Autonomous Driving refers to self-driving vehicles without the need of intervention from the human driver. Safety enhancement, energy optimization, comfort, maintenance and cost are the key benefits of Autonomous Driving. Other benefits include - productivity, reduced congestion/traffic, prevention of car crashes, reducing carbon footprint and ease of parking in congested cities as driverless vehicles could drop passengers off and move on. Autonomous Driving can be achieved via processing visual images/videos at runtime and then converting them to vehicle control signals. This study will help in detecting objects on roads (such as moving vehicles, pedestrians, other static objects on road and road segmentation) using deep convolutional neural network (CNN), which in turn help in aiding driverless future. Semantic segmentation can help recognize objects and their location. Semantic segmentation refers to labelling images with pixel-by-pixel classification that in turn helps to perceive the surrounding environment. Human driver also perceives the driving environment in a similar manner. The purpose of this study is to do an overview and check the feasibility of semantic segmentation using deep learning algorithms in the field of Autonomous Driving specifically in road segmentation task. This study will also be comparative study on CNNs models in terms of accuracy, precision, mean IOU and processing time. The scope of this study will include examination and comparison of two popular algorithms - Fully Convolutional Network (FCNs) and Semantic Segmentation model (SegNet). In detail, this study will mainly focus on road segmentation task in Autonomous Driving with the help of CNN models and will conclude which model is best suited for road segmentation task under different weather conditions, in terms of precision, accuracy, mean IoU and processing time.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Autonomous Driving Through Road Segmentation Based on Computer Vision Techniques


    Additional title:

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



    Conference:

    The International Conference on Data Science and Emerging Technologies ; 2022 December 20, 2022 - December 21, 2022



    Publication date :

    2023-04-01


    Size :

    12 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    Computer Vision for Autonomous Driving

    Kanchana, Bimsara / Peiris, Rojith / Perera, Damitha et al. | IEEE | 2021


    Vision-based Autonomous Road Vehicles

    Graefe, Volker / Kuhnert, Klaus-Dieter | Springer Verlag | 1992


    Vision based autonomous robot navigation:motion segmentation

    Blackburn,M.R. / Nguyen,H.G. / Naval Command, Control and Ocean Surveillance Center,US | Automotive engineering | 1995


    Dynamic computer vision for autonomous road vehicle guidance

    Dickmanns,E.D. / Univ.der Bundeswehr Muenchen,DE | Automotive engineering | 1989


    ROAD SURFACE FRICTION BASED PREDICTIVE DRIVING FOR COMPUTER ASSISTED OR AUTONOMOUS DRIVING VEHICLES

    NISHI YOSHIFUMI / PIDWERBECKI DAVID / BROWNING DAVID et al. | European Patent Office | 2020

    Free access