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Simulation of Path Planning Algorithms Using Commercially Available Road Datasets with Multi-modal Sensory Data

Abstract : Road datasets for computer vision tasks involved in advanced driver assist systems and autonomous driving are publicly available for the technical community for the development of machine learning aided scene understanding using computer vision systems. All the perceived data from multiple sensors mounted on the vehicle must be fused to generate an accurate state of the vehicle and its surroundings. The paper presents details of the simulation implementation of local path planning for an autonomous vehicle based on multi-sensory information. The simulation is carried out with sensory inputs from RGB camera, LIDAR and GPS. The data is obtained from the KITTI dataset. A variant of the D-star algorithm is utilized to demonstrate global and local path-planning capabilities in the simulation environment.
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https://hal.inria.fr/hal-03434780
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Submitted on : Thursday, November 18, 2021 - 2:20:21 PM
Last modification on : Thursday, November 18, 2021 - 2:32:13 PM
Long-term archiving on: : Saturday, February 19, 2022 - 7:10:59 PM

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R. Senthilnathan, Arjun Venugopal, K. S. Vishnu. Simulation of Path Planning Algorithms Using Commercially Available Road Datasets with Multi-modal Sensory Data. 3rd International Conference on Computational Intelligence in Data Science (ICCIDS), Feb 2020, Chennai, India. pp.261-275, ⟨10.1007/978-3-030-63467-4_21⟩. ⟨hal-03434780⟩

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