Autonomous Driving Analysis, sumarry And Conclusion
Autonomous Neural Network Training
- Initially, a pretrained network was used with RGB images, but results were poor. with the following parameters:
Training Results
- Regression methods were tried but also showed poor performance. with the following parameters:
- 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.
- 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:
Experiment Analysis
- 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.
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.
- However, simultaneous recording, steering, and saving caused performance bottlenecks due to the Jetson Nano’s limited resources.
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.