Engineering students entering the Industry will need to be equipped with practical knowledge and design experience.
Detect highway lane lines on a video stream.
Use OpenCV image analysis techniques to identify lines, including Hough Transforms and Canny edge detection.
Build and train a deep neural network to classify traffic signs, using TensorFlow.
Experiment with different network architectures.
Perform image pre-processing and validation to guard against overfitting.
Build and train a convolutional neural network for end-to-end driving in a simulator, using TensorFlow and Keras.
Use optimization techniques such as regularization and dropout to generalize the network for driving on multiple tracks.
Build an advanced lane-finding algorithm using distortion correction, image rectification, color transforms, and gradient thresholding.
Identify lane curvature and vehicle displacement.
Overcome environmental challenges such as shadows and pavement changes.
Create a vehicle detection and tracking pipeline with OpenCV, histogram of oriented gradients (HOG), and support vector machines (SVM).
Implement the same pipeline using a deep network to perform detection.
Optimize and evaluate the model on video data from an automotive camera taken during highway driving.
Implement the extended Kalman filter in C++.
Simulate lidar and radar measurements are used to detect a bicycle that travels around your vehicle.
Use Kalman filter, lidar measurements and radar measurements to track the bicycle’s position and velocity.
This challenge consists of 6 Projects. Teams are expected to complete all the projects. The entire event will be conducted online. Few face to face sessions are planned.
Lane Finding Basic
Traffic Sign Classifier
Advanced Lane Finding
Extended Kalman Filter
Lane Finding Basic:The goal of this first project was to create a simple pipeline to detect road lines in a frame taken from a roof-mounted camera.
Traffic Sign Classifier:The goal of this project was to build a CNN in TensorFlow to classify traffic sign images from the Traffic Sign Dataset.
Behavioral Cloning:The goal of the project was to train a Deep Network to replicate the human steering behavior while driving, thus being able to drive autonomously on a simulator provided by SAEISS. To this purpose, the network takes as input the frame of the frontal camera (say, a roof-mounted camera) and predicts the steering direction at each instant.
Advanced Lane Finding: The goals / steps of this project are the following:
Compute the camera calibration matrix and distortion coefficients given a set of chessboard images.
Apply a distortion correction to raw images.
Use color transforms, gradients, etc., to create a thresholded binary image.
Apply a perspective transform to rectify binary image ("birds-eye view").
Detect lane pixels and fit to find the lane boundary.
Determine the curvature of the lane and vehicle position with respect to center.
Warp the detected lane boundaries back onto the original image.
Output visual display of the lane boundaries and numerical estimation of lane curvature and vehicle position.
Vehicle Detection: The goal of the project was to develop a pipeline to reliably detect cars given a video from a roof-mounted camera.
Extended Kalman Filter: This goal is to implement the extended Kalman filter in C++. We are providing simulated lidar and radar measurements detecting a bicycle that travels around your vehicle. You will use a Kalman filter, lidar measurements and radar measurements to track the bicycle’s position and velocity.