It has become a consensus that autonomous vehicles (AVs) will first be widely deployed on highways. However, the complexity of highway interchanges becomes the bottleneck for deploying AVs. An AV should be sufficiently tested under different highway interchanges, which is still challenging due to the lack of available datasets containing diverse highway interchanges. In this paper, we propose a model-driven method, FLYOVER, to generate a dataset consisting of diverse interchanges with measurable diversity coverage. First, FLYOVER proposes a labeled digraph to model the topology of an interchange. Second, FLYOVER takes real-world interchanges as input to guarantee topology practicality and extracts different topology equivalence classes by classifying the corresponding topology models. Third, for each topology class, FLYOVER identifies the corresponding geometrical features for the ramps and generates concrete interchanges using k-way combinatorial coverage and differential evolution. To illustrate the diversity and applicability of the generated interchange dataset, we test the built-in traffic flow control algorithm in SUMO and the fuel-optimization trajectory tracking algorithm deployed to Alibaba's autonomous trucks on the dataset. The results show that except for the geometrical difference, the interchanges are diverse in throughput and fuel consumption under the traffic flow control and trajectory tracking algorithms, respectively.

翻译：自动驾驶汽车将首先在高速公路上大规模部署已成为共识。然而，高速公路立交桥的复杂性成为部署自动驾驶汽车的瓶颈。自动驾驶汽车需要在不同的高速公路立交桥下进行充分测试，但由于缺乏包含多样化立交桥的可用数据集，这一目标仍具挑战性。本文提出一种模型驱动方法FLYOVER，用于生成具有可量化多样性覆盖的多样化立交桥数据集。首先，FLYOVER提出一种带标签的有向图对立交桥的拓扑结构进行建模；其次，FLYOVER以真实立交桥作为输入以保证拓扑实用性，并通过分类相应拓扑模型提取不同拓扑等价类；然后，对每个拓扑类，FLYOVER识别匝道对应的几何特征，利用k-way组合覆盖和差分进化算法生成具体立交桥。为展示生成立交桥数据集的多样性与适用性，我们在该数据集上测试了SUMO内置的交通流控制算法以及部署在阿里巴巴自动驾驶卡车上的燃油优化轨迹跟踪算法。结果表明，除几何差异外，这些立交桥在交通流控制和轨迹跟踪算法下分别体现出吞吐量和燃油消耗的多样性差异。