A vehicle mass estimation apparatus for estimating the mass m of a vehicle based on rotation information of tires attached to the vehicle, comprising: a rotation speed information detection means for periodically detecting tire rotation speed information of the respective wheels of the vehicle; a rotation acceleration information calculation means for calculating tire rotation acceleration information a based on the rotation speed information obtained by the rotation speed information detection means; a driving force calculation means for calculating a driving force F of the vehicle based on axle shaft torque information of the vehicle; and a vehicle mass estimation means for estimating the mass m of the vehicle as a regression coefficient m when the rotation acceleration information a and the driving force information F are subjected to linear regression, characterized in that the vehicle mass estimation means calculates, with regard to an error between driving force information calculated based on the estimated mass m and the rotation acceleration information a and the driving force information F calculated by the driving force calculation means, a ratio between the dispersion of the error obtained one time before and an error at the current moment; and wherein the vehicle mass estimation means calculates an update adjustment parameter w by adding stabilization parameters α and β to thus obtained ratio, and uses the calculated update adjustment parameter w to calculate a Kalman gain K so that the mass is sequentially estimated so as to reduce an update width of the regression coefficient m when the ratio is higher, wherein the regression coefficient m is calculated based on general sequential update formula m k + 1 = m k + K k F k − m k ′ a k wherein k represents the kth data or the kth estimation value, and K is calculated based on formula K k = ∑ k − 1 − 1 a k a k ′ ∑ k − 1 − 1 a k + 1 W k R − 1 wherein ∑ represents the covariance matrix of the input a and R represents a unit matrix, and w is calculated based on formula W k = α + 0.5 β + F k − m ′ k a k 2 ξ k − 1 wherein ξ k = 1 + a k ′ ∑ k − 1 − 1 a k k − 1 ∑ i = 1 k F i − m i ′ a i 2 a rotation speed information detection means for periodically detecting tire rotation speed information of the respective wheels of the vehicle; a rotation acceleration information calculation means for calculating tire rotation acceleration information a based on the rotation speed information obtained by the rotation speed information detection means; a driving force calculation means for calculating a driving force F of the vehicle based on axle shaft torque information of the vehicle; and a vehicle mass estimation means for estimating the mass m of the vehicle as a regression coefficient m when the rotation acceleration information a and the driving force information F are subjected to linear regression, characterized in that the vehicle mass estimation means calculates, with regard to an error between driving force information calculated based on the estimated mass m and the rotation acceleration information a and the driving force information F calculated by the driving force calculation means, a ratio between the dispersion of the error obtained one time before and an error at the current moment; and wherein the vehicle mass estimation means calculates an update adjustment parameter w by adding stabilization parameters α and β to thus obtained ratio, and uses the calculated update adjustment parameter w to calculate a Kalman gain K so that the mass is sequentially estimated so as to reduce an update width of the regression coefficient m when the ratio is higher, wherein the regression coefficient m is calculated based on general sequential update formula m k + 1 = m k + K k F k − m k ′ a k wherein k represents the kth data or the kth estimation value, and K is calculated based on formula K k = ∑ k − 1 − 1 a k a k ′ ∑ k − 1 − 1 a k + 1 W k R − 1 wherein ∑ represents the covariance matrix of the input a and R represents a unit matrix, and w is calculated based on formula W k = α + 0.5 β + F k − m ′ k a k 2 ξ k − 1 wherein ξ k = 1 + a k ′ ∑ k − 1 − 1 a k k − 1 ∑ i = 1 k F i − m i ′ a i 2