# Leveraging the Edge and Cloud for V2X-Based Real-Time Object Detection in Autonomous Driving

arXiv: https://arxiv.org/abs/2308.05234 · HTML: https://mlsanity.com/paper/2308.05234/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in VLMs for autonomous driving, one of 9 directions in End-to-end autonomous driving.
- Builds on 14 of its 53 references that are in the map.
- First posted on arXiv 9 Aug 2023.

## Placed in

- End-to-end autonomous driving / [VLMs for autonomous driving](https://mlsanity.com/driving/vlms-for-autonomous-driving/)

## Builds on (most cited first, 14 of 14)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [Understanding How Image Quality Affects Deep Neural Networks](https://mlsanity.com/paper/1604.04004/) (2016)
- [OMNet: Learning Overlapping Mask for Partial-to-Partial Point Cloud Registration](https://mlsanity.com/paper/2103.00937/) (2021)
- [A Tutorial on 5G NR V2X Communications](https://mlsanity.com/paper/2102.04538/) (2021)
- [Neural Compression and Filtering for Edge-assisted Real-time Object Detection in Challenged Networks](https://mlsanity.com/paper/2007.15818/) (2020)
- [Fast object detection in compressed JPEG Images](https://mlsanity.com/paper/1904.08408/) (2019)
- [Analyzing and Mitigating JPEG Compression Defects in Deep Learning](https://mlsanity.com/paper/2011.08932/) (2020)
- [Feature Sharing and Integration for Cooperative Cognition and Perception with Volumetric Sensors](https://mlsanity.com/paper/2011.08317/) (2020)
- [A Survey on Mobile Edge Computing for Video Streaming: Opportunities and Challenges](https://mlsanity.com/paper/2209.05761/) (2022)

## Built on by (most cited first, 3 of 3)

- [A Survey on Cloud-Edge-Terminal Collaborative Intelligence in AIoT Networks](https://mlsanity.com/paper/2508.18803/) (2025)
- [Unified End-to-End V2X Cooperative Autonomous Driving](https://mlsanity.com/paper/2405.03971/) (2024)
- [Bandwidth-adaptive Cloud-Assisted 360-Degree 3D Perception for Autonomous Vehicles](https://mlsanity.com/paper/2602.23871/) (2026)

## Abstract (arXiv)

Environmental perception is a key element of autonomous driving because the information received from the perception module influences core driving decisions. An outstanding challenge in real-time perception for autonomous driving lies in finding the best trade-off between detection quality and latency. Major constraints on both computation and power have to be taken into account for real-time perception in autonomous vehicles. Larger object detection models tend to produce the best results, but are also slower at runtime. Since the most accurate detectors cannot run in real-time locally, we investigate the possibility of offloading computation to edge and cloud platforms, which are less resource-constrained. We create a synthetic dataset to train object detection models and evaluate different offloading strategies. Using real hardware and network simulations, we compare different trade-offs between prediction quality and end-to-end delay. Since sending raw frames over the network implies additional transmission delays, we also explore the use of JPEG and H.265 compression at varying qualities and measure their impact on prediction metrics. We show that models with adequate compression can be run in real-time on the cloud while outperforming local detection performance.