Abstract
Automotive light detection and ranging (LiDAR) requires accurate and computationally efficient range estimation methods. At present, such efficiency is achieved at the cost of curtailing the dynamic range of a LiDAR receiver. In this Letter, we propose using decision tree ensemble machine learning models to overcome such a trade-off. Simple and yet powerful models are developed and proven capable of performing accurate measurements across a 45-dB dynamic range.
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Data availability
The full-waveform dataset used in this paper is openly available in [13].
13. D. Bastos, A. Brand ao, A. Lorences-Riesgo, P. Monteiro, A. Oliveira, D. Pereira, H. Olyaei, and M. Drummond, “Full-waveform pulsed LiDAR dataset,” Zenodo, 2022, https://doi.org/10.5281/zenodo.7075871.
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