Vehicle Speed Estimation for Articulated Heavy-Duty Vehicles

Abstract: Common trends in the vehicle industry are semiautonomous functions and autonomous solutions. This new type of functionality sets high requirements on the knowledge about the state of the vehicle. A precise vehicle speed is important for many functions, and one example is the positioning system which often is reliant on an accurate speed estimation. This thesis investigates how an IMU (Inertial Measurement Unit), consisting of a gyroscope and an accelerometer, can support the vehicle speed estimation from wheel speed sensors. The IMU was for this purpose mounted on a wheelloader. To investigate the speed estimation EKFs (Extended Kalman Filters) with different vehicle and sensor models are implemented. Furthermore all filters are extended to Kalman smoothers. First an analysis of the sensors was performed. The EKFs were then developed and verified using a simulation model developed by Volvo Construction Equipment. The filters were also implemented on the wheel loader and tested on data collected from real world scenarios.