Stability Improvements

drive.py

@@ -44,7 +44,7 @@ def update(self, measurement):
 
 
 controller = SimplePIController(0.1, 0.002)
-set_speed = 9
+set_speed = 30
 controller.set_desired(set_speed)
 
 

process.py

@@ -0,0 +1,86 @@
+import csv
+import cv2
+import numpy as np
+
+lines=[]
+
+with open('./Data_buena2/driving_log.csv') as csvfile:
+    reader=csv.reader(csvfile)
+    for line in reader:
+        lines.append(line)
+
+images=[]
+measurements=[]
+correction=.2
+
+for line in lines:
+    for i in range(3):
+        source_path=line[i]
+        tokens=source_path.split('\\')
+        filename=tokens[-1]
+        local_path="./Data_buena2/IMG/"+filename
+        image=cv2.imread(local_path)
+        image=cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+        images.append(image)
+    measurement=float(line[3])
+    measurements.append(measurement)
+    measurements.append(measurement+correction)
+    measurements.append(measurement-correction)
+
+
+augmented_images=[]
+augmented_measurements=[]
+for image,measurement in zip(images,measurements):
+    augmented_images.append(image)
+    augmented_measurements.append(measurement)
+    flipped_image=cv2.flip(image,1)
+    augmented_images.append(flipped_image)
+    augmented_measurements.append(-1*measurement)
+
+
+X_train=np.array(augmented_images)
+y_train=np.array(augmented_measurements)
+
+
+import keras
+from keras.models import Sequential
+from keras.layers import Flatten, Dense, Lambda , Dropout, ELU
+from keras.layers.convolutional import Convolution2D,Cropping2D
+from keras.layers.pooling import MaxPooling2D
+from keras.optimizers import Adam
+
+
+model = Sequential()
+model.add(Lambda(lambda x: x/255.0 -.5, input_shape=(160,320,3)))
+model.add(Cropping2D(cropping=((70,25),(0,0))))
+model.add(MaxPooling2D())
+model.add(Convolution2D(5, 5, 24, subsample=(4, 4), border_mode="same"))
+model.add(ELU())
+model.add(Convolution2D(5, 5, 36, subsample=(2, 2), border_mode="same"))
+model.add(ELU())
+model.add(Convolution2D(5, 5, 48, subsample=(2, 2), border_mode="same"))
+model.add(ELU())
+model.add(Convolution2D(3, 3, 64, subsample=(2, 2), border_mode="same"))
+model.add(ELU())
+model.add(Convolution2D(3, 3, 64, subsample=(2, 2), border_mode="same"))
+model.add(Flatten())
+model.add(ELU())
+model.add(Dense(1164))
+model.add(Dropout(.5))
+model.add(ELU())
+model.add(Dense(100))
+model.add(Dropout(.5))
+model.add(ELU())
+model.add(Dense(50))
+model.add(ELU())
+model.add(Dense(10))
+model.add(Dropout(.2))
+model.add(ELU())
+model.add(Dense(1))
+adam = Adam(lr=0.00001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)
+model.compile(optimizer=adam, loss="mse", metrics=['accuracy'])
+model.summary()
+model.fit(X_train,y_train,validation_split=.2,shuffle=True,nb_epoch=20)
+
+model.save("model.h5")
+