二段階変分オクルージョンモデルによる遮蔽された交通エージェントを考慮した計画
Planning with Occluded Traffic Agents using Bi-Level Variational Occlusion Models
遮蔽された交通エージェントの位置と軌道を二段階で生成するBiVOを提案し、下流の計画に統合可能な軌道分布を出力することで、危険な場面での計画精度を向上させた。
著者: Filippos Christianos, Peter Karkus, Boris Ivanovic, Stefano V. Albrecht, Marco Pavone
分類: cs.LG, cs.RO
原文アブストラクト
Reasoning with occluded traffic agents is a significant open challenge for planning for autonomous vehicles. Recent deep learning models have shown impressive results for predicting occluded agents based on the behaviour of nearby visible agents; however, as we show in experiments, these models are difficult to integrate into downstream planning. To this end, we propose Bi-level Variational Occlusion Models (BiVO), a two-step generative model that first predicts likely locations of occluded agents, and then generates likely trajectories for the occluded agents. In contrast to existing methods, BiVO outputs a trajectory distribution which can then be sampled from and integrated into standard downstream planning. We evaluate the method in closed-loop replay simulation using the real-world nuScenes dataset. Our results suggest that BiVO can successfully learn to predict occluded agent trajectories, and these predictions lead to better subsequent motion plans in critical scenarios.
