Saturday 19 April 2025
As we navigate our daily lives, it’s easy to take for granted the small, yet significant, devices that make our commutes smoother and safer. E-scooters have become an increasingly popular mode of transportation in urban areas, offering a convenient alternative to traditional vehicles. However, their popularity has also raised concerns about safety, particularly when it comes to detecting obstacles on the road.
A recent study aimed to address this issue by developing a real-time obstacle detection system specifically designed for e-scooters. The team behind the project used a combination of cameras and sensors to identify potential hazards, such as potholes, manhole covers, and tree branches, and alert riders in time to avoid them.
The system utilizes an RGB camera and a depth camera, which work together to provide a detailed image of the road ahead. An inertial measurement unit (IMU) measures the linear vertical acceleration of the e-scooter, allowing the system to detect vibrations caused by uneven roads or obstacles.
Using this data, the system is able to identify six types of obstacles commonly encountered on e-scooter routes, including manhole covers, non-directional cracks, and potholes. The team trained a deep learning model using YOLO (You Only Look Once), a popular object detection algorithm, to recognize these obstacles in real-time.
The results are impressive: the system achieved an average precision of 0.827, indicating that it accurately detected over 82% of obstacles on the test route. Moreover, the system was able to estimate distances to detected objects with high accuracy, providing riders with valuable information to adjust their speed and trajectory accordingly.
The study’s findings have significant implications for e-scooter safety. By providing real-time obstacle detection, the system can help reduce the risk of accidents caused by unexpected road hazards. Additionally, the technology has potential applications beyond e-scooters, including autonomous vehicles and other mobility devices.
One of the key advantages of this system is its ability to adapt to different environments and conditions. The team tested the system in various lighting conditions, from bright sunlight to low light, and found that it performed well across all scenarios. This adaptability makes it an attractive solution for e-scooter manufacturers and riders alike.
As urban areas continue to evolve and e-scooters become increasingly popular, innovations like this obstacle detection system are crucial for ensuring the safety of riders.
Cite this article: “Revolutionizing E-Scooter Safety: Real-Time Obstacle Detection with Deep Learning”, The Science Archive, 2025.
E-Scooters, Obstacle Detection, Real-Time, Cameras, Sensors, Rgb, Depth Camera, Inertial Measurement Unit, Deep Learning Model, Yolo.
