グラフ探索とモデル予測制御の統合による自動運転車の滑らかで効率的な経路計画
Integrated Graph Search and Model Predictive Control for Smooth and Efficient Path Planning in Autonomous Vehicles
グラフ探索で得た粗い経路を利用して凸な横方向安全回廊を構築し、その中でMPCにより経路を滑らかに最適化する経路計画手法を提案した。
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著者: Duc-Tien Bui, Ngoc Thinh Nguyen, Hung Duy Nguyen, Dong Bi, Tomislav Mihalj, Arno Eichberger
分類: cs.RO
原文アブストラクト
Path planning is a fundamental component of autonomous vehicles, where achieving safe, comfortable, and dynamically feasible paths while ensuring computational efficiency remains a significant challenge. This paper presents a sequential path planning framework in which a rough path obtained from graph search is explicitly exploited to guide a Model Predictive Control (MPC)-based path refinement. A rough path is first obtained via Dijkstra search on a discretized grid and is then used to construct a spatially varying convex lateral safety corridor that explicitly captures obstacle avoidance constraints, transforming discrete obstacle avoidance decisions into continuous feasibility constraints for optimization. Within this corridor, an MPC problem is formulated to refine the path, enabling efficient optimization while maintaining path smoothness by penalizing the third-order spatial derivative of the lateral offset over a prediction horizon. The proposed algorithm is evaluated in multiple overtaking scenarios on both straight and curved roads, including cases with single and multiple target vehicles, using high-fidelity environment simulations (i.e., CarMaker). Compared with the previous study, which used polynomial fitting and a quadratic programming method, the proposed approach consistently achieves lower lateral acceleration, curvature, and jerk while reducing computational cost by 28.08% on straight roads and 29.52% on curved roads. These results demonstrate that exploiting graph-search structure within an MPC formulation provides an effective balance between path smoothness and computational efficiency for autonomous vehicles in structured driving environments.
