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Unscented Kalman Filter

Sensor Fusion UKF Highway Project

The goal of this project to implement an Unscented Kalman Filter to estimate the state of multiple cars on a highway using noisy lidar and radar measurements. Success of the project can be defined by RMSE values that are lower that the tolerance: 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.

Build Instruction

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

Notes

  • 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 tracking is only along the X/Y axis.


Other Important Dependencies

Basic Build Instructions

  1. Clone this repo.
  2. Make a build directory: mkdir build && cd build
  3. Compile: cmake .. && make
  4. Run it: ./ukf_highway

Code Style (for contributors)

Please stick to Google's C++ style guide as much as possible.

Generating Additional Data (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.