How to improve the measurement accuracy of road gradient is the key content of the research on the speed warning of commercial vehicles in mountainous roads. The large error of the measurement causes a significant effect of the vehicle speed threshold, which causes a risk to the vehicle's safety. Conventional measuring instruments such as accelerometers and gyroscopes generally have noise fluctuation interference or time accumulation error, resulting in large measurement errors. To solve this problem, the Kalman filter method is used to reduce the interference of unwanted signals, thereby improving the accuracy of the slope measurement. However, the Kalman filtering method is limited by the estimation error of various parameters, and the filtering effect is difficult to meet the project research requirements. In this paper, the acceleration of vehicle gravity, driving speed and acceleration of parallel slope are utilied as auxiliary measurement parameters to improve the measurement method. Based on the Kalman model, GA (genetic algorithm) and BP (Back Propagation) neural network are employed to carry out the innovation, covariance matrix and the last Kalman numerical value optimization, improve the filtering effect. Matlab simulation and real vehicle experiment are used to verify this improved algorithm, and the average absolute error can be within 0.2%, to meet the accuracy requirements of the slope required for the speed warning of commercial vehicles during mountainous roads. This confirms the accuracy and reliability of the method presented. In addition, the improved filtering method also has wide application in the fields of navigation, detection, target tracking and other fields.