车辆模型在特定横向加速度下会失效，需警惕复杂度不足带来的安全风险。
Model Validity in Observers: When to Increase the Complexity of Your Model?
- 通过实测对比，发现常见车辆模型在横向加速度超过阈值后严重失准
- 确定了模型可用的安全操作区间：横向加速度低于1.2g
- 验证了学习型观测器在模型失效时仍能保持稳定性能，适合高动态场景
车辆模型有效性是自动驾驶系统准确与安全运行的关键。本文分析了文献中常用的多种车辆模型在真实车辆上的表现，证明在特定横向加速度以上时存在严重精度问题。研究明确了模型可准确描述车辆行为的横向加速度范围（<1.2g）。在此基础上，探讨了采用学习方法建模车辆行为的必要性。进一步研究了模型有效性对状态观测器的影响，对比了基于模型的观测器与基于学习的观测器性能。结果表明，当模型失效时，学习型观测器仍能维持稳定估计，而传统模型依赖型方法则出现明显偏差。本工作强调了模型有效性的核心作用，并给出了模型可安全使用的明确操作域。
原文摘要 · Abstract (English)
Model validity is key to the accurate and safe behavior of autonomous vehicles. Using invalid vehicle models in the different plan and control vehicle frameworks puts the stability of the vehicle, and thus its safety at stake. In this work, we analyze the validity of several popular vehicle models used in the literature with respect to a real vehicle and we prove that serious accuracy issues are encountered beyond a specific lateral acceleration point. We set a clear lateral acceleration domain in which the used models are an accurate representation of the behavior of the vehicle. We then target the necessity of using learned methods to model the vehicle's behavior. The effects of model validity on state observers are investigated. The performance of model-based observers is compared to learning-based ones. Overall, the presented work emphasizes the validity of vehicle models and presents clear operational domains in which models could be used safely.
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