For many years, engineers are searching solutions for car accidents that are caused by human error due to drowsiness or by a person who comes out of nowhere in front of the vehicle. In such a situation, vehicle accident may occur. So, there is a need to develop a vehicle that saves human life from a dangerous accident. Autonomous vehicle have emerged with cameras and sensors to avoid the strategic accidents of human error. Autonomous vehicle systems get information about other vehicles, pedestrians, and other immediate surroundings that is necessary to detect the presence of pedestrians, vehicles and other related entity objects. In this paper, novel LIDAR technologies for automotive applications and the perception algorithms based on field of view (FOV) are used for obstacle avoidance, and ranging of object or vehicle nearby to avoid collision of data in crucial conditions is discussed. This paper is structured based on a vehicle detection and processing of 2D LIDAR sensor to avoid traffic accidents and save life of the people with the help of deep learning algorithm concept. The LIDAR serves as image sensor, and the output response is a steering response, based on the image decisions taken either stop or turn the vehicle depending on the object in front of the vehicle based on road boundary detection.


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    Title :

    Obstacle Avoidance and Ranging Using LIDAR


    Additional title:

    Lect. Notes on Data Eng. and Comms.Technol.


    Contributors:


    Publication date :

    2021-07-23


    Size :

    10 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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