modelling using multi-agent RL with human perceptual-motor constraints

• URL: http://arxiv.org/abs/2510.27383v1
• Date: Fri, 31 Oct 2025 11:18:13 GMT
• Title: Realistic pedestrian-driver interaction modelling using multi-agent RL with human perceptual-motor constraints
• Authors: Yueyang Wang, Mehmet Dogar, Gustav Markkula,
• Abstract summary: We propose a multi-agent reinforcement learning framework that integrates both visual and motor constraints of pedestrian and driver agents.<n>Results show that the combined model with both visual and motor constraints performs best.<n>Our framework accounts for individual differences by modelling parameters controlling the human constraints as population-level distributions.
• Score: 3.4087048085988765
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modelling pedestrian-driver interactions is critical for understanding human road user behaviour and developing safe autonomous vehicle systems. Existing approaches often rely on rule-based logic, game-theoretic models, or 'black-box' machine learning methods. However, these models typically lack flexibility or overlook the underlying mechanisms, such as sensory and motor constraints, which shape how pedestrians and drivers perceive and act in interactive scenarios. In this study, we propose a multi-agent reinforcement learning (RL) framework that integrates both visual and motor constraints of pedestrian and driver agents. Using a real-world dataset from an unsignalised pedestrian crossing, we evaluate four model variants, one without constraints, two with either motor or visual constraints, and one with both, across behavioural metrics of interaction realism. Results show that the combined model with both visual and motor constraints performs best. Motor constraints lead to smoother movements that resemble human speed adjustments during crossing interactions. The addition of visual constraints introduces perceptual uncertainty and field-of-view limitations, leading the agents to exhibit more cautious and variable behaviour, such as less abrupt deceleration. In this data-limited setting, our model outperforms a supervised behavioural cloning model, demonstrating that our approach can be effective without large training datasets. Finally, our framework accounts for individual differences by modelling parameters controlling the human constraints as population-level distributions, a perspective that has not been explored in previous work on pedestrian-vehicle interaction modelling. Overall, our work demonstrates that multi-agent RL with human constraints is a promising modelling approach for simulating realistic road user interactions.

Related papers

• Optimization-Guided Diffusion for Interactive Scene Generation [52.23368750264419]
  We present OMEGA, an optimization-guided, training-free framework that enforces structural consistency and interaction awareness during diffusion-based sampling.<n>We show that OMEGA improves generation realism, consistency, and controllability, increasing the ratio of physically and behaviorally valid scenes.<n>Our approach can also generate $5times$ more near-collision frames with a time-to-collision under three seconds.
  arXiv  Detail & Related papers  (2025-12-08T15:56:18Z)
• SPACeR: Self-Play Anchoring with Centralized Reference Models [50.55045557371374]
  Sim agent policies are realistic, human-like, fast, and scalable in multi-agent settings.<n>Recent progress in imitation learning with large diffusion-based or tokenized models has shown that behaviors can be captured directly from human driving data.<n>We propose SPACeR, a framework that leverages a pretrained tokenized autoregressive motion model as a central reference policy.
  arXiv  Detail & Related papers  (2025-10-20T19:53:02Z)
• Game-Theoretic Modeling of Vehicle Unprotected Left Turns Considering Drivers' Bounded Rationality [17.5324678856791]
  We propose a novel decision-making model for vehicle unprotected left-turn scenarios.<n>Our model integrates game theory with considerations for drivers' bounded rationality.<n>Our findings contribute valuable insights into the vehicle decision-making behaviors with bounded rationality.
  arXiv  Detail & Related papers  (2025-07-02T02:22:11Z)
• Markov Regime-Switching Intelligent Driver Model for Interpretable Car-Following Behavior [19.229274803939983]
  We introduce a regime-switching framework that allows driving behavior to be governed by different IDM parameter sets.<n>We instantiate the framework using a Factorial Hidden Markov Model with IDM dynamics.
  arXiv  Detail & Related papers  (2025-06-17T17:55:42Z)
• Looking for a better fit? An Incremental Learning Multimodal Object Referencing Framework adapting to Individual Drivers [0.0]
  The rapid advancement of the automotive industry has rendered traditional methods of vehicle interaction, such as touch-based and voice command systems, inadequate for a widening range of non-driving related tasks, such as referencing objects outside of the vehicle. We propose textitIcRegress, a novel regression-based incremental learning approach that adapts to changing behavior and the unique characteristics of drivers engaged in the dual task of driving and referencing objects.
  arXiv  Detail & Related papers  (2024-01-29T12:48:56Z)
• Trajeglish: Traffic Modeling as Next-Token Prediction [67.28197954427638]
  A longstanding challenge for self-driving development is simulating dynamic driving scenarios seeded from recorded driving logs. We apply tools from discrete sequence modeling to model how vehicles, pedestrians and cyclists interact in driving scenarios. Our model tops the Sim Agents Benchmark, surpassing prior work along the realism meta metric by 3.3% and along the interaction metric by 9.9%.
  arXiv  Detail & Related papers  (2023-12-07T18:53:27Z)
• InterControl: Zero-shot Human Interaction Generation by Controlling Every Joint [67.6297384588837]
  We introduce a novel controllable motion generation method, InterControl, to encourage the synthesized motions maintaining the desired distance between joint pairs. We demonstrate that the distance between joint pairs for human-wise interactions can be generated using an off-the-shelf Large Language Model.
  arXiv  Detail & Related papers  (2023-11-27T14:32:33Z)
• Persistent-Transient Duality: A Multi-mechanism Approach for Modeling Human-Object Interaction [58.67761673662716]
  Humans are highly adaptable, swiftly switching between different modes to handle different tasks, situations and contexts. In Human-object interaction (HOI) activities, these modes can be attributed to two mechanisms: (1) the large-scale consistent plan for the whole activity and (2) the small-scale children interactive actions that start and end along the timeline. This work proposes to model two concurrent mechanisms that jointly control human motion.
  arXiv  Detail & Related papers  (2023-07-24T12:21:33Z)
• Modeling human road crossing decisions as reward maximization with visual perception limitations [23.561752465516047]
  We develop a model of human pedestrian crossing decisions based on computational rationality. We show that the proposed cognitive-RL model captures human-like patterns of gap acceptance and crossing initiation time. Our results suggest that this is instead a rational adaption to human perceptual limitations.
  arXiv  Detail & Related papers  (2023-01-27T14:20:35Z)
• Isolating and Leveraging Controllable and Noncontrollable Visual Dynamics in World Models [65.97707691164558]
  We present Iso-Dream, which improves the Dream-to-Control framework in two aspects. First, by optimizing inverse dynamics, we encourage world model to learn controllable and noncontrollable sources. Second, we optimize the behavior of the agent on the decoupled latent imaginations of the world model.
  arXiv  Detail & Related papers  (2022-05-27T08:07:39Z)
• Solution Concepts in Hierarchical Games under Bounded Rationality with Applications to Autonomous Driving [8.500525426182115]
  We create game theoretic models of driving behaviour using hierarchical games. We evaluate the behaviour models on the basis of model fit to naturalistic data, as well as their predictive capacity. Our results suggest that among the behaviour models evaluated, at the level of maneuvers, modeling driving behaviour as an adaptation of the Quantal level-k model with level-0 behaviour modelled as pure rule-following provides the best fit to naturalistic driving behaviour.
  arXiv  Detail & Related papers  (2020-09-21T17:13:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.