寻找停车位占用了外卖配送中不成比例的时间，但现有系统均未解决针对商户入口的精确停车点选择问题。我们提出 ParkSense 框架，该框架在自动驾驶车辆（AV）低风险空闲状态下——例如红灯等待、交通拥堵、停车场内低速缓行——复用闲置计算资源，在预缓存的卫星图像与街景图像上运行视觉-语言模型（VLM），以识别商户入口及合法停车区域。我们形式化定义了“配送感知的精准停车”（Delivery-Aware Precision Parking, DAPP）问题；证明量化后的 7B 参数 VLM 在 HW4 级硬件上可在 4–8 秒内完成推理；并估算该方案在美国可为每位司机带来每年 3,000–8,000 美元的收入增长。本文还指出了这一尚未被探索的交叉领域——即自动驾驶、计算机视觉与末端物流——所面临的五个开放研究方向。

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.