Proving ground has been a critical component in testing and validation for Connected and Automated Vehicles (CAV). Although quite a few world-class testing facilities have been under construction over the years, the evaluation of proving grounds themselves as testing approaches has rarely been studied. In this paper, we investigate the effectiveness of CAV proving grounds by its capability to recreate real-world traffic scenarios. We extract typical use cases from naturalistic driving events leveraging non-parametric Bayesian learning techniques. Then, we contribute to a generative sample-based optimization approach to assess the compatibility between traffic scenarios and proving ground road structure. We evaluate the effectiveness of our approach with three CAV testing facilities: Mcity, Almono (Uber ATG), and Kcity. Experiments show that our approach is effective in evaluating the capability of a given CAV proving ground to accommodate real-world driving scenarios.
翻译:验证是连接和自动化车辆(CAV)测试和验证的一个关键组成部分。虽然多年来建造了一些世界级的测试设施,但很少研究作为测试方法的验证依据本身的评价。在本文件中,我们调查CAV以其重新创造真实世界交通情景的能力为证明依据的有效性。我们利用非参数巴伊西亚学习技术,从自然驾驶事件中提取典型使用案例。然后,我们促进一种基于基因的样本优化方法,以评估交通情景与证明地面道路结构之间的兼容性。我们用三个CAV测试设施评估了我们的方法的有效性:Mcity、Almono(Uber ATG)和Kity。实验表明,我们的方法有效地评估了特定CAV证明平台适应现实世界驾驶情景的能力。