# Machine-Learning-Based Rollover Risk Prediction for Autonomous Trucks: A Dynamic Stability Analysis
###### Abstract
This study analyzed multiple parameters affecting rollovers and evaluated the impact of various hyperparameters on prediction accuracy. It demonstrated that key machine learning parameters, including the number of hidden layers, training cycles, learning rate, and batch size, significantly influence prediction performance, further highlighting the efficiency of machine learning algorithms over traditional sensor-based methods. Additionally, [PERSON] et al. [10] introduced an innovative approach by developing a deep neural network (DNN) model designed for predicting vehicle dynamics during complex maneuvers, such as double lane changes. This model leverages data generated from multibody simulation techniques, with input variables including wheel torque and initial speed. The DNN effectively predicts critical vehicle state parameters, such as longitudinal and lateral distances, velocities, and yaw angles. Validation results demonstrate that the DNN predictions align closely with multibody simulation outcomes, highlighting its potential as a robust tool for real-time vehicle state estimation. [PERSON] [PERSON] et al. [11] proposed a novel physics-informed deep learning approach for real-time estimation of critical vehicle parameters, including cornering stiffness. By integrating physical modeling with deep learning techniques, this method significantly improves the accuracy and computational efficiency of parameter estimation, an essential aspect for vehicle stability control systems. The validity of this approach was demonstrated through experiments using a scaled vehicle model, showcasing its potential in enhancing dynamic stability applications. Despite these advancements, there remains a need to further contextualize rollover prediction within broader, globally relevant safety frameworks. [PERSON] et al. [12] emphasized a multivariable control strategy for autonomous vehicle stability using data-driven simulations, which aligns with this study's simulation-based approach. Additionally, [PERSON] et al. [13] presented a real-world evaluation methodology for vehicular dynamics using experimental datasets, suggesting pathways for model validation beyond simulation. The occurrence of rollover accidents, although less frequent than other types of vehicular incidents, presents significantly higher fatality rates. Traditional vehicle safety evaluation methods have predominantly focused on metrics such as the static stability factor (SSF), which evaluates a vehicle's resistance to rollover under static conditions [14]. While the SSF serves as a foundational measure, it does not fully capture the dynamic complexities introduced by real-time maneuvers, road geometry, or load variations. This study evaluated the dynamic stability of trucks concerning maximum safe slope angles through simulation. First, the relationship between the static stability factor (SSF) and the maximum safe slope angle was analyzed. The SSF is a critical metric for assessing vehicle stability and is expressed as follows [14]:  
\[SSF=\frac{T}{2h} \tag{1}\]  
where \(T\) represents the average track width of the front and rear axles, and \(h\) denotes the height of the vehicle's center of gravity. According to existing literature [14], SSF values typically range from 1.02 to 1.45 for passenger cars and light trucks (gross vehicle weight \(\leq\) 4536 kg), 0.65 to 1.14 for medium-duty commercial vehicles (4536-11,793 kg), and 0.51 to 0.7 for heavy-duty commercial vehicles (>11,793 kg). In fully loaded conditions, SSF values tend to be even lower. Next, the dynamic stability of trucks was evaluated based on SSF. To consider various dynamic scenarios, six test evaluation methods defined by NHTSA and SAE were employed: circle, double lane change (DLC), slowly increasing steer (SIS), J-turn, sine with dwell (SWD), and fishhook [15; 16; 17]. The simulations incorporated five types of freight vehicles to account for different vehicle parameters.