Navigating Heat Exposure: Simulation of Route Planning Based on Visual Language Model Agents

• URL: http://arxiv.org/abs/2503.12731v1
• Date: Mon, 17 Mar 2025 01:49:46 GMT
• Title: Navigating Heat Exposure: Simulation of Route Planning Based on Visual Language Model Agents
• Authors: Haoran Ma, Kaihan Zhang, Jiannan Cai,
• Abstract summary: Existing methods fail to account for individual physiological variations and environmental perception mechanisms under thermal stress.<n>We propose a novel Vision Language Model (VLM)-driven Persona-Perception-Planning-Memory framework.<n>Our framework is highly cost-effective, with simulations costing 0.006USD and taking 47.81s per route.
• Score: 0.25602836891933073
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Heat exposure significantly influences pedestrian routing behaviors. Existing methods such as agent-based modeling (ABM) and empirical measurements fail to account for individual physiological variations and environmental perception mechanisms under thermal stress. This results in a lack of human-centred, heat-adaptive routing suggestions. To address these limitations, we propose a novel Vision Language Model (VLM)-driven Persona-Perception-Planning-Memory (PPPM) framework that integrating street view imagery and urban network topology to simulate heat-adaptive pedestrian routing. Through structured prompt engineering on Gemini-2.0 model, eight distinct heat-sensitive personas were created to model mobility behaviors during heat exposure, with empirical validation through questionnaire survey. Results demonstrate that simulation outputs effectively capture inter-persona variations, achieving high significant congruence with observed route preferences and highlighting differences in the factors driving agents decisions. Our framework is highly cost-effective, with simulations costing 0.006USD and taking 47.81s per route. This Artificial Intelligence-Generated Content (AIGC) methodology advances urban climate adaptation research by enabling high-resolution simulation of thermal-responsive mobility patterns, providing actionable insights for climate-resilient urban planning.

Related papers

• A Multimodal Physics-Informed Neural Network Approach for Mean Radiant Temperature Modeling [0.0]
  This study introduces a Physics-Informed Neural Network (PINN) approach that integrates shortwave and longwave radiation modeling with deep learning techniques.<n>By leveraging a multimodal dataset that includes meteorological data, built environment characteristics, and fisheye image-derived shading information, our model enhances predictive accuracy while maintaining physical consistency.
  arXiv  Detail & Related papers  (2025-03-11T14:36:08Z)
• Dream to Drive with Predictive Individual World Model [12.05377034777257]
  This paper presents a novel model-based reinforcement learning (MBRL) method with a predictive individual world model (PIWM) for autonomous driving.<n>PIWM describes the driving environment from an individual-level perspective and captures vehicles' interactive relations and their intentions.<n>It is trained in PIWM's imagination and effectively navigates in the urban driving scenes leveraging intention-aware latent states.
  arXiv  Detail & Related papers  (2025-01-28T06:18:29Z)
• Planning with Adaptive World Models for Autonomous Driving [50.4439896514353]
  We present nuPlan, a real-world motion planning benchmark that captures multi-agent interactions.<n>We learn to model such unique behaviors with BehaviorNet, a graph convolutional neural network (GCNN)<n>We also present AdaptiveDriver, a model-predictive control (MPC) based planner that unrolls different world models conditioned on BehaviorNet's predictions.
  arXiv  Detail & Related papers  (2024-06-15T18:53:45Z)
• SAFE-SIM: Safety-Critical Closed-Loop Traffic Simulation with Diffusion-Controllable Adversaries [94.84458417662407]
  We introduce SAFE-SIM, a controllable closed-loop safety-critical simulation framework. Our approach yields two distinct advantages: 1) generating realistic long-tail safety-critical scenarios that closely reflect real-world conditions, and 2) providing controllable adversarial behavior for more comprehensive and interactive evaluations. We validate our framework empirically using the nuScenes and nuPlan datasets across multiple planners, demonstrating improvements in both realism and controllability.
  arXiv  Detail & Related papers  (2023-12-31T04:14:43Z)
• Towards Causal Representations of Climate Model Data [18.82507552857727]
  This work delves into the potential of causal representation learning, specifically the emphCausal Discovery with Single-parent Decoding (CDSD) method. Our findings shed light on the challenges, limitations, and promise of using CDSD as a stepping stone towards more interpretable and robust climate model emulation.
  arXiv  Detail & Related papers  (2023-12-05T16:13:34Z)
• Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research [76.93956925360638]
  Waymax is a new data-driven simulator for autonomous driving in multi-agent scenes. It runs entirely on hardware accelerators such as TPUs/GPUs and supports in-graph simulation for training. We benchmark a suite of popular imitation and reinforcement learning algorithms with ablation studies on different design decisions.
  arXiv  Detail & Related papers  (2023-10-12T20:49:15Z)
• Reinforcement Learning with Human Feedback for Realistic Traffic Simulation [53.85002640149283]
  Key element of effective simulation is the incorporation of realistic traffic models that align with human knowledge. This study identifies two main challenges: capturing the nuances of human preferences on realism and the unification of diverse traffic simulation models.
  arXiv  Detail & Related papers  (2023-09-01T19:29:53Z)
• Decision Making for Autonomous Driving in Interactive Merge Scenarios via Learning-based Prediction [39.48631437946568]
  This paper focuses on the complex task of merging into moving traffic where uncertainty emanates from the behavior of other drivers. We frame the problem as a partially observable Markov decision process (POMDP) and solve it online with Monte Carlo tree search. The solution to the POMDP is a policy that performs high-level driving maneuvers, such as giving way to an approaching car, keeping a safe distance from the vehicle in front or merging into traffic.
  arXiv  Detail & Related papers  (2023-03-29T16:12:45Z)
• TrafficBots: Towards World Models for Autonomous Driving Simulation and Motion Prediction [149.5716746789134]
  We show data-driven traffic simulation can be formulated as a world model. We present TrafficBots, a multi-agent policy built upon motion prediction and end-to-end driving. Experiments on the open motion dataset show TrafficBots can simulate realistic multi-agent behaviors.
  arXiv  Detail & Related papers  (2023-03-07T18:28:41Z)
• Inference from Real-World Sparse Measurements [21.194357028394226]
  Real-world problems often involve complex and unstructured sets of measurements, which occur when sensors are sparsely placed in either space or time. Deep learning architectures capable of processing sets of measurements with positions varying from set to set and extracting readouts anywhere are methodologically difficult. We propose an attention-based model focused on applicability and practical robustness, with two key design contributions.
  arXiv  Detail & Related papers  (2022-10-20T13:42:20Z)
• Augmented Driver Behavior Models for High-Fidelity Simulation Study of Crash Detection Algorithms [2.064612766965483]
  We present a simulation platform for a hybrid transportation system that includes both human-driven and automated vehicles. We decompose the human driving task and offer a modular approach to simulating a large-scale traffic scenario. We analyze a large driving dataset to extract expressive parameters that would best describe different driving characteristics.
  arXiv  Detail & Related papers  (2022-08-10T19:59:16Z)
• A Transfer Learning approach to Heatmap Regression for Action Unit intensity estimation [50.261472059743845]
  Action Units (AUs) are geometrically-based atomic facial muscle movements. We propose a novel AU modelling problem that consists of jointly estimating their localisation and intensity. A Heatmap models whether an AU occurs or not at a given spatial location.
  arXiv  Detail & Related papers  (2020-04-14T16:51:13Z)
• Counterfactual Vision-and-Language Navigation via Adversarial Path Sampling [65.99956848461915]
  Vision-and-Language Navigation (VLN) is a task where agents must decide how to move through a 3D environment to reach a goal. One of the problems of the VLN task is data scarcity since it is difficult to collect enough navigation paths with human-annotated instructions for interactive environments. We propose an adversarial-driven counterfactual reasoning model that can consider effective conditions instead of low-quality augmented data.
  arXiv  Detail & Related papers  (2019-11-17T18:02:51Z)

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.