Support for connected and autonomous vehicles (CAVs) is a major use case of 5G networks. Due to their large from factors, CAVs can be equipped with multiple radio antennas, cameras, LiDAR and other sensors. In other words, they are "giant" mobile integrated communications and sensing devices. The data collected can not only facilitate edge-assisted autonomous driving, but also enable intelligent radio resource allocation by cellular networks. In this paper we conduct an initial study to assess the feasibility of delivering multi-modal sensory data collected by vehicles over emerging commercial 5G networks. We carried out an "in-the-wild" drive test and data collection campaign between Minneapolis and Chicago using a vehicle equipped with a 360° camera, a LiDAR device, multiple smart phones and a professional 5G network measurement tool. Using the collected multi-modal data, we conduct trace-driven experiments in a local streaming testbed to analyze the requirements and performance of streaming multi-modal sensor data over existing 4G/5G networks. We reveal several notable findings and point out future research directions.
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This content will become publicly available on May 13, 2025
SmallMap: Low-cost Community Road Map Sensing with Uncertain Delivery Behavior
Accurate road networks play a crucial role in modern mobile applications such as navigation and last-mile delivery. Most existing studies primarily focus on generating road networks in open areas like main roads and avenues, but little attention has been given to the generation of community road networks in closed areas such as residential areas, which becomes more and more significant due to the growing demand for door-to-door services such as food delivery. This lack of research is primarily attributed to challenges related to sensing data availability and quality. In this paper, we design a novel framework called SmallMap that leverages ubiquitous multi-modal sensing data from last-mile delivery to automatically generate community road networks with low costs. Our SmallMap consists of two key modules: (1) a Trajectory of Interest Detection module enhanced by exploiting multi-modal sensing data collected from the delivery process; and (2) a Dual Spatio-temporal Generative Adversarial Network module that incorporates Trajectory of Interest by unsupervised road network adaptation to generate road networks automatically. To evaluate the effectiveness of SmallMap, we utilize a two-month dataset from one of the largest logistics companies in China. The extensive evaluation results demonstrate that our framework significantly outperforms state-of-the-art baselines, achieving a precision of 90.5%, a recall of 87.5%, and an F1-score of 88.9%, respectively. Moreover, we conduct three case studies in Beijing City for courier workload estimation, Estimated Time of Arrival (ETA) in last-mile delivery, and fine-grained order assignment.
more » « less- Award ID(s):
- 2047822
- PAR ID:
- 10538835
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
- Volume:
- 8
- Issue:
- 2
- ISSN:
- 2474-9567
- Page Range / eLocation ID:
- 1 to 26
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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