Connexion
S'inscrire
Réinitialiser le mot de passe
Publier & Distribuer
Solutions d'édition
Solutions de distribution
Thèmes
Architecture et design
Arts
Business et économie
Chimie
Chimie industrielle
Droit
Géosciences
Histoire
Informatique
Ingénierie
Intérêt général
Linguistique et sémiotique
Littérature
Mathématiques
Musique
Médecine
Pharmacie
Philosophie
Physique
Sciences bibliothécaires et de l'information, études du livre
Sciences des matériaux
Sciences du vivant
Sciences sociales
Sport et loisirs
Théologie et religion
Études classiques et du Proche-Orient ancient
Études culturelles
Études juives
Publications
Journaux
Livres
Comptes-rendus
Éditeurs
Blog
Contact
Chercher
EUR
USD
GBP
Français
English
Deutsch
Polski
Español
Français
Italiano
Panier
Home
Journaux
International Journal on Smart Sensing and Intelligent Systems
Édition 13 (2020): Edition 1 (January 2020)
Accès libre
1/10th scale autonomous vehicle based on convolutional neural network
Avishkar Seth
Avishkar Seth
,
Alice James
Alice James
et
Subhas C. Mukhopadhyay
Subhas C. Mukhopadhyay
| 25 août 2020
International Journal on Smart Sensing and Intelligent Systems
Édition 13 (2020): Edition 1 (January 2020)
À propos de cet article
Article précédent
Article suivant
Résumé
Article
Figures et tableaux
Références
Auteurs
Articles dans cette édition
Aperçu
PDF
Citez
Partagez
Article Category:
Research-Article
Publié en ligne:
25 août 2020
Pages:
1 - 17
Reçu:
26 juil. 2020
DOI:
https://doi.org/10.21307/ijssis-2020-021
Mots clés
Autonomous vehicle
,
Convolutional neural network
,
Raspberry pi 4
,
Ultrasonic sensor
,
Camera
© 2020 Avishkar Seth published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Figure 1:
Picture of the individual vehicle hardware components used in the system.
Figure 2:
Complete system circuit diagram.
Figure 3:
CAD design for the laser cut base plate implemented on Autodesk Fusion 360 (left) and the base plate placed on the vehicle chassis (right).
Figure 4:
CAD design for the tower type case implemented on Autodesk Fusion 360 (left) and the physical PLA material 3D printed case (right).
Figure 5:
The overview of the software message queue of the system (vehicle state).
Figure 6:
Windows host PC installation steps.
Figure 7:
Software configuration steps for the Raspberry Pi 4 setup.
Figure 8:
Screenshot of the camera code configuration and sample 160 × 120 pixels image from the vehicle on board camera.
Figure 9:
Screen capture of the I2C detected on the Rpi terminal.
Figure 10:
The Localhost web interface (screen capture) to control the vehicle.
Figure 11:
The complete assembly of the vehicle hardware system.
Figure 12:
The code snippet program of the motor calibration.
Figure 13:
The steering servo and PWM working principle.
Figure 14:
Table generated for steering Angle and PWM equivalent values.
Figure 15:
The custom-built indoor track design and its implementation.
Figure 16:
A sample training dataset from the track training.
Figure 17:
Training data model loss graph.
Figure 18:
The car algorithm flowchart.
Figure 19:
Training and autopilot steering angle histogram graph plot analysis.
No.
Convolution filters
Strides
FC layers
Parameters
Loss
1
12 × 3 × 3, 18 × 3 × 3, 24 × 3 × 3, 36 × 3 × 3
3,2,2,1
900, 246, 32
1,100k
0.098543