Finding a safety feature algorithm for electric-scooter by detecting the closest obstacles

Recently, the e-scooter is more popular and trendy, therefore improvement of the safety feature of e-scooter, a moving vehicle that has a maximum speed about 40 km/hour is very important. Especially, Toronto has just approved these kind ofvehicles that can ride on the bike lanes under the by-law. In this research, an algorithm is proposed to use a single camera for detecting the obstacles using the latest deep learning-based models. This new approach omits the complex alignment and expensive equipment. The deep learning models are employed to judge whether the safe distance exists between the e-scooter and the obstacle. If it detects the obstacles, it reports the closest obstacle by comparing depth information of all obstacles and a warning will be issued based on the obstacle detection. If no obstacle is detected, a message informing about the safe riding situation is announced. The proposed algorithm has the accuracy about 70%. In the future work, by including additional advanced deep learning models for depth estimation and distance calculation, a better performance is expected