# Vehicle Rollover Warning and Control Based on Attitude Detection and Fuzzy PID
## 1 Introduction  
A rollover while a vehicle is in motion not only poses a threat to the driver but is also prone to causing significant property damage and may even cause additional accidents. Among various traffic accidents, car rollover accidents result in a mortality rate of 33%, with over 90% of non-collision accidents being due to car rollovers [1]. Therefore, it is crucial to provide timely rollover warnings for moving cars and to use active control techniques and other means to avoid rollover accidents, and subsequently improve the driving stability of cars. The driving stability and safety of forest fire trucks in forest terrain are not only subjected to highly nonlinear vehicle dynamics constraints but also affected by complex terrain elements, such as inclinations in the road and bumpy obstacles, which significantly increase the probability of vehicle rollover. Generally, vehicle rollovers are divided into trip- and non-trip-type rollovers. A trip-type rollover is caused by the vehicle sliding sideways because of obstacles, whereas the non-trip-type rollover is caused by the vehicle driving in a curve. The unpredictability of trip-type rollovers makes it difficult to control them effectively and actively; therefore, researchers have focused on non-trip-type rollovers of vehicles [2; 3; 4; 5]. The basis of studying vehicle driving stability is to establish the dynamic model of the vehicle. The form of the model and the factors considered in modeling should ensure the accuracy of the theoretical established model while meeting the actual driving posture of the vehicle. Analyzing the characteristics of the vehicle and further establishing the vehicle rollover dynamics model of different vehicles, external excitations, etc., is important for vehicle rollover warning and rollover control. Researchers have established respective dynamics models according to their needs, and based on their complexity, there are rolloverplane models, longitudinal-lateral-vertical three-degrees-of-freedom (3-DOF) dynamics models, and multi-degrees-of-freedom models. In studying the anti-rollover of tractors, [PERSON] et al. [6] established a 3-DOF vehicle dynamics model that includes automatic steering. [PERSON] [7] and colleagues established a 3-DOF linear dynamics model capable of real-time changes. [PERSON] [8] established an 8-DOF vehicle dynamics model for the anti-rollover of crawler vehicles. [PERSON] et al. [9] developed a 4-DOF vehicle dynamics model for the uncertainty of the driving environment of driverless cars. [PERSON] et al. [10] established a multi-degrees-of-freedom vehicle dynamics model, including a tire model based on uneven roads. Moreover, [PERSON] [11] established a random wandering model to estimate the attitude of the vehicle, such as longitudinal velocity, lateral velocity, and transverse angular velocity, and avoided complex tire forces. As the degrees of freedom increase, the dynamics model becomes more complex and the error increases. Therefore, this study combined the study of driving stability of forest fire truck operations to establish a 3-DOF dynamics model of transverse pendulum motion, lateral motion, and longitudinal motion. Active anti-rollover techniques and rollover warnings are two of the widely researched current topics. Rollover warning refers to an early warning system that can determine whether the vehicle is close to or has reached the critical situation of rollover according to the current state of the vehicle and provide a timely warning. It is conducive to the driver to take emergency measures and the operation of the anti-rollover control system, which can prevent rollover accidents. Researchers have proposed early warning algorithms for rollover warning in combination with their own research objects. [PERSON] [12] and colleagues combined the secondary predicted zero moment point position of a vehicle to realize vehicle rollover warning.