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:自主车辆(AVs)将首先在高速公路上广泛部署;然而,公路交汇的复杂性将成为部署AVs的瓶颈。不同公路交汇下,AV应该进行充分测试,由于缺乏包含不同公路交汇的数据集,这仍然具有挑战性。在本文件中,我们提议采用模型驱动方法FLYOVS,以生成由不同互换组成的数据集,其中包括可计量的多样性覆盖。首先,FLYOVS提出一个标志性分解,以模拟交汇的地形学。第二,FLYOVS采用现实世界互换,作为保证地形实用性的投入,通过对相应的地形模型进行抽取不同的等同等级。第三,FLYULEOVS为每个地形类确定相应的坡道地形特征,并利用K-way组合覆盖和差异演化来生成混凝土互换。为了说明生成的互换数据集的多样性和适用性,我们测试SUMO的内建交通流量控制算,以及燃料消耗轨迹跟踪到Al-libabas的电路段跟踪结果,在数据跟踪中,通过不同的燃料流流压记录。