Self driving car using tensorflow

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dc.contributor.author Khan, Zarrar Ahmed
dc.contributor.author Thakur, Waqqas (16DET72)
dc.contributor.author Ansari, Arman (15ET14)
dc.contributor.author Patel, Md. Aabid (16DET109)]
dc.contributor.author Shaikhnag, Usman (16DET70)
dc.date.accessioned 2019-05-28T06:03:05Z
dc.date.available 2019-05-28T06:03:05Z
dc.date.issued 2019-05
dc.identifier.uri http://www.aiktcdspace.org:8080/jspui/handle/123456789/3032
dc.description Submitted in partial fulfillment of the requirements for the degree of Bachelor of Engineering 2019 en_US
dc.description.abstract Deep Learning has led us to newer possibilities in solving complex control and navigation related tasks. The paper presents Deep Learning with back propagation autonomous navigation and obstacle avoidance of self-driving cars, applied with Deep Q Network to a simulated car an urban environment. The approach uses two types of sensor data as input: camera sensor and laser sensor in front of the car. It also designs a cost-efcient high-speed car prototype capable of running the same algorithm in real-time. The design uses a camera and a Hokuyo Lidar sensor in the car front. It uses embedded GPU (Nvidia-TX2) or CPU for running deep-learning algorithms based on sensor inputs. en_US
dc.language.iso en en_US
dc.publisher AIKTC en_US
dc.relation.ispartofseries PE0483;
dc.subject Project Report - EXTC en_US
dc.title Self driving car using tensorflow en_US
dc.type Project Report en_US


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