# Probabilistic 3D Multi-Object Cooperative Tracking for Autonomous Driving via Differentiable Multi-Sensor Kalman Filter

arXiv: https://arxiv.org/abs/2309.14655 · HTML: https://mlsanity.com/paper/2309.14655/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 30 references that are in the map.
- First posted on arXiv 26 Sep 2023.

## Builds on (most cited first, 25 of 25)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://mlsanity.com/paper/1912.04838/) (2019)
- [PointPillars: Fast Encoders for Object Detection from Point Clouds](https://mlsanity.com/paper/1812.05784/) (2018)
- [Center-based 3D Object Detection and Tracking](https://mlsanity.com/paper/2006.11275/) (2020)
- [Planning-oriented Autonomous Driving](https://mlsanity.com/paper/2212.10156/) (2022)
- [Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Dataset](https://mlsanity.com/paper/2104.10133/) (2021)
- [V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer](https://mlsanity.com/paper/2203.10638/) (2022)
- [OPV2V: An Open Benchmark Dataset and Fusion Pipeline for Perception with Vehicle-to-Vehicle Communication](https://mlsanity.com/paper/2109.07644/) (2021)
- [V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Prediction](https://mlsanity.com/paper/2008.07519/) (2020)
- [CoBEVT: Cooperative Bird's Eye View Semantic Segmentation with Sparse Transformers](https://mlsanity.com/paper/2207.02202/) (2022)
- [Learning Distilled Collaboration Graph for Multi-Agent Perception](https://mlsanity.com/paper/2111.00643/) (2021)
- [F-Cooper: Feature based Cooperative Perception for Autonomous Vehicle Edge Computing System Using 3D Point Clouds](https://mlsanity.com/paper/1909.06459/) (2019)
- [When2com: Multi-Agent Perception via Communication Graph Grouping](https://mlsanity.com/paper/2006.00176/) (2020)
- [V2X-Sim: Multi-Agent Collaborative Perception Dataset and Benchmark for Autonomous Driving](https://mlsanity.com/paper/2202.08449/) (2022)
- [V2V4Real: A Real-world Large-scale Dataset for Vehicle-to-Vehicle Cooperative Perception](https://mlsanity.com/paper/2303.07601/) (2023)
- [Who2com: Collaborative Perception via Learnable Handshake Communication](https://mlsanity.com/paper/2003.09575/) (2020)
- [V2X-Seq: A Large-Scale Sequential Dataset for Vehicle-Infrastructure Cooperative Perception and Forecasting](https://mlsanity.com/paper/2305.05938/) (2023)
- [GNN3DMOT: Graph Neural Network for 3D Multi-Object Tracking with Multi-Feature Learning](https://mlsanity.com/paper/2006.07327/) (2020)
- [Probabilistic 3D Multi-Modal, Multi-Object Tracking for Autonomous Driving](https://mlsanity.com/paper/2012.13755/) (2020)
- [Probabilistic 3D Multi-Object Tracking for Autonomous Driving](https://mlsanity.com/paper/2001.05673/) (2020)
- [Backprop KF: Learning Discriminative Deterministic State Estimators](https://mlsanity.com/paper/1605.07148/) (2016)
- [Multimodal Sensor Fusion with Differentiable Filters](https://mlsanity.com/paper/2010.13021/) (2020)
- [How to Train Your Differentiable Filter](https://mlsanity.com/paper/2012.14313/) (2020)

## Built on by (most cited first, 15 of 15)

- [V2V-LLM: Vehicle-to-Vehicle Cooperative Autonomous Driving with Multimodal Large Language Models](https://mlsanity.com/paper/2502.09980/) (2025)
- [Vehicle-to-Everything Cooperative Perception for Autonomous Driving](https://mlsanity.com/paper/2310.03525/) (2023)
- [Griffin: Aerial-Ground Cooperative Detection and Tracking Dataset and Benchmark](https://mlsanity.com/paper/2503.06983/) (2025)
- [CoopTrack: Exploring End-to-End Learning for Efficient Cooperative Sequential Perception](https://mlsanity.com/paper/2507.19239/) (2025)
- [Systematic Literature Review on Vehicular Collaborative Perception -- A Computer Vision Perspective](https://mlsanity.com/paper/2504.04631/) (2025)
- [LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation](https://mlsanity.com/paper/2411.14927/) (2024)
- [Spb3DTracker: A Robust LiDAR-Based Person Tracker for Noisy Environment](https://mlsanity.com/paper/2408.05940/) (2024)
- [DINO-CoDT: Multi-class Collaborative Detection and Tracking with Vision Foundation Models](https://mlsanity.com/paper/2506.07375/) (2025)
- [Long-SCOPE: Fully Sparse Long-Range Cooperative 3D Perception](https://mlsanity.com/paper/2604.09206/) (2026)
- [AFFormer: Adaptive Feature Fusion Transformer for V2X Cooperative Perception under Channel Impairments](https://mlsanity.com/paper/2605.01888/) (2026)
- [Optimizing Cooperative Multi-Object Tracking using Graph Signal Processing](https://mlsanity.com/paper/2506.09469/) (2025)
- [Robustifying 3D Perception via Least-Squares Graphs for Multi-Agent Object Tracking](https://mlsanity.com/paper/2507.04762/) (2025)
- [Data-Driven Discovery of Interpretable Kalman Filter Variants through Large Language Models and Genetic Programming](https://mlsanity.com/paper/2508.11703/) (2025)
- [SRA-CP: Spontaneous Risk-Aware Selective Cooperative Perception](https://mlsanity.com/paper/2511.17461/) (2025)
- [VIPS: Vehicle-Infrastructure Cooperative Planning Benchmark via Pseudo-Simulation](https://mlsanity.com/paper/2609.02462/) (2026)

## Abstract (arXiv)

Current state-of-the-art autonomous driving vehicles mainly rely on each individual sensor system to perform perception tasks. Such a framework's reliability could be limited by occlusion or sensor failure. To address this issue, more recent research proposes using vehicle-to-vehicle (V2V) communication to share perception information with others. However, most relevant works focus only on cooperative detection and leave cooperative tracking an underexplored research field. A few recent datasets, such as V2V4Real, provide 3D multi-object cooperative tracking benchmarks. However, their proposed methods mainly use cooperative detection results as input to a standard single-sensor Kalman Filter-based tracking algorithm. In their approach, the measurement uncertainty of different sensors from different connected autonomous vehicles (CAVs) may not be properly estimated to utilize the theoretical optimality property of Kalman Filter-based tracking algorithms. In this paper, we propose a novel 3D multi-object cooperative tracking algorithm for autonomous driving via a differentiable multi-sensor Kalman Filter. Our algorithm learns to estimate measurement uncertainty for each detection that can better utilize the theoretical property of Kalman Filter-based tracking methods. The experiment results show that our algorithm improves the tracking accuracy by 17% with only 0.037x communication costs compared with the state-of-the-art method in V2V4Real. Our code and videos are available at https://github.com/eddyhkchiu/DMSTrack/ and https://eddyhkchiu.github.io/dmstrack.github.io/ .