Walkthrough all Matlab Code Used In This Project

Tabelle 12: Storing input data

Name	Application	Description
storingcropsteeringandframesoncsvrgb.m	Storing steering values and recorded frames	Steering values are stored in a CSV file as well as their corresponding frames.
storingcropsteeringandframesoncsvrgb_calibrated.m	Storing steering values and calibrated recorded frames in RGB.	Steering values are stored in a CSV file as well as their corresponding calibrated frames in RGB.
Frames_conversion.m	Convert RGB frames to BW, gray, and Canny edge.
Video_conversion.m		Pretrained NN to label each pixel in an image.
speedlimit.m		Pretrained NN to label each pixel in an image.
storingcropsteeringandframesoncsvrgb_calibrated.m		Pretrained NN to label each pixel in an image.

Tabelle 8: Calibration

Name	Application	Description
Calibrationchecker.m	ResNet-50/101	Pretrained NN for image classification tasks.
Calibrationchecker_video.m		Pretrained models for detecting and localizing objects.
test_camera_calibration.m		Pretrained NN to label each pixel in an image.
256by256frames.m		Pretrained NN to label each pixel in an image.
cali.m		Pretrained NN to label each pixel in an image.
calibration_256_256_script.m		Pretrained NN to label each pixel in an image.
calibration_500by220.m		Pretrained NN to label each pixel in an image.
calibration_500by220_final.m		Pretrained NN to label each pixel in an image.
calibration_with_one_image.m		Pretrained NN to label each pixel in an image.
test_extractedframes_256by256_calibrationpara.m		Pretrained NN to label each pixel in an image.
Bird_eye_view.m		Pretrained NN to label each pixel in an image.

Tabelle 8: Lane Trackin

Name	Application	Description
Lane_angle_tracking.m	ResNet-50/101	Pretrained NN for image classification tasks.
Lane_tracking.m		Pretrained models for detecting and localizing objects.
Lane_tracking_lane_change.m		Pretrained NN to label each pixel in an image.
addinglanepliudot.m		Pretrained NN to label each pixel in an image.
Lane_tracking_lane_change.m		Pretrained NN to label each pixel in an image.

Tabelle 8: python libraries

Name	Application	Description
Jtest.py	ResNet-50/101	Pretrained NN for image classification tasks.
Jtest0.py		Pretrained models for detecting and localizing objects.
Jtest01.py		Pretrained NN to label each pixel in an image.
Jtest02.py		Pretrained NN to label each pixel in an image.
Jtest2.py		Pretrained NN to label each pixel in an image.
Jtest4.py		Pretrained NN to label each pixel in an image.

Tabelle 8: autonomous driving

Name	Application	Description
autonomous_driving.m	ResNet-50/101	Pretrained NN for image classification tasks.
autonomous_driving_lanetracking.m		Pretrained models for detecting and localizing objects.
RunningtheJetRacerAutonomouslyupdate.m		Pretrained NN to label each pixel in an image.
RunningtheJetRacerAutonomously.m		Pretrained NN to label each pixel in an image.

Tabelle 8: Models

Name	Application	Description
Load	ResNet-50/101	Pretrained NN for image classification tasks.
Retrain		Pretrained models for detecting and localizing objects.
Finetune		Pretrained NN to label each pixel in an image.

Tabelle 8: Training

Name	Application	Description
Cleaned_RGB_CNN_Classify_model.m	ResNet-50/101	Pretrained NN for image classification tasks.
Cleaned_RGB_RGBRect_CNN_Classify_model.m		Pretrained models for detecting and localizing objects.
Complete_CNN_Classification.m		Pretrained NN to label each pixel in an image.
RGB_CNN_Classify_model.m	ResNet-50/101	Pretrained NN for image classification tasks.
RGB_CNN_model.m		Pretrained models for detecting and localizing objects.
RGB_CNN_Regression_model.m		Pretrained NN to label each pixel in an image.
RGB_LSTM_model.m	ResNet-50/101	Pretrained NN for image classification tasks.
RGB_Pretrained_model.m		Pretrained models for detecting and localizing objects.
RGB_Pretrained_model_script.m		Pretrained NN to label each pixel in an image.
RGB_Reinforcementlearning_model.m	ResNet-50/101	Pretrained NN for image classification tasks.
RGB_RNN_model.m		Pretrained models for detecting and localizing objects.
RGB_Transferlearning_model.m		Pretrained NN to label each pixel in an image.
RGBmodel.m	ResNet-50/101	Pretrained NN for image classification tasks.
Cleaned_Gray_CNN_Classify_model.m		Pretrained models for detecting and localizing objects.
Gray_CNN_model - Kopie.m		Pretrained NN to label each pixel in an image.
Gray_CNN_model.m	ResNet-50/101	Pretrained NN for image classification tasks.
Gray_LSTM_model.m		Pretrained models for detecting and localizing objects.
Gray_Pretrained_model.m		Pretrained NN to label each pixel in an image.
Gray_Reinforcementlearning_model.m	ResNet-50/101	Pretrained NN for image classification tasks.
Gray_RNN_model.m		Pretrained NN to label each pixel in an image.
Gray_Transferlearning_model.m	ResNet-50/101	Pretrained NN for image classification tasks.
Graymodel.m		Pretrained NN to label each pixel in an image.
greendottraining.m	ResNet-50/101	Pretrained NN for image classification tasks.
online_training.m		Pretrained NN to label each pixel in an image.
online_training_RNN.m	ResNet-50/101	Pretrained NN for image classification tasks.
PretrainedNN.m		Pretrained NN to label each pixel in an image.
resnet_pretrained.m	ResNet-50/101	Pretrained NN for image classification tasks.
ResNet18.m		Pretrained NN to label each pixel in an image.
ResNet18gray.m	ResNet-50/101	Pretrained NN for image classification tasks.
ResNet18rgb.m		Pretrained NN to label each pixel in an image.
Resnet18trainonceperfram.m	ResNet-50/101	Pretrained NN for image classification tasks.
traininglaneplusdot.m		Pretrained NN to label each pixel in an image.

Tabelle 8: .MAT files

Name	Application	Description
cameraParams.mat	ResNet-50/101	Pretrained NN for image classification tasks.
cameraCalibrationParams.mat		Pretrained models for detecting and localizing objects.
Finetune		Pretrained NN to label each pixel in an image.