Vision-based approach for estimating lateral dynamics of Powered Two-Wheeled Vehicles

A novel approach based on a mounted monocular camera and an IMU to estimate the lateral dynamics (sideslip angle and lateral velocity) of Powered Two-Wheeled Vehicles (P2WV) is proposed in this paper. This approach is divided into two parts, the estimation of the derivative of the lateral offset and the relative heading. For the first, a method based on inverse perspective mapping to detect the lane markers and fit them into a second-degree polynomial that gives the lateral offset, then the derivative is estimated from the latter. For the relative heading, a method based on detecting the vanishing point is used to estimate it. Finally, these two main parameters are used to estimate the sideslip angle and the lateral velocity. The proposed method is tested on a popular motorcycle simulator software (BikeSim) throughout several scenarios on straight and curved roads with high and low speeds while executing a Double Lane Change (DLC) maneuver. The proposed method shows promising results in terms of error and real-time execution.