FLYOVER: 自動運転車テスト用の多様な高速道路インターチェンジを生成するモデル駆動手法
FLYOVER: A Model-Driven Method to Generate Diverse Highway Interchanges for Autonomous Vehicle Testing
実在のインターチェンジを入力に、トポロジーと幾何形状をモデル化し、組合せ被覆と進化計算で多様なインターチェンジのデータセットを生成する手法を提案した。
著者: Yuan Zhou, Gengjie Lin, Yun Tang, Kairui Yang, Wei Jing, Ping Zhang, Junbo Chen, Liang Gong, Yang Liu
分類: cs.RO
原文アブストラクト
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.
