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Revistas
International Journal on Smart Sensing and Intelligent Systems
Volumen 13 (2020): Edición 1 (January 2020)
Acceso abierto
1/10th scale autonomous vehicle based on convolutional neural network
Avishkar Seth
Avishkar Seth
,
Alice James
Alice James
y
Subhas C. Mukhopadhyay
Subhas C. Mukhopadhyay
| 25 ago 2020
International Journal on Smart Sensing and Intelligent Systems
Volumen 13 (2020): Edición 1 (January 2020)
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Article Category:
Research-Article
Publicado en línea:
25 ago 2020
Páginas:
1 - 17
Recibido:
26 jul 2020
DOI:
https://doi.org/10.21307/ijssis-2020-021
Palabras clave
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