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Using Deep Learning to Predict Obsta...

ProQuest Information and Learning Co.

Using Deep Learning to Predict Obstacle Trajectories for Collision Avoidance in Autonomous Vehicles.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Using Deep Learning to Predict Obstacle Trajectories for Collision Avoidance in Autonomous Vehicles./
Author:	Virdi, Jaskaran.
Description:	1 online resource (43 pages)
Notes:	Source: Masters Abstracts International, Volume: 57-04.
Contained By:	Masters Abstracts International57-04(E).
Subject:	Computer science. -
Online resource:	click for full text (PQDT)
ISBN:	9780355665222

Using Deep Learning to Predict Obstacle Trajectories for Collision Avoidance in Autonomous Vehicles.
Virdi, Jaskaran.

Using Deep Learning to Predict Obstacle Trajectories for Collision Avoidance in Autonomous Vehicles. - 1 online resource (43 pages)

Source: Masters Abstracts International, Volume: 57-04.

Thesis (M.S.)--University of California, San Diego, 2018.

Includes bibliographical references

As a part of developing autonomous vehicles and better Advanced driver assistance systems(ADAS), it is important to consider how the spatio-temporal activities of other agents in the environment like pedestrians, vehicles, etc. which are competing for space on roads might impact the motion planning performance of the vehicle. A system which can predict future obstacle trajectories as well as warn the driver or the autonomous vehicle about an impending collision will lead to safer roads and save lives.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355665222Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

Using Deep Learning to Predict Obstacle Trajectories for Collision Avoidance in Autonomous Vehicles.
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100 1 $a Virdi, Jaskaran. $3 1194236
245 1 0 $a Using Deep Learning to Predict Obstacle Trajectories for Collision Avoidance in Autonomous Vehicles.
264 0 $c 2018
300 $a 1 online resource (43 pages)
336 $a text $b txt $2 rdacontent
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500 $a Source: Masters Abstracts International, Volume: 57-04.
500 $a Adviser: Henrik Christensen.
502 $a Thesis (M.S.)--University of California, San Diego, 2018.
504 $a Includes bibliographical references
520 $a As a part of developing autonomous vehicles and better Advanced driver assistance systems(ADAS), it is important to consider how the spatio-temporal activities of other agents in the environment like pedestrians, vehicles, etc. which are competing for space on roads might impact the motion planning performance of the vehicle. A system which can predict future obstacle trajectories as well as warn the driver or the autonomous vehicle about an impending collision will lead to safer roads and save lives.
520 $a Previous vehicle trajectory prediction approaches use motion models which have assumptions like constant velocity or constant acceleration which doesn't generalize well. Our approach is completely data driven and gives promising results for predicting trajectory of the obstacle up to 2 seconds in the future using a deep recurrent neural network.
520 $a Taking inspiration from the recent success of sequence-to-sequence models in language translation we apply sequence-to-sequence recurrent neural networks to the new problem of trajectory prediction. The proposed scheme feeds the sequence of obstacles' past trajectory data obtained from sensors like LIDAR and GPS to the LSTM and predicts the position of the obstacle at future time steps. We use the KITTI dataset which provides us with annotated trajectory data for learning and evaluation.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
655 7 $a Electronic books. $2 local $3 554714
690 $a 0984
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of California, San Diego. $b Computer Science. $3 1182161
773 0 $t Masters Abstracts International $g 57-04(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10689398 $z click for full text (PQDT)

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