paulyehtw/Lidar-and-Radar-sensor-fusion-with-Extended-Kalman-Filter

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paulyehtw/Lidar-and-Radar-sensor-fusion-with-Extended-Kalman-Filter

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Fusing Lidar and Radar data with Extended Kalman Filter (EKF)

Lidar-and-Radar-sensor-fusion-with-Extended-Kalman-Filter

Most autonomous driving cars are equipped with Lidar and Radar. However the outputs of those two are different, the output of Lidar is positions of objects in cartesian coordinates whereas Radar gives out the position and velocity of the objects in polar coordinates. The Extended Kalman Filter is utilized as it can fuse non-linear data, in this case the data from cartesian coordinates and polar coordinates. To estimate the non-linear measurement, the Jacobian matrix is introduced.

As the results below, the EKF functions fuses linear Lidar with non-linear Radar data.

The code is written from scratch.

Data source is from Udacity course Self-Driving Car

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A quasi Hybrid A* method is introduced for motion planning of autonomous driving car, based on MPC and Bezier spline

Jupyter Notebook

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Implement an MPC approach to coordinate automated vehicles with fixed priorities

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With Radar data, use Fast Fourier Transform(FFT) and 2D CA-CFAR to derive range and velocity of the objects

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Assignments for Machine Learning course on Coursera from Stanford University

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Jupyter Notebook