Remote driving testbed with force feedback based on slip angle estimation

While automotive research plays a significant role nowadays, most of the experimental activity demands costly platforms and involves safety issues. Plenty of driving simulators were proposed to reduce the costs and guarantee safety. However, they still cannot reflect the physical world, resulting in subjective assessments in any aspect of the study. This paper introduces an affordable remote driving testbed based on small-scale car-like mobile platforms and a physical road. The driver in the remote driving station observes a real-time video taken from a front-facing camera installed in the car. For a realistic driving experience, we have developed a torque feedback mechanism based on the small-scale car motion to mimic the influence of the physical linkage between the front wheels and the steering wheel of a standard car. This mechanism demands knowledge of the car's side-slip angle that is not directly measured. Here, we introduce a supervised learning-based combined regression model (RidgeCV and Bootstrap aggregating decision tree) that estimates the side-slip angle for highly non-linear behavior.