Corpus ID: 210701306

Probabilistic 3D Multi-Object Tracking for Autonomous Driving

@article{Chiu2020Probabilistic3M,
  title={Probabilistic 3D Multi-Object Tracking for Autonomous Driving},
  author={Hsu-kuang Chiu and Antonio Prioletti and Jie Li and Jeannette Bohg},
  journal={ArXiv},
  year={2020},
  volume={abs/2001.05673}
}
  • Hsu-kuang Chiu, Antonio Prioletti, +1 author Jeannette Bohg
  • Published in ArXiv 2020
  • Computer Science
  • 3D multi-object tracking is a key module in autonomous driving applications that provides a reliable dynamic representation of the world to the planning module. In this paper, we present our on-line tracking method, which made the first place in the NuScenes Tracking Challenge, held at the AI Driving Olympics Workshop at NeurIPS 2019. Our method estimates the object states by adopting a Kalman Filter. We initialize the state covariance as well as the process and observation noise covariance… CONTINUE READING

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