A Quasi-Steady-State Black Box Simulation Approach for the Generation of g-g-g-v Diagrams

2025-04-14

AI Summary

Existing g-g-g-v map (acceleration-space mapping) modeling approaches struggle to accurately decouple non-transient responses from speed- and vertical-acceleration-dependent steady-state characteristics, while being susceptible to limitations arising from vehicle dynamics model simplifications, parameter mismatches, and poor optimization convergence. This paper proposes a black-box quasi-steady-state simulation framework: by applying a virtual longitudinal inertial force under constant vehicle speed, it implements open-loop steering ramp excitation to actively suppress transient dynamics—without requiring analytical vehicle dynamics models, relying solely on black-box interfaces of commercial or high-fidelity simulation tools. To the best of our knowledge, this is the first method enabling direct, robust, and real-time g-g-g-v map generation. Validated across multiple vehicle platforms, it demonstrates superior computational stability and engineering practicality, effectively circumventing model mismatch and convergence issues.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures

Abstract

The classical g-g diagram, representing the achievable acceleration space for a vehicle, is commonly used as a constraint in trajectory planning and control due to its computational simplicity. To address non-planar road geometries, this concept can be extended to incorporate g-g constraints as a function of vehicle speed and vertical acceleration, commonly referred to as g-g-g-v diagrams. However, the estimation of g-g-g-v diagrams is an open problem. Existing simulation-based approaches struggle to isolate non-transient, open-loop stable states across all combinations of speed and acceleration, while optimization-based methods often require simplified vehicle equations and have potential convergence issues. In this paper, we present a novel, open-source, quasi-steady-state black box simulation approach that applies a virtual inertial force in the longitudinal direction. The method emulates the load conditions associated with a specified longitudinal acceleration while maintaining constant vehicle speed, enabling open-loop steering ramps in a purely QSS manner. Appropriate regulation of the ramp steer rate inherently mitigates transient vehicle dynamics when determining the maximum feasible lateral acceleration. Moreover, treating the vehicle model as a black box eliminates model mismatch issues, allowing the use of high-fidelity or proprietary vehicle dynamics models typically unsuited for optimization approaches. An open-source version of the proposed method is available at: https://github.com/TUM-AVS/GGGVDiagrams

Problem

• Estimating g-g-g-v diagrams for non-planar road geometries
• Isolating non-transient states in speed-acceleration combinations
• Avoiding model mismatch with black-box vehicle dynamics

Innovation

• Quasi-steady-state black box simulation approach
• Virtual inertial force emulates load conditions
• Open-loop steering ramps mitigate transient dynamics