Abstract
Monocular 3d object detection methods are promising in the field of making autonomous robots without lidar, which can reduce costs of production significantly. However monocular 3d object detection methods tend to have low precision due to inaccurate inference of distances to objects. Nevertheless, there are several ways to measure the impact of detection precision on the downstream autonomous driving task. In this work, autonomous agents which use lidar, monocular camera, and ground truth for 3d object detection are compared in the CARLA simulator. Each agent has passed a set of routes with challenging traffic situations, totaling 122.5 km driven. Quality of movement was assessed using the collisions statistics, as a result, the agent using a monocular camera performed 4.5% better than the agent using lidar. This indicates the applicability of monocular 3d object detection algorithms in certain cases.
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Acknowledgements
This work was done as the part of the state task of the Ministry of Education and Science of Russia No. 075-00913-21-01 “Development and study of new architectures of reconfigurable growing neural networks, methods and algorithms for their learning”.
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Filatov, N., Isakov, T., Bakhshiev, A. (2022). Research on the Applicability of Monocular 3d Object Detection Using CARLA Simulator. In: Kryzhanovsky, B., Dunin-Barkowski, W., Redko, V., Tiumentsev, Y., Klimov, V.V. (eds) Advances in Neural Computation, Machine Learning, and Cognitive Research V. NEUROINFORMATICS 2021. Studies in Computational Intelligence, vol 1008. Springer, Cham. https://doi.org/10.1007/978-3-030-91581-0_30
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DOI: https://doi.org/10.1007/978-3-030-91581-0_30
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