Visual Scene Understanding for Self-Driving Cars Using Deep Learning and Stereovision

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dc.contributor.authorGrenier, Amélie
dc.date.accessioned2024-05-05T11:13:04Z
dc.date.available2024-05-05T11:13:04Z
dc.date.issued2019-02-07 16:16
dc.description.abstractPoster presented at the 2018 Defence and Security Doctoral Symposium.Autonomous driving has been rapidly evolving for the last few years and there is a lot of fervour in increasing the intelligence of these vehicles. One key aspect of a self-driving car is its ability to sense the environment in order to be aware of its surrounding.Our interest lies in using computer vision and deep learning techniques to detect surrounding entities; localising and recognising them. Here, we present a novel deconvolutional neural network for semantic segmentation, combined with disparity map information to localise each vehicle in front of the ego-vehicle, including occluded instances, in an urban traffic environment. We also compare our approach with state-of-the-art instance segmentation methods. In the future, we will extend our work to other types of obstacles, to improve awareness and increase obstacle avoidance and path finding capabilities of a vehicle.
dc.identifier.citationGrenier, Amélie (2019). Visual Scene Understanding for Self-Driving Cars Using Deep Learning and Stereovision. Cranfield Online Research Data (CORD). Poster. https://doi.org/10.17862/cranfield.rd.7370174.v1
dc.identifier.doi10.17862/cranfield.rd.7370174.v1
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/21474
dc.publisherCranfield University
dc.rightsCC BY 4.0
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subject'Semantic segmentation'
dc.subject'Deep learning'
dc.subject'Scene understanding'
dc.subject'DSDS18 poster'
dc.subject'DSDS18'
dc.subject'Autonomous Vehicles'
dc.subject'Computer Vision'
dc.subject'Knowledge Representation and Machine Learning'
dc.titleVisual Scene Understanding for Self-Driving Cars Using Deep Learning and Stereovision
dc.typePoster

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