信頼モデリングによる自動運転車の協調知覚フレームワークTruPercept
TruPercept: Trust Modelling for Autonomous Vehicle Cooperative Perception from Synthetic Data
車車間通信で共有される知覚情報を相手の信頼度で重み付けして融合し、見通し外や遠距離の物体検出を強化する手法を提案。悪意ある・不正確な報告を含む合成データセットも公開した。
著者: Braden Hurl, Robin Cohen, Krzysztof Czarnecki, Steven Waslander
分類: cs.MA, cs.CV, cs.RO
原文アブストラクト
Inter-vehicle communication for autonomous vehicles (AVs) stands to provide significant benefits in terms of perception robustness. We propose a novel approach for AVs to communicate perceptual observations, tempered by trust modelling of peers providing reports. Based on the accuracy of reported object detections as verified locally, communicated messages can be fused to augment perception performance beyond line of sight and at great distance from the ego vehicle. Also presented is a new synthetic dataset which can be used to test cooperative perception. The TruPercept dataset includes unreliable and malicious behaviour scenarios to experiment with some challenges cooperative perception introduces. The TruPercept runtime and evaluation framework allows modular component replacement to facilitate ablation studies as well as the creation of new trust scenarios we are able to show.
