About Self-Driving Car Challenge

Engineering students entering the Industry will need to be equipped with practical knowledge and design experience.

Challenge Objective

• 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.

Challenge Elements

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.

Project Id	Project Name
1	Lane Finding Basic
2	Traffic Sign Classifier
3	Behavioral Cloning
4	Advanced Lane Finding
5	Vehicle Detection
6	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.