Conventional vehicles are certified through classical approaches, where different physical certification tests are set up on test tracks to assess the required safety levels. These approaches are well suited for vehicles with limited complexity and limited interactions with other entities as last-second resources. However, these approaches do not allow the evaluation of safety with real behaviors for critical and edge cases nor the evaluation of the ability to anticipate them in the mid or long term. This is particularly relevant for automated and autonomous driving functions that make use of advanced predictive systems to anticipate future actions and motions to be considered in the path planning layer. In this article, we present and analyze the results of physical tests on the proving grounds of several predictive systems in automated driving functions developed within the framework of the BRidging Gaps for the Adoption of Automated VEhicles (BRAVE) project. Based on our experience in testing predictive automated driving functions, we identify the main limitations of current physical testing approaches when dealing with predictive systems, analyze the main challenges ahead, and provide a set of practical actions and recommendations to consider in future physical testing procedures for automated and autonomous driving functions.

中文翻译：

---

测试预测性自动驾驶系统：经验教训和未来建议

传统车辆通过经典方法进行认证，在测试轨道上设置不同的物理认证测试以评估所需的安全水平。这些方法非常适合作为最后一秒资源的复杂性有限且与其他实体交互有限的车辆。然而，这些方法不允许评估关键和边缘情况的真实行为的安全性，也不允许评估在中期或长期预测它们的能力。这对于利用先进的预测系统来预测路径规划层中要考虑的未来动作和运动的自动和自动驾驶功能尤其重要。在本文中，我们在自动驾驶功能的几个预测系统的试验场上展示和分析物理测试的结果，这些系统是在自动驾驶汽车采用的桥梁 (BRAVE) 项目框架内开发的。基于我们在预测性自动驾驶功能测试方面的经验，我们确定了当前物理测试方法在处理预测系统时的主要局限性，分析了未来的主要挑战，并提供了一套实际行动和建议，以供在未来的物理测试程序中考虑自动驾驶和自动驾驶功能。