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.

© 2023 Optica Publishing Group

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