Autonomous Driving From the Sky: Design and End-to-End Performance Evaluation

TL;DR

This paper proposes a novel UAV-assisted framework using mmwave communication to enhance autonomous vehicle perception, evaluated through comprehensive simulations considering real UAV data and various communication strategies.

Contribution

It introduces an integrated UAV-ground communication framework at mmwave frequencies for autonomous driving, combining aerial perception with ground processing in a full-stack simulation.

Findings

Trade-offs between centralized and distributed processing analyzed

Throughput, latency, and reliability impacts studied

Simulation results demonstrate benefits of UAV-assisted perception

Abstract

For autonomous vehicles to operate without human intervention, information sharing from local sensors plays a fundamental role. This can be challenging to handle with bandwidth-constrained communication systems, which calls for the adoption of new wireless technologies, like in the mmwave bands, to solve capacity issues. Another approach is to exploit uav, able to provide human users and their cars with an aerial bird's-eye view of the scene otherwise unavailable, thus offering broader and more centralized observations. In this article we combine both aspects and design a novel framework in which uav, operating at mmwave, broadcast sensory information to the ground as a means to extend the (local) perception range of vehicles. To do so, we conduct a full-stack end-to-end simulation campaign with ns-3 considering real UAV data from the Stanford Drone Dataset, and study four scenarios representing different uav-to-ground communication strategies. Our results focus on the trade-off between centralized data processing in the sky vs. distributed local processing on the ground, with considerations related to the throughput, latency and reliability of the communication process.