MMD-OPT：最大平均差異に基づく自動運転のためのサンプル効率の良い衝突リスク最小化
MMD-OPT : Maximum Mean Discrepancy Based Sample Efficient Collision Risk Minimization for Autonomous Driving
動的障害物の予測分布に対して、再生核ヒルベルト空間への埋め込みと最大平均差異（MMD）を用いて衝突リスクのサンプル効率の良い代理指標を定義し、軌道最適化に適用する手法を提案。CVaRベースの手法より少ないサンプルで安全な軌道が得られることを示した。
著者: Basant Sharma, Arun Kumar Singh
分類: cs.LG, cs.RO
原文アブストラクト
We propose MMD-OPT: a sample-efficient approach for minimizing the risk of collision under arbitrary prediction distribution of the dynamic obstacles. MMD-OPT is based on embedding distribution in Reproducing Kernel Hilbert Space (RKHS) and the associated Maximum Mean Discrepancy (MMD). We show how these two concepts can be used to define a sample efficient surrogate for collision risk estimate. We perform extensive simulations to validate the effectiveness of MMD-OPT on both synthetic and real-world datasets. Importantly, we show that trajectory optimization with our MMD-based collision risk surrogate leads to safer trajectories at low sample regimes than popular alternatives based on Conditional Value at Risk (CVaR).
