Applied CS

Vehicle Detection and Tracking. (Independent)

  • Created a vehicle detection and tracking pipeline with OpenCV, histogram of oriented gradients (HOG), and support vector machines (SVM).

  • Optimized and evaluated the model on video data from a automotive camera taken during highway driving.

Advanced Lane Detection on Roads (Independent)

  • Built an advanced lane-finding algorithm using distortion correction, image rectification, color transforms, and gradient thresholding.

  • Identified lane curvature and vehicle displacement.

  • Overcame environmental challenges such as shadows and pavement changes.

Use Deep Learning to Clone Driving Behavior (Independent)

  • Built and trained a convolutional neural network for end-to-end driving in a simulator, using TensorFlow and Keras.

  • Used optimization techniques such as regularization and dropout to generalize the network for driving on multiple tracks.

Traffic Sign Classification (Independent)

  • Built and trained a deep neural network to classify traffic signs, using TensorFlow.

  • Experimented with different network architectures.

  • Performed image pre-processing and validation to guard against overfitting.

Finding Lane Lines on the Road (Independent)

  • Detected highway lane lines on a video stream.

  • Used OpenCV image analysis techniques to identify lines, including Hough Transforms and Canny edge detection.