| --- |
| license: mit |
| tags: |
| - robotics |
| - autonomous-vehicles |
| - object-tracking |
| - kalman-filter |
| - fmcw-lidar |
| - doppler-lidar |
| - pytorch |
| datasets: |
| - AevaScenes |
| metrics: |
| - prediction-error |
| pipeline_tag: object-detection |
| --- |
| |
| # D-KalmanNet: Neural Kalman Filtering for Doppler LiDAR Tracking |
|
|
| This repository contains the pre-trained weights for **D-KalmanNet**, the tracking component of the DPNet framework. D-KalmanNet integrates a structured Gaussian State Space (GSS) model with a recurrent neural network to accurately predict and track the future states of dynamic obstacles using measurements from (FMCW) **Doppler LiDAR**. |
|
|
| The full framework can be found in the official GitHub repository. |
|
|
| ## Model Details |
|
|
| - **Developed by:** [UUwei-zuo](https://github.com/UUwei-zuo) |
| - **Dataset Trained On:** [AevaScenes](https://github.com/aevainc/aevascenes) |
| - **Framework:** PyTorch |
| - **Associated Code:** [GitHub: UUwei-zuo/DPNet](https://github.com/UUwei-zuo/DPNet) |
| - **Paper:** [RA-L '26][DPNet: Doppler LiDAR Motion Planning for Highly-Dynamic Environments](https://arxiv.org/abs/2512.00375) |
|
|
| ## How to Use |
|
|
| Intructions for loading the pretraining `model.pt` or training your custom model can be found in [GitHub: UUwei-zuo/DPNet](https://github.com/UUwei-zuo/DPNet). |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{zuo2026dpnet, |
| author={Zuo, Wei and Ren, Zeyi and Li, Chengyang and Wang, Yikun and Zhao, Mingle and Wang, Shuai and Sui, Wei and Gao, Fei and Wu, Yik-Chung and Xu, Chengzhong}, |
| journal={IEEE Robotics and Automation Letters}, |
| title={DPNet: Doppler LiDAR Motion Planning for Highly-Dynamic Environments}, |
| year={2026}, |
| volume={11}, |
| number={6}, |
| pages={7190-7197}, |
| doi={10.1109/LRA.2026.3685933} |
| } |
| ``` |
