ParkSense: 配達ドライバーはどこに駐車すべきか？アイドル状態の自動運転計算と視覚言語モデルの活用
ParkSense: Where Should a Delivery Driver Park? Leveraging Idle AV Compute and Vision-Language Models
配達時の駐車場所選定を自動化するため、自動運転車のアイドル計算資源を利用して視覚言語モデルで衛星・街並み画像から入口と駐車可能エリアを特定するフレームワークを提案した。
分類: cs.CV, cs.RO
原文アブストラクト
Finding parking consumes a disproportionate share of food delivery time, yet no system addresses precise parking-spot selection relative to merchant entrances. We propose ParkSense, a framework that repurposes idle compute during low-risk AV states -- queuing at red lights, traffic congestion, parking-lot crawl -- to run a Vision-Language Model (VLM) on pre-cached satellite and street view imagery, identifying entrances and legal parking zones. We formalize the Delivery-Aware Precision Parking (DAPP) problem, show that a quantized 7B VLM completes inference in 4-8 seconds on HW4-class hardware, and estimate annual per-driver income gains of 3,000-8,000 USD in the U.S. Five open research directions are identified at this unexplored intersection of autonomous driving, computer vision, and last-mile logistics.