A study of distance sensing for bicycle collision warning system - Ong, Min Shun (Nanyang Technological University, 2021)
Amid the pandemic, cycling experienced increased popularity, which led to a spike in bicycle-related accidents. Thus, the government have improved cycling amenities and imposed different sets of speed limits on a bicycle, but it still did not prevent the rising rate of bicycle-related accidents. In addition, there was a rise in accidents whereby bicycles were crashing into stationary vehicles on the road. The objective of this study was to develop an algorithm for a front-mounted collision avoidance warning system for bicycles. The algorithm was developed in C++ language with Arduino IDE. Distance data collected from the light detection and ranging (LIDAR) sensor was used to evaluate possible collision risk using the time to collision (TTC) and relative speed principles. Electronic components provided audible and visual warnings to alert cyclists and pedestrians when a potential collision was detected. Furthermore, the algorithm also provides autonomous braking to alert the cyclist when a risk of collision is detected. Different cycling scenarios were simulated on two Arduino car prototypes to assess the performance of the proposed system and the algorithm. After which, results collected from the experiments were compared to the real-life bicycle experiment conducted by the author. With the experiments simulated by the Arduino cars, it is proven that the proposed algorithm in this study can prevent a collision by controlling the braking behaviour of the rear bicycle depending on the front cyclist’s actions.