End to end learning for autonomous driving on unpaved roads : a study towards automated wildlife patrol

dc.contributor.author	Brahmbhatt, Khushal
dc.contributor.author	Ojino, Ronald
dc.date.accessioned	2021-05-18T17:42:01Z
dc.date.available	2021-05-18T17:42:01Z
dc.date.issued	2020
dc.description.abstract	The aim is to investigate the technological feasibility of deploying automated Unmanned Ground Vehicles (UGV) for automated wildlife patrol. The presentation outlines a feasibility study and cost benefit analysis for a project that would cover peak seasons when there is a shortage of park rangers. Data is drawn from field tests in Nairobi National Park; Ruma National Park; and on paved road/highways in Kenya).	en
dc.format.mimetype	application/pdf
dc.identifier.uri	http://hdl.handle.net/10625/60132
dc.language.iso	en
dc.subject	AI4D	en
dc.subject	ARTIFICIAL INTELLIGENCE	en
dc.subject	MACHINE LEARNING	en
dc.subject	UNMANNED GROUND VEHICLES	en
dc.subject	DATA COLLECTION	en
dc.subject	WILDLIFE CONSERVATION	en
dc.subject	NATIONAL PARKS	en
dc.subject	NAIROBI	en
dc.subject	KENYA	en
dc.subject	SOUTH OF SAHARA	en
dc.title	End to end learning for autonomous driving on unpaved roads : a study towards automated wildlife patrol	en
dc.type	Presentation	en
idrc.copyright.holder	© 2021, KHUSHAL BRAHMBHATT
idrc.copyright.oapermissionsource	CC BY 4.0	en
idrc.dspace.access	Open Access	en
idrc.project.componentnumber	108914001
idrc.project.number	108914
idrc.project.title	Building a network of excellence in artificial intelligence in Sub-Saharan Africa	en
idrc.rims.adhocgroup	IDRC SUPPORTED	en

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