潜在的な協力度を考慮した合流計画フレームワーク
Uncertainty-Aware Online Merge Planning with Learned Driver Behavior
高速道路の合流において、周囲のドライバーの協力度をパーティクルフィルタで推定し、POMDPでオンライン計画を行う手法を提案した。
著者: Liam A. Kruse, Esen Yel, Ransalu Senanayake, Mykel J. Kochenderfer
分類: cs.RO
原文アブストラクト
Safe and reliable autonomy solutions are a critical component of next-generation intelligent transportation systems. Autonomous vehicles in such systems must reason about complex and dynamic driving scenes in real time and anticipate the behavior of nearby drivers. Human driving behavior is highly nuanced and specific to individual traffic participants. For example, drivers might display cooperative or non-cooperative behaviors in the presence of merging vehicles. These behaviors must be estimated and incorporated in the planning process for safe and efficient driving. In this work, we present a framework for estimating the cooperation level of drivers on a freeway and plan merging maneuvers with the drivers' latent behaviors explicitly modeled. The latent parameter estimation problem is solved using a particle filter to approximate the probability distribution over the cooperation level. A partially observable Markov decision process (POMDP) that includes the latent state estimate is solved online to extract a policy for a merging vehicle. We evaluate our method in a high-fidelity automotive simulator against methods that are agnostic to latent states or rely on $\textit{a priori}$ assumptions about actor behavior.
