Research on Prediction Algorithm of Vehicle Speed Based on Driving Intention Classification
Speed prediction of surrounding vehicles has received much attention because of its great potential to improve the vehicle's safety, fuel consumption, and efficiency. However, its driving prediction faces significant challenges due to the uncertainty of its driving intention, vehicle dynamics, and the interaction between the predicted object and its surrounding environment. First, the Fuzzy C mean (FCM) method is used to achieve automatic recognition of driving intentions by offline training using information related to vehicle acceleration; Second, the Gaussian process regression (GPR) method is used to predict future speeds under three different driving intentions using historical and cur rent vehicle speeds; Third, the three predicted speeds are fused according to the fuzzy classification results of driving intentions by iterative method for 1s rolling prediction; Finally, experimental validation is performed on a 6.6m long electric mobile robot. The results show that 90.33% of the prediction errors are less than 1m/s in the future 1s speed prediction, and the maximum speed prediction error is 2.45m/s.
2021 6th International Conference on Automation, Control and Robotics Engineering (CACRE)