Abstract
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.
Abstract (translated)
已经成为共识,自动驾驶车辆(AVs)将首先在高速公路上广泛部署。然而,高速公路交叉口的的复杂性成为部署AVs的瓶颈。一个AV应该在不同的高速公路交叉口中进行充分的测试,但由于缺乏包含多种高速公路交叉数据的dataset,仍然具有挑战性。在本文中,我们提出了一种基于模型的方法,FLYOVER,以生成包含多种交叉口的可测量多样性覆盖的dataset。首先,FLYOVER提出了一个标记的图来建模交叉口的拓扑结构。其次,FLYOVER使用现实世界的交叉口作为输入,以确保拓扑实际性,并提取不同拓扑等价类,通过分类相应的拓扑模型。第三,对于每个拓扑类,FLYOVER识别对应几何特征的 ramp 和生成基于 k-way组合覆盖和差分进化的 concrete 交叉口。为了展示生成的交叉口dataset的多样性和应用性,我们测试了SUMO内置的交通流量控制算法和部署到 Alibaba 的自主卡车上的 fuel-optimization 路径跟踪算法。结果显示,除了几何差异,在交通流量控制和路径跟踪算法下,交叉口的流量和燃料消耗都有所不同。
URL
https://arxiv.org/abs/2301.12738