# Estimation of Intelligent Commercial Vehicle Sideslip Angle Based on Steering Torque
## 1 Introduction  
Vehicle safety control is a hugely popular research topic in the field of autonomous driving, mainly including active yaw control (AYC) and electronic stability control (ESC) [1, 2]. The yaw rate can usually be measured directly by onboard sensors, but the sideslip angle requires expensive sensors to obtain, which is too costly for commercial applications. Consequently, indirect estimation methods are commonly used in commercial applications to obtain more accurate vehicle sideslip angles through software estimation based on a number of existing sensors in the vehicle, including wheel speed sensors (WSSs), inertial measurement units (IMUs), and global navigation satellite systems (GNSSs) [3]. However, the accuracy and stability of the currently used GNSSs are difficult to guarantee, especially in highway tunnels and urban street canyons, which causes great security risks to the active safety system of autonomous vehicles and connected automated vehicles. Therefore, reliable and accurate vehicle sideslip angle estimation has been the focus of intelligent driving research. The extensive methods for sideslip angle estimation mainly include vehicle-model-based methods (Kalman filter, sliding mode observer, Luenberger observer) and methods using a GNSS or integrated navigation system (INS) [4, 5, 6, 7]. The vehicle-model-based (VM-based) sideslip angle estimation method is designed by using onboard sensors and a dynamic vehicle model. The performance of this method is largely affected by the accuracy of the vehicle dynamics model, including the road condition, the vehicle degree of freedom, and the nonlinear characteristics of tires under extreme conditions [8, 9, 10]. A novel road classification method using measured signals of vehicle systems was proposed to precisely estimate road information [11]. In [12, 13], linear vehicle models of two DOF, three DOF, and five DOF were established. Through the study of tire nonlinear region characteristics, the sideslip angle was estimated based onthe extended Kalman filter (EKF). For the nonlinear characteristics of tires, a method to estimate the vehicle sideslip angle using tire force sensors was proposed and evaluated with field tests [9]. Some researchers tried only to use the acceleration and yaw rate from IMU, which is not affected by precise vehicle dynamic parameters. However, the performance of this method is heavily dependent on the bias of the IMU, as integrating acceleration and yaw rate over a long period of time will result in large estimation errors. Therefore, a VDM-VKM fusion method is proposed in [14], which synthesizes the advantages of both VDM-based and VKM-based methods. With the development of autonomous driving technology, sideslip angle and attitude are crucial for autonomous vehicles and connected automated vehicles. A variety of sensors are used on vehicles to increase the ability to sense external information, usually including magnetometers, cameras, radars, and GNSSs. These new technologies bring promising solutions for sideslip angle estimation and fusion algorithms [15; 16]. However, the performance of these sensors depends heavily on the environment. To compensate for the low update rate and delay of the GNSS receiver due to low cost, the IMU and GNSS were fused to estimate the vehicle sideslip angle using asynchronous measurement updates and measurement offset techniques [17]. In [18], an IMU-based automated vehicle sideslip angle and attitude estimation method is proposed, which is based on low sampling rate GNSS speed and position measurement using parallel adaptive Kalman filters. This method can estimate sideslip angle and attitude at the same time and is robust to vehicle parameters and road friction even when the vehicle enters a critical maneuver. Based on the velocity error measurements between the reduced Inertial Navigation System (R-INS) and GNSS, a velocity-based Kalman filter algorithm is proposed to estimate the velocity errors, attitude errors, and gyro bias errors of the R-INS.