<jupyter_start><jupyter_text>2. Intro to Extended Kalman Filter Project  2.1 - Project IntroductionNow that you have learned how the extended Kalman filter works, you are going to implement the extended Kalman filter in C++. We are providing `simulated lidar and radar measurements detecting a bicycle that travels around your vehicle`. You will use a Kalman filter, lidar measurements and radar measurements to track the bicycle's position and velocity.The first step is to `download the simulator`, which contains all the projects for Self-Driving Car Nanodegree. More detailed instruction about setting up the simulator with `uWebSocketIO` can be found at the end of this section.`Lidar` measurements are `red circles`,` radar` measurements are `blue circles` with an arrow pointing in the direction of the observed angle, and `estimation markers` are `green triangles`. The video below shows what the simulator looks like when a c++ script is using its Kalman filter to track the object. The simulator provides the script the measured data (either lidar or radar), and the script feeds back the measured estimation marker, and RMSE values from its Kalman filter. https://youtu.be/bb2IPCmJGwM  2.2 - Example of Tracking with Lidar<jupyter_code>HTML("""
<video width="640" height="360" controls>
  <source src='_Videos/3_Data_collected.mp4'>
</video>
""")<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.06272530641672674, 'fraction_numerical': 0.010093727469358327, 'mean_word_length': 5.36697247706422, 'pattern_counts': {'":': 0, '<': 8, '<?xml version=': 0, '>': 8, 'https://': 1, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '137232', 'license': 'MIT', 'n_tokens_mistral': 369, 'n_tokens_neox': 347, 'n_words': 191, 'path': 'L25 Project 5 Notes.ipynb', 'repo_name': 'wuxiankof/CarND-Extended-Kalman-Filter-Project', 'size': '1387'}