  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.
