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Processing of LiDAR for Traffic Scene Perception of Autonomous Vehicles

Publication Type : Conference Paper

Publisher : 2020 International Conference on Communication and Signal Processing (ICCSP)

Source : 2020 International Conference on Communication and Signal Processing (ICCSP), 2020

Url : https://ieeexplore.ieee.org/document/9182175

Keywords : Autonomous vehicles, LiDAR, CNN, Segmentation algorithms

Campus : Amritapuri

Center : Humanitarian Technology (HuT) Labs

Year : 2020

Abstract : The future of automated driving system deals with the emergence of autonomous vehicles popularly referred as self-driving cars or driver-less cars. The ability of sensing its environment with or without human interaction is the redeeming feature of autonomous vehicles. The traditional segmentation algorithms mainly uses camera data,whose accuracy is degraded by the effect of shadows, bright sunlight or headlight of other cars. This paper focuses on Lidar sensors which provides 3-D geometry information of the vehicle surroundings with high accuracy. Apart from the previous works which deals only with segmentation using data from Lidar sensor, it deals with predicting the probabilities of detecting things such as human, vehicles, traffic signals, stationary objects etc. Moreover with the use of Ford campus vision and KITTI vision detection bench marks it achieves a high accuracy of about 90 percentage.

Cite this Research Publication : O Urmila.;Rajesh Kannan Megalingam, "Processing of LiDAR for Traffic Scene Perception of Autonomous Vehicles", 2020 International Conference on Communication and Signal Processing (ICCSP), 2020

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