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SebastianHuch / DeepSTEP

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DeepSTEP - Deep-Learning Based Spatio-Temporal End-to-End Perception for Autonomous Vehicles

DeepSTEP

This repository provides a neural network for object detection and local mapping using multi-modal sensor data from camera, LiDAR, and RaDAR. This is the official repository to our paper presented at the IEEE IV 2023.

Citation

If you find our work useful in your research, please consider citing:

@INPROCEEDINGS{DeepSTEP2023,
  author={Huch, Sebastian and Sauerbeck, Florian and Betz, Johannes},
  booktitle={2023 IEEE Intelligent Vehicles Symposium (IV)}, 
  title={DeepSTEP - Deep Learning-Based Spatio-Temporal End-To-End Perception for Autonomous Vehicles}, 
  year={2023},
  volume={},
  number={},
  pages={1-8},
  doi={10.1109/IV55152.2023.10186768}}

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