The document outlines the curriculum for Udacity's Self-Driving Car Engineer Nanodegree program, which is divided into three terms covering computer vision and deep learning, sensor fusion and control, and path planning and systems. The terms include 14 projects involving techniques like lane detection, traffic sign classification, behavioral cloning, Kalman filtering, particle filtering, PID control, path planning and more. Upon completion, the program has been assessed as equivalent to a 60 credit master's degree program.

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