GM3: A General Physical Model for Micro-Mobility Vehicles

2025-10-09

AI Summary

Existing micro-mobility vehicle (MMV) dynamics models rely on simplified kinematics or configuration-specific physics, neglecting critical nonlinear effects—including tire slip, load transfer, and rider-vehicle lean—thus lacking unified modeling capability across diverse wheeled configurations. Method: We propose GM3, a general-purpose physics-based model integrating the brush tire model and rigid-body dynamics to explicitly capture tire–ground contact forces, vertical load redistribution, and coupled rider–vehicle lean. A decoupled simulation framework enables arbitrary wheel layouts and model-agnostic dynamic comparison. Using fixed-step RK4 integration, GM3 incorporates human–machine interaction and script-based control for high-fidelity real-time trajectory simulation and logging. Results: Evaluated on the “deathCircle” scenario from the Stanford Drone Dataset, GM3 achieves centimeter-level trajectory fidelity and accurate dynamic reconstruction for cyclists, scooters, and push-carts, demonstrating strong generalizability and precision across heterogeneous MMV classes.

Technology Category

• Planning, Routing, and Scheduling: Model-Based Reasoning
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Computer Vision: Low Level & Physics-based Vision

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

Modeling the dynamics of micro-mobility vehicles (MMV) is becoming increasingly important for training autonomous vehicle systems and building urban traffic simulations. However, mainstream tools rely on variants of the Kinematic Bicycle Model (KBM) or mode-specific physics that miss tire slip, load transfer, and rider/vehicle lean. To our knowledge, no unified, physics-based model captures these dynamics across the full range of common MMVs and wheel layouts. We propose the "Generalized Micro-mobility Model" (GM3), a tire-level formulation based on the tire brush representation that supports arbitrary wheel configurations, including single/double track and multi-wheel platforms. We introduce an interactive model-agnostic simulation framework that decouples vehicle/layout specification from dynamics to compare the GM3 with the KBM and other models, consisting of fixed step RK4 integration, human-in-the-loop and scripted control, real-time trajectory traces and logging for analysis. We also empirically validate the GM3 on the Stanford Drone Dataset's deathCircle (roundabout) scene for biker, skater, and cart classes.

Problem

• Modeling micro-mobility vehicle dynamics for autonomous systems
• Capturing tire slip and rider lean in unified physics
• Supporting arbitrary wheel configurations across MMV types

Innovation

• Tire brush model for arbitrary wheel configurations
• Model-agnostic simulation with RK4 integration
• Empirical validation using drone dataset trajectories