对比三种单车模型在短时位姿精度，助力自动驾驶规划模型选型
Short-Horizon Position Accuracy of Single-Track Models: Implications for Motion Planning of Autonomous Vehicles
- 基于实测数据对比三类单车模型的短时位姿预测精度
- 实车实验识别参数，验证模型在多种驾驶场景下的误差表现
- 揭示模型复杂度、参数质量与精度间的权衡，指导MPC应用选型
精确且计算高效的车辆模型对自动驾驶运动规划至关重要，其中位置精度直接影响轨迹可行性与安全性。然而，位置精度尚未在真实测量下系统评估。本文通过实车测试，在多种驾驶工况下对比了三种单车模型的短时位姿精度。模型参数通过搭载传感器的测试车辆开展专门实验进行识别。本研究不旨在找出单一最优模型，而是提供关于模型复杂度、参数化质量与位置精度之间权衡的深入见解，为模型预测控制（MPC）应用中的模型选择提供依据。
原文摘要 · Abstract (English)
Accurate and computationally efficient vehicle models are essential for motion planning of autonomous vehicles, where positional accuracy directly affects trajectory feasibility and safety. However, the positional accuracy has not been systematically evaluated against real measurements. Therefore, this paper compares the short-horizon positional accuracy of three single-track vehicle models against vehicle measurements across various driving maneuvers. Model parameters are identified through dedicated experiments with the instrumented test vehicle. Rather than identifying a single best model, this work aims to provide insight into the trade-offs between model complexity, parameterization quality, and positional accuracy for informed model selection in Model Predictive Control applications.
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