Computer Vision Aided Methodology for Steering Position Detection

Steering angle is a critical parameter in vehicle dynamics, traditionally measured using Steering Angle Sensors (SAS). However, external sensors used for development testing often pose challenges due to their bulk, installation complexity, and additional costs. With the upcoming regulatory requirements related to driver drowsiness detection, vehicles will incorporate cameras in near future. This study explores an innovative approach to repurpose the same camera for steering angle measurement, eliminating the need for dedicated sensors and reducing costs significantly. The proposed method utilizes colored tapes or visible markers affixed to the steering wheel, with a strategically placed camera capturing steering movement. Depending on requirements, various types of cameras (including thermal) can be deployed at different positions (front, back, or side). The vehicle's ECU employs an advanced object detection algorithm to track the movement of these markers, subsequently calculating the steering angle. The algorithm accounts for key factors such as center of the steering circle, wheel radius, movement variations, and skew corrections to compensate for differences arising from camera positioning. This approach not only simplifies hardware integration but also results in substantial cost savings by eliminating the need for dedicated Steering Angle Sensors and external sensors for testing. Experimental validation demonstrates high accuracy in steering angle estimation, making it a viable alternative to conventional sensor-based methods. The concept has been successfully tested, marking a significant advancement in vehicle dynamics monitoring with minimal hardware modifications.