Brief Industry Paper: The Necessity of Adaptive Data Fusion in Infrastructure-Augmented Autonomous Driving System

TL;DR

This paper introduces the IAAD system for autonomous driving, emphasizing the importance of adaptive data fusion methods to handle network variability in real-world deployment.

Contribution

It provides a comprehensive system design overview and proposes new adaptive fusion methods to improve cooperative autonomous driving under fluctuating network conditions.

Findings

Network condition fluctuations are the main technical challenge.

Proposed 'inter-frame fusion' and 'planning fusion' methods complement existing techniques.

Each fusion method offers unique benefits and constraints.

Abstract

This paper is the first to provide a thorough system design overview along with the fusion methods selection criteria of a real-world cooperative autonomous driving system, named Infrastructure-Augmented Autonomous Driving or IAAD. We present an in-depth introduction of the IAAD hardware and software on both road-side and vehicle-side computing and communication platforms. We extensively characterize the IAAD system in the context of real-world deployment scenarios and observe that the network condition that fluctuates along the road is currently the main technical roadblock for cooperative autonomous driving. To address this challenge, we propose new fusion methods, dubbed "inter-frame fusion" and "planning fusion" to complement the current state-of-the-art "intra-frame fusion". We demonstrate that each fusion method has its own benefit and constraint.