Real-Time Dynamic Map With Crowdsourcing Vehicles in Edge Computing

Document Type

Article

Publication Date

4-1-2023

Abstract

Autonomous driving perceives surroundings with line-of-sight sensors that are compromised under environmental uncertainties. To achieve real time global information in high definition map, we investigate to share perception information among connected and automated vehicles. However, it is challenging to achieve real time perception sharing under varying network dynamics in automotive edge computing. In this paper, we propose a novel real time dynamic map, named LiveMap to detect, match, and track objects on the road. We design the data plane of LiveMap to efficiently process individual vehicle data with multiple sequential computation components, including detection, projection, extraction, matching and combination. We design the control plane of LiveMap to achieve adaptive vehicular offloading with two new algorithms (central and distributed) to balance the latency and coverage performance based on deep reinforcement learning techniques. We conduct extensive evaluation through both realistic experiments on a small-scale physical testbed and network simulations on an edge network simulator. The results suggest that LiveMap significantly outperforms existing solutions in terms of latency, coverage, and accuracy.

Identifier

85139864044 (Scopus)

Publication Title

IEEE Transactions on Intelligent Vehicles

External Full Text Location

https://doi.org/10.1109/TIV.2022.3214119

e-ISSN

23798858

First Page

2810

Last Page

2820

Issue

4

Volume

8

Grant

2212050

Fund Ref

National Science Foundation

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