September 1, 2019.
Traffic is not only a source of frustration but also a leading cause of death for people under 35 years of age. Recent research has focused on how driver assistance technology can be used to mitigate traffic fatalities and create more enjoyable commutes. In addition, self-driving vehicles can reduce fuel consumption the amount by 5% and increases the number of cars on the highway. To achieve this we need to research reliable sensors. This summer I research Rplidar A2 sensor which hopefully will be responsible for recording distance to the preceding car and helping prevent Insider Attacks or Misbehaviors of self-driving vehicles. My research focus on how well can the Rplidar A2 detect its surroundings. The Rplidar was tested in the lab inside multiply dimension boxes. I gathered the data produces by Rplidar using a Linux computer and wrote a python program to graph that data. I used two different filter formulas to reduce the noise of the graph. In conclusion, the Rplidar was able to detect the dimension of the box however more test is needed before it could be implemented in the vehicles.
Applied Statistics | Artificial Intelligence and Robotics | Programming Languages and Compilers
California Polytechnic State University (Cal Poly SLO)
The 2019 STEM Teacher and Researcher Program and this project have been made possible through support from Chevron (www.chevron.com), the National Science Foundation through the Robert Noyce Program under Grant #1836335 and 1340110, the California State University Office of the Chancellor, and California Polytechnic State University. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the funders.