Autonomous Driving Analysis, sumarry And Conclusion

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(without GPU Coder).
• 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.

Training Results

• Pretrained methods were tried but also showed poor performance. with the following parameters:

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

Fig. 9: regression_50_epoch2

Fig. 10: regression_50_epoch

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

Fig. 13: Camera setup for image recording during driving

More details of further work on Autonomous driving can be found here[].

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.