# EI-Drive: A Platform for Cooperative Perception with Realistic Communication Models

arXiv: https://arxiv.org/abs/2412.09782 · HTML: https://mlsanity.com/paper/2412.09782/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 34 references that are in the map.
- First posted on arXiv 13 Dec 2024.

## Builds on (most cited first, 13 of 13)

- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement Learning](https://mlsanity.com/paper/2109.12674/) (2021)
- [CoBEVT: Cooperative Bird's Eye View Semantic Segmentation with Sparse Transformers](https://mlsanity.com/paper/2207.02202/) (2022)
- [Flow: A Modular Learning Framework for Mixed Autonomy Traffic](https://mlsanity.com/paper/1710.05465/) (2017)
- [Cooperative Perception with Deep Reinforcement Learning for Connected Vehicles](https://mlsanity.com/paper/2004.10927/) (2020)
- [CarDreamer: Open-Source Learning Platform for World Model based Autonomous Driving](https://mlsanity.com/paper/2405.09111/) (2024)
- [MASS: Mobility-Aware Sensor Scheduling of Cooperative Perception for Connected Automated Driving](https://mlsanity.com/paper/2302.13029/) (2023)
- [AICP: Augmented Informative Cooperative Perception](https://mlsanity.com/paper/2101.05508/) (2021)
- [Online V2X Scheduling for Raw-Level Cooperative Perception](https://mlsanity.com/paper/2202.06085/) (2022)
- [AVstack: An Open-Source, Reconfigurable Platform for Autonomous Vehicle Development](https://mlsanity.com/paper/2212.13857/) (2022)
- [Towards Low-Latency and Ultra-Reliable Vehicle-to-Vehicle Communication](https://mlsanity.com/paper/1704.06894/) (2017)
- [Collaborative 3D Object Detection for Automatic Vehicle Systems via Learnable Communications](https://mlsanity.com/paper/2205.11849/) (2022)

## Built on by (most cited first, 1 of 1)

- [Towards Efficient 3D Object Detection for Vehicle-Infrastructure Collaboration via Risk-Intent Selection](https://mlsanity.com/paper/2601.03001/) (2026)

## Abstract (arXiv)

The growing interest in autonomous driving calls for realistic simulation platforms capable of accurately simulating cooperative perception process in realistic traffic scenarios. Existing studies for cooperative perception often have not accounted for transmission latency and errors in real-world environments. To address this gap, we introduce EI-Drive, an edge-AI based autonomous driving simulation platform that integrates advanced cooperative perception with more realistic communication models. Built on the CARLA framework, EI-Drive features new modules for cooperative perception while taking into account transmission latency and errors, providing a more realistic platform for evaluating cooperative perception algorithms. In particular, the platform enables vehicles to fuse data from multiple sources, improving situational awareness and safety in complex environments. With its modular design, EI-Drive allows for detailed exploration of sensing, perception, planning, and control in various cooperative driving scenarios. Experiments using EI-Drive demonstrate significant improvements in vehicle safety and performance, particularly in scenarios with complex traffic flow and network conditions. All code and documents are accessible on our GitHub page: urlhttps://ucd-dare.github.io/eidrive.github.io/.