Recognition Model of Sideslip of Surrounding Vehicles Based on Perception Information of Driverless Vehicle

Abstract

At present, many vehicle sideslip driving status estimation approaches based on the inner information of the sideslip vehicle have been studied. However, the method of identifying the sideslip in surrounding vehicles is rarely developed. The surrounding severe sideslip vehicle threatens the safety of driverless vehicles if the driverless vehicle cannot detect the surrounding sideslip vehicle. Therefore, this study proposes a sideslip recognition model that uses the perception information of driverless vehicles to assess the sideslip driving status of the surrounding vehicles. Firstly, the severe sideslip is defined, which may influence the safety of driverless vehicles, and the sideslip recognition problem is described. Secondly, the severe sideslip is divided into two categories according to different sideslip trajectories. The two types of sideslip progress are analyzed, and the sideslip features of the two types of serious sideslip are extracted. A sideslip recognition model is established using a logical rule method based on the sideslip features. Finally, some simulation experiments are designed to verify the proposed sideslip recognition model. The simulation results show that the true-positive rate and the false-positive rates are 100% and 6.8%, respectively, which demonstrates that the proposed sideslip recognition model has a good performance.