高速道路の自動運転に向けた車線変更の開始と計画手法
Lane-Change Initiation and Planning Approach for Highly Automated Driving on Freeways
自然な運転データから人間の車線変更判断を学習し、安全性制約付きの有限時間最適化で計画する低計算コスト手法を提案。人間の判断を最大92%再現し、追い越しシミュレーションで実証した。
著者: Salar Arbabi, Shilp Dixit, Ziyao Zheng, David Oxtoby, Alexandros Mouzakitis, Saber Fallah
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
Quantifying and encoding occupants' preferences as an objective function for the tactical decision making of autonomous vehicles is a challenging task. This paper presents a low-complexity approach for lane-change initiation and planning to facilitate highly automated driving on freeways. Conditions under which human drivers find different manoeuvres desirable are learned from naturalistic driving data, eliminating the need for an engineered objective function and incorporation of expert knowledge in form of rules. Motion planning is formulated as a finite-horizon optimisation problem with safety constraints. It is shown that the decision model can replicate human drivers' discretionary lane-change decisions with up to 92% accuracy. Further proof of concept simulation of an overtaking manoeuvre is shown, whereby the actions of the simulated vehicle are logged while the dynamic environment evolves as per ground truth data recordings.
