## 1 Introduction  
In modern society, information on vehicle weight plays a crucial role in various fields, such as traffic management, road/vehicle safety, and logistics. The weight of a vehicle influences its acceleration, braking, and handling performance. Lighter vehicles accelerate faster, have shorter stopping distances, and are much easier to handle, while heavier vehicles are harder to control and manage. Accurately estimating the vehicle's mass in real time is very advantageous for understanding the vehicle's dynamic characteristics and response, improving dynamic performance, and providing drivers with a safer and more efficient driving environment using an active safety system often requiring the accurate mass of the vehicle. However, due to the nonlinearity of the system and complicated driving patterns, it is difficult to precisely estimate mass using existing sensor values every time. Consequently, numerous studies on vehicle mass estimation have been conducted to date. Firstly, many studies have proposed mass estimation strategies using model-based approaches [1; 2; 3; 4; 5]. These methods focus on constructing a dynamic model of the vehicle and then estimating its mass based on the proposed model. Also, Ref. [6] used sensors mounted on the suspension and vertical accelerometers to estimate mass, but the sensor cost was impractically high. Refs. [7, 8] developed algorithms to estimate mass using GNSS sensors and IMU sensor data, while Ref. [9] proposed a method to estimate mass based on initial values obtained from rear suspension displacement sensors and subsequent longitudinal and lateral movements. Ref. [10] estimated the mass using the reconfigured longitudinal vehicle model and a recursive least square algorithm and introduced a bias reduction strategy to guarantee high accuracy even at low sampling speeds. [PERSON]. [11] proposed an event-based approach to integrate sensor data during events and estimate mass in real time. However, this method requires additional sensors, which increases costs. [PERSON]. [1, 2, 12, 13] employed Kalman filters for noise elimination and real-time estimation. [PERSON]. [1] used the Strong Tracking Extended Kalman Filter (ST-EKF) to estimate mass based on the vehicle's longitudinal speed and yaw rate. [PERSON]. [2] constructed a longitudinal dynamic model and used a steady-state Kalman filter (KF) using a theoretical model. [PERSON]. [12] used an adaptive extended Kalman filter (AEKF) to simultaneously estimate vehicle mass and road gradient using suspension system data, while [PERSON]. [13] employed the Unscented Kalman Filter (UKF) to estimate the vehicle's pitch angle and, subsequently, the road gradient, and then used the dynamic model to estimate mass. To improve accuracy and convergence, RLS-based methods have also been used, notably by Refs. [5, 14, 15, 16, 17]. [PERSON]. [14] used the RLS method with a forgetting factor to simultaneously estimate vehicle mass and time-varying road gradients. Similarly, [PERSON]. [5] proposed an RLS method to estimate mass by dividing acceleration data into specific intervals and estimating mass within each interval. [PERSON]. [5, 16, 17] developed longitudinal dynamics and optimized the RLS method using fuzzy controllers to control the forgetting factor. Additionally, dual filter methods were proposed in [6, 11, 18, 19]. [PERSON]. [6] proposed a sensor fusion method using dual Kalman filters and RLS to estimate road irregularities and vehicle mass under both static and dynamic conditions, enhancing accuracy by updating model parameters with RLS through state estimation by Kalman filters. Ref. [11] considered the effects of braking and turning, using EKF to estimate road gradient and RLS to estimate vehicle mass based on the estimated gradient. Ref. [18] used EKF and RLS to estimate both vehicle mass and road gradient during actual driving, considering braking and turning directions, while [19] applied linear KF and RLS to estimate road gradient and mass. However, these methods, Refs.