# Soar: Design and Deployment of A Smart Roadside Infrastructure System for Autonomous Driving

arXiv: https://arxiv.org/abs/2404.13786 · HTML: https://mlsanity.com/paper/2404.13786/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 81 references that are in the map.
- First posted on arXiv 21 Apr 2024.

## Builds on (most cited first, 9 of 9)

- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [PointPillars: Fast Encoders for Object Detection from Point Clouds](https://mlsanity.com/paper/1812.05784/) (2018)
- [Robot Operating System 2: Design, Architecture, and Uses In The Wild](https://mlsanity.com/paper/2211.07752/) (2022)
- [Cooperative Perception for 3D Object Detection in Driving Scenarios using Infrastructure Sensors](https://mlsanity.com/paper/1912.12147/) (2019)
- [NestDNN: Resource-Aware Multi-Tenant On-Device Deep Learning for Continuous Mobile Vision](https://mlsanity.com/paper/1810.10090/) (2018)
- [Providentia -- A Large-Scale Sensor System for the Assistance of Autonomous Vehicles and Its Evaluation](https://mlsanity.com/paper/1906.06789/) (2019)
- [Collaborative Mobile Edge Computing in 5G Networks: New Paradigms, Scenarios, and Challenges](https://mlsanity.com/paper/1612.03184/) (2016)
- [LiveMap: Real-Time Dynamic Map in Automotive Edge Computing](https://mlsanity.com/paper/2012.10252/) (2020)
- [Infrastructure Enabled Autonomy: A Distributed Intelligence Architecture for Autonomous Vehicles](https://mlsanity.com/paper/1802.04112/) (2018)

## Built on by (most cited first, 4 of 4)

- [Ultra-High-Frequency Harmony: mmWave Radar and Event Camera Orchestrate Accurate Drone Landing](https://mlsanity.com/paper/2502.14992/) (2025)
- [Responsive DNN Adaptation for Video Analytics against Environment Shift via Hierarchical Mobile-Cloud Collaborations](https://mlsanity.com/paper/2505.00745/) (2025)
- [Taming Asynchronous CPU-GPU Coupling for Frequency-aware Latency Estimation on Mobile Edge](https://mlsanity.com/paper/2604.15357/) (2026)
- [Denial of Deadline: Network-Driven Accuracy Collapse in Distributed Inference Pipelines](https://mlsanity.com/paper/2607.24692/) (2026)

## Abstract (arXiv)

Recently,smart roadside infrastructure (SRI) has demonstrated the potential of achieving fully autonomous driving systems. To explore the potential of infrastructure-assisted autonomous driving, this paper presents the design and deployment of Soar, the first end-to-end SRI system specifically designed to support autonomous driving systems. Soar consists of both software and hardware components carefully designed to overcome various system and physical challenges. Soar can leverage the existing operational infrastructure like street lampposts for a lower barrier of adoption. Soar adopts a new communication architecture that comprises a bi-directional multi-hop I2I network and a downlink I2V broadcast service, which are designed based on off-the-shelf 802.11ac interfaces in an integrated manner. Soar also features a hierarchical DL task management framework to achieve desirable load balancing among nodes and enable them to collaborate efficiently to run multiple data-intensive autonomous driving applications. We deployed a total of 18 Soar nodes on existing lampposts on campus, which have been operational for over two years. Our real-world evaluation shows that Soar can support a diverse set of autonomous driving applications and achieve desirable real-time performance and high communication reliability. Our findings and experiences in this work offer key insights into the development and deployment of next-generation smart roadside infrastructure and autonomous driving systems.