Udacity Self-Driving Car Engineer Nanodegree Graduate
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The summary of the projects of the Udacity Self-Driving Car Nanodegree
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Udacity Self-Driving Car Engineer Nanodegree Graduate
- 2. Nine months, 3 terms: Computer vision and deep learning (5 projects) Sensor fusion, localization, and control (5 projects) Path planning, concentrations, and systems (4 projects) Udacity's Self-Driving Car Engineer Nanodegree
- 3. Identify lane lines on the road, first in an image, then in a video stream utilizing image analysis techniques: color channels, thresholding, region masking, Canny edge detection, and Hough transform. View project on github Lane line detection
- 4. Traffic sign classification Classify traffic signs with logistic regression, neural networks, gradient descent, backpropagation, TensorFlow, regularization, and convolutional networks. View project on github
- 5. Behavioural cloning Train a deep network to drive a car using Keras, transfer learning, and data augmentation. View project on github
- 6. Identify the lane boundaries and curvature in a video from a front-facing camera on a car. Calibration and distortion correction, image rectification, color transforms, and gradient thresholding. View project on github Advanced lane detection
- 7. Identify vehicles in a video from a front-facing camera. Histogram of gradients, support vector machine, heat map, data normalization, shuffling. View project on github Vehicle detection and tracking
- 8. Implement an Extended Kalman Filter in C++ capable of real-time tracking of linear motion. Lidar and radar data fusion. View project on github Sensor fusion: extended Kalman filter
- 9. Code an Unscented Kalman Filter capable of tracking non-linear motion. Constant turn rate and velocity magnitude model, sigma point prediction, code optimization. View project on github Sensor fusion: unscented Kalman filter
- 10. With data and a map, determine the precise location of a vehicle using the principles of Markov localization. View project on github Localization: particle filter
- 11. Implement a PID (proportional-integral- derivative) controller in C++ to maneuver a vehicle around the track. View project on github PID controller
- 12. Implement a model predictive controller using Ipopt and CppAD to drive a vehicle around the track even with additional latency between commands. View project on github Model predictive controller
- 13. Design a path planner that is able to create smooth, safe paths for a vehicle to follow along a 3-lane highway with traffic, keep inside its lane, avoid hitting other cars, and pass slower moving traffic all by using localization, sensor fusion, and map data. View project on github Path planning
- 14. Label the pixels of a road in images using a Fully Convolutional Network (FCN). Encoder, decoder, skip connections. 1x1 convolution, inference optimization, and semantic segmentation. View project on github Advanced deep learning
- 15. Document the functional safety of a lane assistance system, demonstrating knowledge of functional safety including developing a hazard and risk analysis, safety concepts, and engineering safety requirements. View project on github Functional safety
- 16. Collaborate with an international team (level5-engineers), to develop code to safely control an autonomous Lincoln MKZ around a closed-circuit test track. View project on github System integration
- 17. The SDC Nanodegree program has been assessed as equivalent to a 60 credit package of learning at the master’s degree level on the New Zealand Qualifications Framework. Image credits Slide 02: creativeshot Slide 06: ricgu8086 Slide 07: Sheng Xu Slide 08: Udacity Slide 11: Wikipedia Slide 15: Udacity Slide 16: Udacity