This project is part of Udacity Sensor FUsion COurse. It implements an Unscented Kalman Filter to estimate the state of multiple cars on a highway using noisy lidar and radar measurements. The state prediction accuracy is determined by RMSE values that are lower that the tolerance outlined in the project.
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