An end-to-end fully automatic bay parking approach for autonomous vehicles

Rui Li, Weitian Wang, Yi Chen, Srivatsan Srinivasan, Venkat N. Krovi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

Fully automatic parking (FAP) is a key step towards the age of autonomous vehicle. Motivated by the contribution of human vision to human parking, in this paper, we propose a computer vision based FAP method for the autonomous vehicles. Based on the input images from a rear camera on the vehicle, a convolutional neural network (CNN) is trained to automatically output the steering and velocity commands for the vehicle controlling. The CNN is trained by Caffe deep learning framework. A 1/10th autonomous vehicle research platform (1/10-SAVRP), which configured with a vehicle controller unit, an automated driving processor, and a rear camera, is used for demonstrating the parking maneuver. The experimental results suggested that the proposed approach enabled the vehicle to gain the ability of parking independently without human input in different driving settings.

Original languageEnglish
Title of host publicationControl and Optimization of Connected and Automated Ground Vehicles; Dynamic Systems and Control Education; Dynamics and Control of Renewable Energy Systems; Energy Harvesting; Energy Systems; Estimation and Identification; Intelligent Transportation and Vehicles; Manufacturing; Mechatronics; Modeling and Control of IC Engines and Aftertreatment Systems; Modeling and Control of IC Engines and Powertrain Systems; Modeling and Management of Power Systems
PublisherAmerican Society of Mechanical Engineers (ASME)
ISBN (Electronic)9780791851906
DOIs
StatePublished - 2018
EventASME 2018 Dynamic Systems and Control Conference, DSCC 2018 - Atlanta, United States
Duration: 30 Sep 20183 Oct 2018

Publication series

NameASME 2018 Dynamic Systems and Control Conference, DSCC 2018
Volume2

Conference

ConferenceASME 2018 Dynamic Systems and Control Conference, DSCC 2018
Country/TerritoryUnited States
CityAtlanta
Period30/09/183/10/18

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