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Achieving Achieving Complete Autonomy in Self-Driving vehicles. Synopsis CSE 411 - Seminar Submitted By
Sammit Arun Nadkarni B. Tech. Computer Science Engineering Semester VII Sign up to vote on this title
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%he last decade has seen massive developments in the &eld automotive technology. 'rom ()S to par!ing assist #e are no seriously considering the possibility of completely autonomou vehicles that can pretty much conduct themselves on the roa #ith minimal human intervention. %esla "otors has alre launched self-driving cars #here autonomous driving is the *S). Although tremendous research has gone into this disciplin the varying road conditions and driving rules globally are b in,uencers in terms of ho# this technology can be brought to i ,a#s. ver the last fe# years after the launch of autonomou cars many accidents have been reported #hich #ere alleged caused by malfunction or inadeuate technology. ur aim is understand this technology and to comment on ho# th technologies may be perfected or analy/e if they can perfected at all to the point #here they can deal #ith a dynam road environment #ith the help of intelligent algorithms.
ABSTRACT
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1. Introduction
Obstacle recognition is one of the most challenging fields in autonom driving along with the artificial intelligence which is used to made decisi based on the detected environment. The hardware and software to ach complete autonomous driving is already being developed today althoug has still to be optimized and perfected before it can be deployed to masses safely.
It has been shown that GPS,I! and "I#$% data can be used to genera high definition map which can be used for localization.. Over the past decades, attempts have been made to put out a complete autonomous ca the public roads only to be restricted to a subset of the full driving tas& s as highway'only, par&ing'only and throttle(brea& only )*+. #eveloping entire system top to bottom reuires the combination and coherenc appropriate sensors, computational hardware, networ&ing and sof You're Reading a Preview infrastructure. Unlock full access with a free trial.
Previous attempts have been successful to the point of semi'autonom Download Withintervention Free Trial driving only to be limited by human and hardware limitatio -hile fully autonomous driving has remained an important but elusive g many notable attempts and several important milestones have been reach Sign up to vote onhas this title The Stanford #riving teams autonomous vehicle been the useful Useful NotThe successful with its /ol&swagen Passat named 01unior2. most succes demonstration was closed to pedestrians and bicyclists, featured wide ro with eds und and ffic lights lth h
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2. Motivation
The advancement in hardware and software has allowed for po computing power to be utilized along with smart algorithms however a f fail proof system is yet to be reached. 8omplete reliance and trust of s driving cars can only be achieved when the system wor&s with a z percent failure rate, a simple e7ample of this can be seen in #ub #riverless etro which is completely automated. Therefor in achieving goal there will be significant effects in road transport such as decrease zero accident rate, reduced congestion and increased efficiency convenience.
The challenges which remain to be tac&led include narrow crosswal&s, and intersections as well as the primary safety concerns. 9in the hardest perception and reasoning tas&s still remain an unsolved are which a vehicle must navigate by demonstrating reasoning s&il une7pected scenarios at nearly the proficiency of a human driver.
3. !"ective and Sco#eYou're Reading a Preview Unlock full access with a free trial.
$n autonomous driving system is made up of certain modules which tightly coupled togetherDownload to achieve self'driving car. 8hanges need to Witha Free Trial made in each module in order for a solution to be implemented through the system. The areas to focus on include: Sign up to vote on this title •
Useful Not which notuseful only allows $ard%are& incorporating new hardware detection of ob;ects but also accurate identification of the sub;ect in o
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•
Ma##ing and locali)ation 'apping the environment and placing the accurately is an important part as it determines the locality of the GPS,I! and /elodyne "I#$% data is used to generate a high defini infrared map that can be used for localization.)*+
•
!"ect Recognition& In certain situations a self'driving vehicle reu specific details to made sophisticated precedency decisions. Ther more detailed and ob;ect identification is reuired which not recognizes the ob;ect by analyses it too.
•
Traffic light detection 'The importance of detection of traffic remains apparent however the method used can vary. Traditional meth include the use of a camera to detect the color of the signal, this met can also be e7tended to accessing the signal networ& information f more reliable detection.
•
Tra"ectory *lanning& #riving in realistic road traffic where cer factors such as traffic emergencies and dr You're flow, Readingcongestion, a Preview maneuvers are involved, intelligent tra;ectory planning is re Unlock full access with a free trial. $lgorithms provide for reactive obstacles avoidance by the combi usage of steering and brea&ing(acceleration )<+. Download With Free Trial
+. Summary Sign up to vote on this title
Not useful Self'#riving cars along with all the convenience that co Useful and advantages with it still reuire many more innovations to be made. 8hoosing the r hardware in order to build an ine7pensive robust system as we
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,. Reference( )*+
1. "evinson and S. Thrun, 0%obust vehicle localization in urban environm using probabilistic maps,2 2010 IEEE Int. Conf. Robot. Autom., pp. <3=6> <3=?, 6@*@. )6+ 1. "evinson, 1. $s&eland, 1. Aec&er, 1. #olson, #. 5eld, S. Bammel, 1. C. Bolter, #. "anger, O. Pin&, /. Pratt, . So&ols&y, G. Stane&, #. Stavens, $ Teichman, . -erling, and S. Thrun, 0Towards fully autonomous driving Systems and algorithms,2 IEEE Intell. Veh. Symp. Proc., no. Iv, pp. *D3>* 6@**. )3+ 1. "evinson and S. Thrun, 0!nsupervised calibration for multi'beam laser Springer Tract A!". Robot. , vol. =E, pp. *=E>*E3, 6@*<. )<+ $. Teichman and S. Thrun, 0Trac&ing'based semi'supervised learning,2 #. Rob. Re., vol. 3*, no. =, pp. ?@<>?*?, 6@*6.
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