Sensor Fusion UKF Highway Project Starter Code
In this project you will implement an Unscented Kalman Filter to estimate the state of multiple cars on a highway using noisy lidar and radar measurements. Passing the project requires obtaining RMSE values that are lower that the tolerance outlined in the project rubric.
The main program can be built and ran by doing the following from the project top directory.
- mkdir build
- cd build
- cmake ..
- make
- ./ukf_highway
Note that the programs that need to be written to accomplish the project are src/ukf.cpp, and src/ukf.h
The program main.cpp has already been filled out, but feel free to modify it.
main.cpp is using highway.h to create a straight 3 lane highway environment with 3 traffic cars and the main ego car at the center.
The viewer scene is centered around the ego car and the coordinate system is relative to the ego car as well. The ego car is green while the
other traffic cars are blue. The traffic cars will be accelerating and altering their steering to change lanes. Each of the traffic car's has
it's own UKF object generated for it, and will update each indidual one during every time step.
The red spheres above cars represent the (x,y) lidar detection and the purple lines show the radar measurements with the velocity magnitude along the detected angle. The Z axis is not taken into account for tracking, so you are only tracking along the X/Y axis.
- cmake >= 3.5
- All OSes: click here for installation instructions
- make >= 4.1 (Linux, Mac), 3.81 (Windows)
- Linux: make is installed by default on most Linux distros
- Mac: install Xcode command line tools to get make
- Windows: Click here for installation instructions
- gcc/g++ >= 5.4
- Linux: gcc / g++ is installed by default on most Linux distros
- Mac: same deal as make - install Xcode command line tools
- Windows: recommend using MinGW
- PCL 1.2
- Clone this repo.
- Make a build directory:
mkdir build && cd build - Compile:
cmake .. && make - Run it:
./ukf_highway
We've purposefully kept editor configuration files out of this repo in order to keep it as simple and environment agnostic as possible. However, we recommend using the following settings:
- indent using spaces
- set tab width to 2 spaces (keeps the matrices in source code aligned)
Please stick to Google's C++ style guide as much as possible.
- FR.0 Compiling and Testing
- FR.1 Code Efficiency
- FR.2 Accuracy
- FR.3 Follows the Correct Algorithm
The project code must compile without errors using cmake and make.
The methods in the code should avoid unnecessary calculations.
px, py, vx, vy output coordinates must have an RMSE <= [0.30, 0.16, 0.95, 0.70] after running for longer than 1 second.
Your Sensor Fusion algorithm follows the general processing flow as taught in the preceding lessons.
This is optional!
If you'd like to generate your own radar and lidar modify the code in highway.h to alter the cars. Also check out tools.cpp to
change how measurements are taken, for instance lidar markers could be the (x,y) center of bounding boxes by scanning the PCD environment
and performing clustering. This is similar to what was done in Sensor Fusion Lidar Obstacle Detection.
This information is only accessible by people who are already enrolled in Sensor Fusion. If you are enrolled, see the project page in the classroom for instructions and the project rubric.