Jump into writing code that controls a simulated vehicle. Send throttle and steering commands to the car to try and get it to navigate around a test track.
- The
park
function causes the car to parallel park in the right lane without going off the road or hitting any of the other vehicles.
In this project you’ll practice using your object oriented programming and matrix math skills by filling out the methods in a partially-completed Matrix
class
-
If your code passes the provided tests in
test.py
then your project will meet specification for this criteria. -
determinant()
,trace()
,inverse()
,T() (transpose)
of matrix are calculated the right way and we get the correct output. -
add()
,neg()
,sub()
,mul()
,rmul()
are calculated the right way and we get the correct output. -
Code quality issues should not make a project non-passing unless they are problematic enough that it is not possible to understand the code at all with a reasonable amount of effort.
The goal for this course is translation: given a program written in Python, you’ll apply your knowledge of C++ syntax by translating the Histogram Filter code from the first course into C++.
-
Code passes the provided tests in
tests.cpp
-
'initialize_beliefs'
is implemented the right way. -
'sense'
is implemented the right way. -
'move'
is implemented the right way. -
Code quality issues should not make a project non-passing unless they are problematic enough that it is not possible to understand the code at all with a reasonable amount of effort.
In this project, you will build a route-planning algorithm like the one used in Google Maps to calculate the shortest path between two points on a map.
-
Running
test.py
shows "all tests pass". -
The student implements all required methods. (New Version).
-
The
heuristic function
used to estimate the distance between two intersections is guaranteed to return a distance which is less than or equal to the true path length between the intersections. -
Student answered all question correctly. (New Version Only)
-
Code avoids obvious inappropriate use of lists and takes advantage of the performance improvement afforded by sets / dictionaries where appropriate. For example, a data structure like the
"open_set"
on which membership checks are frequently performed (e.g. if node inopen_set
) should not be a list.
In this project, you’ll use your knowledge of computer vision techniques to build a classifier for images of traffic lights! You'll be given a dataset of traffic light images , pre-process these images, extract features and use those features to classify the traffic light images into three categories: red, yellow, or green.
-
In the project notebook, all questions are answered.
-
All input images (before they are classified) should be processed so that they are the same size.
-
All labels should be a one-hot encoded vector of length 3. Ex. ‘yellow’ becomes:
[0, 1, 0]
. -
Using
HSV colorspace
, extract a feature from a traffic light image that represents the level(s) of brightness in an image. This feature can help classify any traffic light image. A feature can be a list, array, or a single value. -
Using any created features, write a classification function that takes in a standardized
RGB
image and outputs whether a traffic light is red, yellow, or green as a one-hot encoded label. -
The model must have greater than
90%
accuracy on the given test set. -
In the given test set, red traffic lights can never be mistakenly labeled as green.