提出一种新方法生成车辆加速度图,支持复杂路况下的安全驾驶规划。
A Quasi-Steady-State Black Box Simulation Approach for the Generation of g-g-g-v Diagrams
- 基于准稳态黑箱仿真,通过虚拟惯性力模拟纵向加速度。
- 可在不同车速下稳定求出最大横向加速度,避免瞬态动态干扰。
- 兼容高保真车辆模型,适合自动驾驶系统开发与测试。
经典g-g图描述了车辆可实现的加速度空间,常用于轨迹规划与控制,因其计算简便。为应对非平面道路几何,该概念可扩展为考虑车速与垂直加速度的g-g-g-v图。然而,g-g-g-v图的估计仍是开放问题。现有基于仿真的方法难以在所有速度与加速度组合下分离非瞬态、开环稳定状态,而基于优化的方法通常依赖简化车辆方程,存在收敛困难。本文提出一种开源的准稳态(QSS)黑箱仿真方法,在纵向施加虚拟惯性力,模拟指定纵向加速度的同时保持恒定车速,从而实现纯QSS条件下的开环转向斜坡。合理调节转向斜坡速率可自然抑制瞬态车辆动力学影响,进而准确确定最大可行横向加速度。将车辆模型视为黑箱,避免了模型失配问题,可直接使用高保真或专有车辆动力学模型,适用于传统优化方法难以处理的场景。相关开源代码已发布于:https://github.com/TUM-AVS/GGGVDiagrams
原文摘要 · Abstract (English)
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
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