# AutoCast: Scalable Infrastructure-less Cooperative Perception for Distributed Collaborative Driving

arXiv: https://arxiv.org/abs/2112.14947 · HTML: https://mlsanity.com/paper/2112.14947/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Dec 2021.

## Built on by (most cited first, 26 of 26)

- [V2V4Real: A Real-world Large-scale Dataset for Vehicle-to-Vehicle Cooperative Perception](https://mlsanity.com/paper/2303.07601/) (2023)
- [V2X-Seq: A Large-Scale Sequential Dataset for Vehicle-Infrastructure Cooperative Perception and Forecasting](https://mlsanity.com/paper/2305.05938/) (2023)
- [COOPERNAUT: End-to-End Driving with Cooperative Perception for Networked Vehicles](https://mlsanity.com/paper/2205.02222/) (2022)
- [Learning for Vehicle-to-Vehicle Cooperative Perception under Lossy Communication](https://mlsanity.com/paper/2212.08273/) (2022)
- [Towards Vehicle-to-everything Autonomous Driving: A Survey on Collaborative Perception](https://mlsanity.com/paper/2308.16714/) (2023)
- [CMP: Cooperative Motion Prediction with Multi-Agent Communication](https://mlsanity.com/paper/2403.17916/) (2024)
- [A Survey and Framework of Cooperative Perception: From Heterogeneous Singleton to Hierarchical Cooperation](https://mlsanity.com/paper/2208.10590/) (2022)
- [TraF-Align: Trajectory-aware Feature Alignment for Asynchronous Multi-agent Perception](https://mlsanity.com/paper/2503.19391/) (2025)
- [DSRC: Learning Density-insensitive and Semantic-aware Collaborative Representation against Corruptions](https://mlsanity.com/paper/2412.10739/) (2024)
- [V2X-DGW: Domain Generalization for Multi-agent Perception under Adverse Weather Conditions](https://mlsanity.com/paper/2403.11371/) (2024)
- [On Data Fabrication in Collaborative Vehicular Perception: Attacks and Countermeasures](https://mlsanity.com/paper/2309.12955/) (2023)
- [MASS: Mobility-Aware Sensor Scheduling of Cooperative Perception for Connected Automated Driving](https://mlsanity.com/paper/2302.13029/) (2023)
- [A novel efficient Multi-view traffic-related object detection framework](https://mlsanity.com/paper/2302.11810/) (2023)
- [CAML: Collaborative Auxiliary Modality Learning for Multi-Agent Systems](https://mlsanity.com/paper/2502.17821/) (2025)
- [Task-Oriented Wireless Communications for Collaborative Perception in Intelligent Unmanned Systems](https://mlsanity.com/paper/2406.03086/) (2024)
- [NLOS Dies Twice: Challenges and Solutions of V2X for Cooperative Perception](https://mlsanity.com/paper/2307.06615/) (2023)
- [InfoCom: Kilobyte-Scale Communication-Efficient Collaborative Perception with Information Bottleneck](https://mlsanity.com/paper/2512.10305/) (2025)
- [Communication-Critical Planning via Multi-Agent Trajectory Exchange](https://mlsanity.com/paper/2303.06080/) (2023)
- [MMCD: Multi-Modal Collaborative Decision-Making for Connected Autonomy with Knowledge Distillation](https://mlsanity.com/paper/2509.18198/) (2025)
- [JigsawComm: Joint Semantic Feature Encoding and Transmission for Communication-Efficient Cooperative Perception](https://mlsanity.com/paper/2511.17843/) (2025)
- [COOPERTRIM: Adaptive Data Selection for Uncertainty-Aware Cooperative Perception](https://mlsanity.com/paper/2602.13287/) (2026)
- [Enhanced Cooperative Perception for Autonomous Vehicles Using Imperfect Communication](https://mlsanity.com/paper/2404.08013/) (2024)
- [Can We Remove the Ground? Obstacle-aware Point Cloud Compression for Remote Object Detection](https://mlsanity.com/paper/2410.00582/) (2024)
- [Wireless Communication as an Information Sensor for Multi-agent Cooperative Perception: A Survey](https://mlsanity.com/paper/2505.00747/) (2025)
- [Active Asymmetric Multi-Agent Multimodal Learning under Uncertainty](https://mlsanity.com/paper/2602.04763/) (2026)
- [CooperScene: Multi-Modal Cooperative Autonomy Benchmark with C-V2X Communication Characterization](https://mlsanity.com/paper/2606.31219/) (2026)

## Abstract (arXiv)

Autonomous vehicles use 3D sensors for perception. Cooperative perception enables vehicles to share sensor readings with each other to improve safety. Prior work in cooperative perception scales poorly even with infrastructure support. AutoCast enables scalable infrastructure-less cooperative perception using direct vehicle-to-vehicle communication. It carefully determines which objects to share based on positional relationships between traffic participants, and the time evolution of their trajectories. It coordinates vehicles and optimally schedules transmissions in a distributed fashion. Extensive evaluation results under different scenarios show that, unlike competing approaches, AutoCast can avoid crashes and near-misses which occur frequently without cooperative perception, its performance scales gracefully in dense traffic scenarios providing 2-4x visibility into safety critical objects compared to existing cooperative perception schemes, its transmission schedules can be completed on the real radio testbed, and its scheduling algorithm is near-optimal with negligible computation overhead.