動的マニューバと交通制約を考慮した自動運転計画アルゴリズム
AutonoVi: Autonomous Vehicle Planning with Dynamic Maneuvers and Traffic Constraints
最適化ベースのマニューバ計画と制御速度障害物により、車線変更や急停止、歩行者・自転車回避、交通ルール遵守を統合した自動運転ナビゲーション手法を提案し、都市・高速のシミュレーションで検証した。
著者: Andrew Best, Sahil Narang, Daniel Barber, Dinesh Manocha
分類: cs.RO, cs.MA
原文アブストラクト
We present AutonoVi:, a novel algorithm for autonomous vehicle navigation that supports dynamic maneuvers and satisfies traffic constraints and norms. Our approach is based on optimization-based maneuver planning that supports dynamic lane-changes, swerving, and braking in all traffic scenarios and guides the vehicle to its goal position. We take into account various traffic constraints, including collision avoidance with other vehicles, pedestrians, and cyclists using control velocity obstacles. We use a data-driven approach to model the vehicle dynamics for control and collision avoidance. Furthermore, our trajectory computation algorithm takes into account traffic rules and behaviors, such as stopping at intersections and stoplights, based on an arc-spline representation. We have evaluated our algorithm in a simulated environment and tested its interactive performance in urban and highway driving scenarios with tens of vehicles, pedestrians, and cyclists. These scenarios include jaywalking pedestrians, sudden stops from high speeds, safely passing cyclists, a vehicle suddenly swerving into the roadway, and high-density traffic where the vehicle must change lanes to progress more effectively.
