Evrard.leuteu-feukeu@stud.hshl.de: Die Seite wurde neu angelegt: „=== Materials and Setup === * Jetson Nano Developer Kit * USB Gamepad * MATLAB® 2020B (compatible with CUDA 10.2) [https://de.mathworks.com/support/requirements/supported-compilers.html] * Wi-Fi connection between Jetson Nano and host PC === Experiment Design and Requirements === * MATLAB® was used to connect to the Jetson Nano via SSH. * The Gamepad receiver had to be physically connected to the Jetson Nano. * MATLAB® controlled the Gamepad indire…“

2025-04-30T14:56:37Z

Die Seite wurde neu angelegt: „=== Materials and Setup === * Jetson Nano Developer Kit * USB Gamepad * MATLAB® 2020B (compatible with CUDA 10.2) [https://de.mathworks.com/support/requirements/supported-compilers.html] * Wi-Fi connection between Jetson Nano and host PC === Experiment Design and Requirements === * MATLAB® was used to connect to the Jetson Nano via SSH. * The Gamepad receiver had to be physically connected to the Jetson Nano. * MATLAB® controlled the Gamepad indire…“

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=== Materials and Setup ===

* Jetson Nano Developer Kit
* USB Gamepad
* MATLAB® 2020B (compatible with CUDA 10.2) [https://de.mathworks.com/support/requirements/supported-compilers.html]
* Wi-Fi connection between Jetson Nano and host PC

=== Experiment Design and Requirements ===

* MATLAB® was used to connect to the Jetson Nano via SSH.
* The Gamepad receiver had to be physically connected to the Jetson Nano.
* MATLAB® controlled the Gamepad indirectly using SSH commands to access libraries on the Jetson.
* Data such as images and steering values were collected and transferred via Wi-Fi.

[[File:matlab_ssh_code
[[Datei:Matlab ssh code.jpg|mini]]
.jpg|frame|Fig. 3: Ssh code snipped to connect using MATLAB and JetRacer]]

== Experiment Procedure ==

=== Step 1: System Connection and Gamepad Control ===
Since MATLAB® could not directly access the Gamepad receiver, an SSH command method was developed to control the Jetson Nano libraries from the background without delay or signal loss.

=== Step 2: Data Collection ===
* Data was collected using the integrated camera at 256×256 pixel resolution.
* Real-time steering commands were logged and synchronized with images.
* To reduce image distortion, camera calibration was performed using a calibration256by256.mat parameter.
[[File:Camera_no_calibration.png|frame|Fig. 5: Camera setup for image without calibration]]
[[File:
[[Datei:Camera_with_calibration.png|mini]]
.png|frame|Fig. 6: Camera setup for image with calibration]]

=== Step 3: Neural Network Training ===
* Initially, a pretrained network was used with RGB images, but results were poor. with the following parameters:

[[Datei:Pretrained 50 epoch.png|mini]]
[[Datei:Pretrained 50 epoch 2.png|mini]]

* Regression methods were tried but also showed poor performance. with the following parameters:

[[File:regression_50_epoch2.png|mini]]|Fig. 9: regression_50_epoch2]]

[[File:regression_50_epoch.png|mini]]|frame|Fig. 10: regression_50_epoch]]

* The project switched to **classification** using **discrete steering values**. with the following parameters:

* MATLAB® Apps (GUI) were used to manually annotate images by clicking the correct path.

=== Step 4: System Optimization ===
* Lane tracking algorithms were integrated to automatically label lane positions.
* Additional features like lane angles were extracted to improve model inputs.
* Grayscale image recordings were used to achieve better generalization and faster processing.
* Still using **classification**. with the following parameters:
[[File:
[[Datei:Cleaned CNN Classify.png|mini]]
.png|frame|Fig. 13: Camera setup for image recording during driving]]

== Experiment Analysis ==

=== Training Results ===
* Models trained on discrete steering values showed significantly better results than regression-based approaches.
* Grayscale recordings increased model stability.
* Calibrated images helped reduce the steering error caused by lens distortion.

=== System Performance ===
* A maximum speed limit (~1 m/s) was successfully enforced via software.
* The JetRacer could autonomously drive several laps counterclockwise in the right lane.
* However, simultaneous recording, steering, and saving caused performance bottlenecks due to the Jetson Nano’s limited resources.

=== Limitations ===
* Hardware constraints limited the maximum processing speed.
* Initial data quality was poor due to randomized manual steering input.
* Better results were obtained after switching to discrete, consistent control signals.

== Summary and Outlook ==

This project demonstrated that it is possible to optimize autonomous navigation on the JetRacer using a combination of classical control algorithms and neural networks. Despite hardware limitations, stable autonomous driving behavior was achieved.

Future improvements could include:
* Expanding the dataset with more diverse environmental conditions.
* Flash working Model directly on jetson nano using gpu coder
* Use accurate gamepad and use more complex training method.
* Improving automated labeling and refining the PD controller parameters for faster driving without loss of robustness.

Experiment Preparation For Autonomous Driving - Versionsgeschichte