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+https://drive.google.com/file/d/1kqcGNFD2SyplB15PNQxAQnc-f6okLPem/view?usp=sharing
diff --git a/labs/terrainclassification/TerrainNet.ipynb b/labs/terrainclassification/TerrainNet.ipynb
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+++ b/labs/terrainclassification/TerrainNet.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nJupyter Notebook for training and testing the Terrain Classification ResNet-18 CNN. \\n'"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "Jupyter Notebook for training and testing the Terrain Classification ResNet-18 CNN. \n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "Cell Purpose: Import all required modules\n",
+    "\"\"\"\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn import metrics\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "from torch.utils.data import DataLoader\n",
+    "import torchvision.transforms as transforms\n",
+    "from torchvision import datasets, models    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading: \"https://download.pytorch.org/models/resnet18-f37072fd.pth\" to C:\\Users\\gupte/.cache\\torch\\hub\\checkpoints\\resnet18-f37072fd.pth\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f2f6dce4f8044a12addc2560657a6274",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0.00/44.7M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "Cell Purpose: Defining important model parameters\n",
+    "\"\"\"\n",
+    "\n",
+    "#Mapping for CNN output\n",
+    "label_map = {\"Asphalt\": 0, \"Dirt\": 1, \"Grassy\": 2, \"Rocky\": 3}\n",
+    "\n",
+    "#CNN parameters\n",
+    "batch_size = 16\n",
+    "num_workers = 0\n",
+    "\n",
+    "#Training and testing transforms:\n",
+    "train_transform = transforms.Compose([transforms.RandomResizedCrop(224),\n",
+    "                                      transforms.RandomHorizontalFlip(p=0.5),\n",
+    "                                      transforms.RandomRotation(10),\n",
+    "                                      transforms.ColorJitter(brightness=0.4, contrast=0.4, saturation=0.4, hue=0),\n",
+    "                                      transforms.ToTensor(),\n",
+    "                                      transforms.Normalize((0.485,0.456,0.406), (0.229,0.224,0.225))\n",
+    "                                    ])\n",
+    "test_transform = transforms.Compose([transforms.RandomResizedCrop(224),\n",
+    "                                     transforms.CenterCrop(224),\n",
+    "                                     transforms.ToTensor(),\n",
+    "                                     transforms.Normalize((0.485,0.456,0.406), (0.229,0.224,0.225))\n",
+    "                                    ])\n",
+    "\n",
+    "#Training and testing datasets:\n",
+    "train_data = datasets.ImageFolder(\"C:/Users/gupte/Desktop/racecar-arjun-gupte/labs/FinalDataSet/Train\", transform=train_transform)\n",
+    "test_data = datasets.ImageFolder(\"C:/Users/gupte/Desktop/racecar-arjun-gupte/labs/FinalDataSet/Test\", transform=test_transform)\n",
+    "\n",
+    "#Training and testing data loaders:\n",
+    "train_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size, num_workers=num_workers, shuffle=True)\n",
+    "test_loader = torch.utils.data.DataLoader(test_data, batch_size=batch_size, num_workers=num_workers, shuffle=False)\n",
+    "\n",
+    "#Create pre-trained ResNet18 model\n",
+    "model = models.resnet18(pretrained=True)\n",
+    "\n",
+    "#Change last layer's output dimension to the desired number (4 output categories => out_features = 4)\n",
+    "model.fc = nn.Linear(in_features=512, out_features=4)\n",
+    "#print(model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+      "Epoch: 23 Iteration: 27 out of: 27\n",
+      "Epoch: 24 \tTraining Loss: 0.085930\n",
+      "Epoch: 24 Iteration: 0 out of: 27\n",
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+      "Epoch: 24 Iteration: 21 out of: 27\n",
+      "Epoch: 24 Iteration: 22 out of: 27\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 24 Iteration: 23 out of: 27\n",
+      "Epoch: 24 Iteration: 24 out of: 27\n",
+      "Epoch: 24 Iteration: 25 out of: 27\n",
+      "Epoch: 24 Iteration: 26 out of: 27\n",
+      "Epoch: 24 Iteration: 27 out of: 27\n",
+      "Epoch: 25 \tTraining Loss: 0.037157\n",
+      "Epoch: 25 Iteration: 0 out of: 27\n",
+      "Epoch: 25 Iteration: 1 out of: 27\n",
+      "Epoch: 25 Iteration: 2 out of: 27\n",
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+      "Epoch: 25 Iteration: 11 out of: 27\n",
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+      "Epoch: 25 Iteration: 26 out of: 27\n",
+      "Epoch: 25 Iteration: 27 out of: 27\n",
+      "Epoch: 26 \tTraining Loss: 0.029099\n",
+      "Epoch: 26 Iteration: 0 out of: 27\n",
+      "Epoch: 26 Iteration: 1 out of: 27\n",
+      "Epoch: 26 Iteration: 2 out of: 27\n",
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+      "Epoch: 26 Iteration: 27 out of: 27\n",
+      "Epoch: 27 \tTraining Loss: 0.047497\n",
+      "Epoch: 27 Iteration: 0 out of: 27\n",
+      "Epoch: 27 Iteration: 1 out of: 27\n",
+      "Epoch: 27 Iteration: 2 out of: 27\n",
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+      "Epoch: 27 Iteration: 27 out of: 27\n",
+      "Epoch: 28 \tTraining Loss: 0.082898\n",
+      "Epoch: 28 Iteration: 0 out of: 27\n",
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+      "Epoch: 28 Iteration: 27 out of: 27\n",
+      "Epoch: 29 \tTraining Loss: 0.049404\n",
+      "Epoch: 29 Iteration: 0 out of: 27\n",
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+      "Epoch: 29 Iteration: 15 out of: 27\n",
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+      "Epoch: 29 Iteration: 18 out of: 27\n",
+      "Epoch: 29 Iteration: 19 out of: 27\n",
+      "Epoch: 29 Iteration: 20 out of: 27\n",
+      "Epoch: 29 Iteration: 21 out of: 27\n",
+      "Epoch: 29 Iteration: 22 out of: 27\n",
+      "Epoch: 29 Iteration: 23 out of: 27\n",
+      "Epoch: 29 Iteration: 24 out of: 27\n",
+      "Epoch: 29 Iteration: 25 out of: 27\n",
+      "Epoch: 29 Iteration: 26 out of: 27\n",
+      "Epoch: 29 Iteration: 27 out of: 27\n",
+      "Epoch: 30 \tTraining Loss: 0.052002\n"
+     ]
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "Cell Purpose: Train the model\n",
+    "\"\"\"\n",
+    "\n",
+    "#Model loss function\n",
+    "criterion = nn.CrossEntropyLoss()\n",
+    "\n",
+    "#Model optimizer function\n",
+    "optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9)\n",
+    "\n",
+    "#Model scheduler function\n",
+    "scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.1)\n",
+    "\n",
+    "#Number of epochs for training\n",
+    "n_epochs = 30\n",
+    "\n",
+    "#Number of iterations per epoch for training - given batch size, it will take n_iterations to complete one epoch\n",
+    "n_iterations = int(len(train_data)/batch_size)\n",
+    "\n",
+    "#List for training loss values\n",
+    "train_loss_progress = []\n",
+    "\n",
+    "#Put the model in training mode\n",
+    "model.train()\n",
+    "\n",
+    "#Go through each epoch\n",
+    "for epoch in range(n_epochs):\n",
+    "\n",
+    "    #Reset training loss every epoch\n",
+    "    train_loss = 0.0\n",
+    "\n",
+    "    #Go through each iteration\n",
+    "    for iter, (data, target) in enumerate(train_loader):\n",
+    "\n",
+    "        print(\"Epoch:\", epoch, \"Iteration:\", iter, \"out of:\", n_iterations)\n",
+    "\n",
+    "        #Clear the gradients of all optimized variables every iteration\n",
+    "        optimizer.zero_grad()\n",
+    "\n",
+    "        #Forward pass: Feed data into model and get outputs\n",
+    "        outputs = model(data)\n",
+    "        #print(outputs)\n",
+    "\n",
+    "        #Calculate the loss (error)\n",
+    "        loss = criterion(outputs, target)\n",
+    "\n",
+    "        #Backward pass: Compute gradient of the loss with respect to model parameters\n",
+    "        loss.backward()\n",
+    "\n",
+    "        #Perform a single optimization step (parameter update)\n",
+    "        optimizer.step()\n",
+    "\n",
+    "        #Update training loss for the epoch (multiply by data.size(0) because we are calculating loss for not one, but data.size() images)\n",
+    "        train_loss += loss.item()*data.size(0)\n",
+    "\n",
+    "    #Have the scheduler take a step\n",
+    "    scheduler.step()\n",
+    "\n",
+    "    #Calculate average loss for the epoch and append to train_loss_progress\n",
+    "    train_loss = train_loss/len(train_loader.dataset)\n",
+    "    train_loss_progress.append(train_loss)\n",
+    "    print('Epoch: {} \\tTraining Loss: {:.6f}'.format(epoch, train_loss))\n",
+    "    \n",
+    "\n",
+    "#Save the model after training is finished\n",
+    "PATH = \"./TerrainNet.pt\"\n",
+    "torch.save(model.state_dict(), PATH)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Final accuracy of the network on the test set: 87 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "Cell Purpose: Test the model\n",
+    "\"\"\"\n",
+    "\n",
+    "correct_predictions = 0\n",
+    "total_predictions = 0\n",
+    "target_values = np.array([])\n",
+    "predicted_values = np.array([])\n",
+    "\n",
+    "#Stores model accuracy as percentages\n",
+    "test_accuracy_progress = []\n",
+    "\n",
+    "#Put the model in testing mode\n",
+    "model.eval()\n",
+    "\n",
+    "with torch.no_grad():\n",
+    "    \n",
+    "    #16 images at a time (batch size = 16)\n",
+    "    for data, target in test_loader:\n",
+    "\n",
+    "        #Pass data into model and get output\n",
+    "        outputs = model(data)\n",
+    "        _, predicted = torch.max(outputs.data, 1)\n",
+    "\n",
+    "        #Update performance variables\n",
+    "        total_predictions += target.size(0)\n",
+    "        correct_predictions += (predicted == target).sum().item() \n",
+    "        predicted = predicted.float()\n",
+    "        target = target.float()\n",
+    "\n",
+    "        #Record predicted and target values\n",
+    "        predicted_values = np.concatenate([predicted_values, predicted.tolist()])\n",
+    "        target_values = np.concatenate([target_values, target.tolist()])\n",
+    "\n",
+    "        #Record model accuracy for the current batch of images\n",
+    "        test_accuracy_progress.append(100 * correct_predictions / total_predictions)\n",
+    "\n",
+    "print('Final accuracy of the network on the test set: %d %%' % (100 * correct_predictions / total_predictions))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "Cell Purpose: Plot training and testing results\n",
+    "\"\"\"\n",
+    "\n",
+    "#Plot training results\n",
+    "training_loss_range = np.arange(1, len(train_loss_progress) + 1)\n",
+    "fig, axs = plt.subplots()\n",
+    "axs.plot(training_loss_range, train_loss_progress, c='b', label=\"Train loss\")\n",
+    "plt.xlabel('Epochs')\n",
+    "plt.ylabel('Loss')\n",
+    "plt.xticks(range(0, n_epochs + 1, 5), range(0, n_epochs + 1, 5))\n",
+    "axs.legend()\n",
+    "plt.show()\n",
+    "\n",
+    "#Plot testing results\n",
+    "testing_accuracy_range = np.arange(1, len(test_accuracy_progress) + 1)\n",
+    "fig, axs = plt.subplots()\n",
+    "axs.plot(testing_accuracy_range, test_accuracy_progress, c='r', label=\"Test accuracy\")\n",
+    "plt.xlabel('Batches')\n",
+    "plt.ylabel('Accuracy')\n",
+    "plt.xticks(range(0, len(test_accuracy_progress) + 1, 2), range(0, len(test_accuracy_progress) + 1, 2))\n",
+    "axs.legend()\n",
+    "plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "Cell Purpose: Create confusion matrix for results\n",
+    "\"\"\"\n",
+    "\n",
+    "#Generate and configure confusion matrix\n",
+    "confusion_matrix = metrics.confusion_matrix(target_values, predicted_values)\n",
+    "labels = list(label_map.keys())\n",
+    "df_confusion_matrix = pd.DataFrame(confusion_matrix, index=labels, columns=labels)\n",
+    "fig = plt.figure()\n",
+    "res = sns.heatmap(df_confusion_matrix, annot=True, cmap='Blues')\n",
+    "\n",
+    "plt.title(\"Confusion Matrix\")\n",
+    "plt.ylabel('True label')\n",
+    "plt.xlabel('Predicted label')\n",
+    "\n",
+    "plt.show()\n",
+    "plt.close()"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "0ba8f08b784b7e67edf2cffa16fb978c43f2330bd47da969bc2ffbc904ee1fee"
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/labs/terrainclassification/terrain_classification.py b/labs/terrainclassification/terrain_classification.py
new file mode 100644
index 0000000..61c1b4d
--- /dev/null
+++ b/labs/terrainclassification/terrain_classification.py
@@ -0,0 +1,87 @@
+"""
+Copyright MIT and Harvey Mudd College
+MIT License
+Summer 2022
+
+Bonus Lab - Terrain Classification
+"""
+
+########################################################################################
+# Imports
+########################################################################################
+
+import sys
+
+sys.path.insert(1, "../../library")
+import racecar_core
+import racecar_utils as rc_utils
+
+#Put any additional import statements that are required here
+
+########################################################################################
+# Global variables
+########################################################################################
+
+rc = racecar_core.create_racecar()
+
+# Put any global variables here
+test_transform = transforms.Compose([transforms.RandomResizedCrop(224),
+                                     transforms.CenterCrop(224),
+                                     transforms.ToTensor(),
+                                     transforms.Normalize((0.485,0.456,0.406), (0.229,0.224,0.225))
+                                   ])
+
+terrain_dictionary = {0: "Asphalt", 1: "Dirt", 2: "Grass", 3: "Rocky"}
+
+########################################################################################
+# Functions
+########################################################################################
+
+
+def start():
+    """
+    This function is run once every time the start button is pressed
+    """
+
+    # Begin at a full stop
+    rc.drive.stop()
+
+    # Print start message
+    print(">> Bonus Lab - Terrain Classification\n")
+
+    #This line loads the pre-trained ResNet-18 CNN model
+    model.load_state_dict(torch.load("TerrainNet.pt"))
+
+
+def update():
+    """
+    After start() is run, this function is run every frame until the back button
+    is pressed
+    """
+    #Control the car's steering angle with the left joystick or "A" and "D" keys
+    (x, y) = rc.controller.get_joystick(rc.controller.Joystick.LEFT)
+
+    # TODO (warmup): Capture an image (the camera_image variable) from the color camera every second
+    # Next, process the image to ensure the CNN is classifying only the terrain in the image
+
+
+    #The following two lines save the image as a .jpg in the current working directory
+    image = Image.fromarray(camera_image)
+    image.save("./currentCameraImage.jpg")
+
+    # TODO (main challenge): Have the CNN process the image and print the terrain being driven on
+    with torch.no_grad():
+        #The following three lines open the saved image, apply the test transforms, and perform additional pre-processing to the image.
+        image = Image.open("./currentCameraImage.jpg")
+        image_tensor = test_transform(image).float()
+        image_tensor = image_tensor.unsqueeze_(0)
+
+    # TODO (main challenge): Adjust the speed of the car based on the terrain (rougher terrains - lower speed, smoother terrains - higher speed)
+
+########################################################################################
+# DO NOT MODIFY: Register start and update and begin execution
+########################################################################################
+
+if __name__ == "__main__":
+    rc.set_start_update(start, update)
+    rc.go()
diff --git a/labs/terrainclassification/terrain_classification_solution.py b/labs/terrainclassification/terrain_classification_solution.py
new file mode 100644
index 0000000..f674973
--- /dev/null
+++ b/labs/terrainclassification/terrain_classification_solution.py
@@ -0,0 +1,121 @@
+"""
+Copyright MIT and Harvey Mudd College
+MIT License
+Summer 2022
+
+Bonus Lab - Terrain Classification
+"""
+
+########################################################################################
+# Imports
+########################################################################################
+
+import sys
+
+sys.path.insert(1, "../../library")
+import racecar_core
+import racecar_utils as rc_utils
+
+#Put any additional import statements that are required here
+import cv2 as cv
+import torch
+import torch.nn as nn
+import torchvision.transforms as transforms
+from torch.autograd import Variable
+from PIL import Image
+import PIL
+########################################################################################
+# Global variables
+########################################################################################
+
+rc = racecar_core.create_racecar()
+
+# Put any global variables here
+model = models.resnet18(pretrained = True)
+model.fc = torch.nn.Linear(512, 4)
+model = model.eval()
+
+test_transform = transforms.Compose([transforms.RandomResizedCrop(224),
+                                     transforms.CenterCrop(224),
+                                     transforms.ToTensor(),
+                                     transforms.Normalize((0.485,0.456,0.406), (0.229,0.224,0.225))
+                                   ])
+
+terrain_dictionary = {0: "Asphalt", 1: "Dirt", 2: "Grass", 3: "Rocky"}
+
+########################################################################################
+# Functions
+########################################################################################
+
+
+def start():
+    """
+    This function is run once every time the start button is pressed
+    """
+
+    # Begin at a full stop
+    rc.drive.stop()
+
+    # Print start message
+    print(">> Bonus Lab - Terrain Classification\n")
+
+    #This line loads the pre-trained ResNet-18 CNN model
+    model.load_state_dict(torch.load("TerrainNet.pt"))
+
+
+def update():
+    """
+    After start() is run, this function is run every frame until the back button
+    is pressed
+    """
+    #Control the car's steering angle with the left joystick or "A" and "D" keys
+    (x, y) = rc.controller.get_joystick(rc.controller.Joystick.LEFT)
+
+    # TODO (warmup): Capture an image (the camera_image variable) from the color camera every second
+    # Next, process the image to ensure the CNN is classifying only the terrain
+    if counter % 60 == 0:
+        camera_image = rc.camera.get_color_image()
+        camera_image = rc_utils.crop(camera_image, (rc.camera.get_height() // 2, 0), (rc.camera.get_height(), rc.camera.get_width()))
+        camera_image = cv.cvtColor(camera_image, cv.COLOR_BGR2RGB)
+
+        #The following two lines save the image as a .jpg in the current working directory
+        image = Image.fromarray(camera_image)
+        image.save("./currentCameraImage.jpg")
+
+
+    # TODO (main challenge): Have the CNN process the image and print the terrain being driven on
+        with torch.no_grad():
+            #The following three lines open the saved image, apply the test transforms, and perform additional pre-processing to the image.
+            image = Image.open("./currentCameraImage.jpg")                                 #Open the image taken from above
+            image_tensor = test_transform(image).float()                                   #Perform all transforms on the image
+            image_tensor = image_tensor.unsqueeze_(0)                                      #Add another filler dimension of size 1
+
+            model_output = model(Variable(image_tensor))                                   #Pass the image into the model
+            _, predicted_category = torch.max(model_output.data, 1)                        #Process the predicted category
+            predicted_category = predicted_category.float()                                #Convert the predicted category to a float
+
+        terrain = terrain_dictionary.get(predicted_category.tolist()[0])                   #Find the corresponding terrain from the terrain dictionary
+
+    # TODO (main challenge): Adjust the speed of the car based on the terrain (rougher terrains - lower speed, smoother terrains - higher speed)
+        if terrain == "Asphalt":
+            speed = 1.0
+            print("Asphalt: MAX SPEED\n\n")
+        elif terrain == "Grass":
+            speed = 0.75
+            print("Grass: MODERATE SPEED\n\n")
+        elif terrain == "Dirt":
+            speed = 0.5
+            print("Dirt: MODERATE SPEED\n\n")
+        elif terrain == "Rocky":
+            speed = 0.25
+            print("Rocky: LOW SPEED\n\n")
+
+    rc.drive.set_speed_angle(speed, angle)
+
+########################################################################################
+# DO NOT MODIFY: Register start and update and begin execution
+########################################################################################
+
+if __name__ == "__main__":
+    rc.set_start_update(start, update)
+    rc.go()