日本フィジカルAI新聞

世界のフィジカルAIを、日本語で。

安全性検証arXiv:2104.06178

認証付き制御:自動運転車の検証可能な安全性のためのアーキテクチャ

Certified Control: An Architecture for Verifiable Safety of Autonomous Vehicles

シェア:XThreadsFacebookLINEはてブBluesky

複雑な知覚を検証する代わりに、制御器が安全性の証明書を生成し、小型で形式検証可能なランタイムモニタがそれを検査する新たな安全アーキテクチャを提案し、LiDARデータの証明書で実証した。

著者: Daniel Jackson, Valerie Richmond, Mike Wang, Jeff Chow, Uriel Guajardo, Soonho Kong, Sergio Campos, Geoffrey Litt, Nikos Arechiga

分類: cs.RO, cs.LO, cs.SE, cs.SY, eess.SY

原文アブストラクト

Widespread adoption of autonomous cars will require greater confidence in their safety than is currently possible. Certified control is a new safety architecture whose goal is two-fold: to achieve a very high level of safety, and to provide a framework for justifiable confidence in that safety. The key idea is a runtime monitor that acts, along with sensor hardware and low-level control and actuators, as a small trusted base, ensuring the safety of the system as a whole. Unfortunately, in current systems complex perception makes the verification even of a runtime monitor challenging. Unlike traditional runtime monitoring, therefore, a certified control monitor does not perform perception and analysis itself. Instead, the main controller assembles evidence that the proposed action is safe into a certificate that is then checked independently by the monitor. This exploits the classic gap between the costs of finding and checking. The controller is assigned the task of finding the certificate, and can thus use the most sophisticated algorithms available (including learning-enabled software); the monitor is assigned only the task of checking, and can thus run quickly and be smaller and formally verifiable. This paper explains the key ideas of certified control and illustrates them with a certificate for LiDAR data and its formal verification. It shows how the architecture dramatically reduces the amount of code to be verified, providing an end-to-end safety analysis that would likely not be achievable in a traditional architecture.

関連論文

  1. 1Boston Dynamics、車輪付きAtlasの研究試作機を紹介研究・技術
  2. 2Hugging Face、ロボット学習基盤「LeRobot v0.7.0」を公開 対話操作と大規模データ学習に対応新製品・発表
  3. 3AGIBOTのG2 6台、上海のコンビニで24時間試験 1,730タスクを実施量産・展開
  4. 4Agility Robotics、ナスダックで取引開始 米国初の人型ロボット専業上場企業と報道業界動向
  5. 5UC BerkeleyとNVIDIAの研究者、AIエージェントでロボット制御コードを仮想環境で改善研究・技術
PR本紙発行元 EmplifAI