# Machine-Learning-Based Rollover Risk Prediction for Autonomous Trucks: A Dynamic Stability Analysis
###### Abstract  
In response to the 2023 mandate requiring electronic stability control (ESC) for trucks in South Korea, domestic manufacturers have called for a relaxation of the maximum safe slope angle to reduce production costs. However, limited research exists on the quantitative relationship between ESC implementation and vehicle rollover stability under relaxed safety standards. This study addresses this gap by conducting dynamic simulations of standardized rollover tests to evaluate the static stability factor (SSF) and by developing a machine-learning-based model for predicting rollover risk. The model incorporates planned path curvature and driving speed to compute lateral acceleration, which serves as a key input for predicting the lateral load transfer ratio (LTR), a critical indicator of vehicle stability. Among several models tested, the recurrent neural network (RNN) achieved the highest accuracy in LTR prediction. The results highlight the effectiveness of integrating data-driven models into dynamic stability assessment frameworks, offering practical insights for optimizing route planning and speed control--particularly in autonomous freight vehicle applications. self-driving trucks; rollover prediction; lateral-load transfer ratio; machine learning; recurrent neural network +
Footnote †: 2020: _Article_  
rollover stability of trucks improves due to ESC implementation, domestic truck manufacturers have increasingly called for a relaxation of the maximum safe slope angle to reduce manufacturing and operational costs. Ensuring the dynamic stability of heavy-duty vehicles, particularly autonomous freight trucks, is a growing concern globally. Modern safety systems such as anti-lock braking systems (ABS) and ESC have shown notable effectiveness in mitigating rollover risks [3; 4]. Nevertheless, these systems operate reactively and depend heavily on predefined threshold responses. Consequently, there is a growing need for predictive systems that can proactively assess stability based on vehicle state and environmental inputs. To mitigate the human and material losses caused by truck rollovers and enhance road operation efficiency, technology is needed to actively control vehicle speed to prevent accidents and optimize driving speed for improved freight transport efficiency. Traditional vehicle safety technologies, such as ABS and ESC, have primarily focused on real-time braking control; however, new technologies capable of proactively preventing hazards in rapidly changing situations are becoming increasingly necessary [5; 6]. Key factors contributing to vehicle rollover in dynamic conditions include lateral acceleration, the position and height of the center of gravity, suspension performance, and roll resistance. Among these, lateral acceleration is considered the most influential factor [7]. While lateral acceleration can be derived from road curvature and vehicle speed, it alone is insufficient to determine vehicle stability. Other factors, such as the center of gravity height and suspension characteristics, vary due to vehicle aging, maintenance conditions, and real-time changes in cargo weight and position during driving. Due to these uncertainties, recent advancements in machine-learning-based autonomous driving technologies have emerged as a promising solution. Recent research has explored the integration of machine learning techniques for predicting dynamic vehicle states such as roll angle and lateral load transfer ratio (LTR). For example, [PERSON] et al. [8] developed a machine-learning-based model to estimate the roll angle and LTR using a comprehensive set of vehicle kinematic variables such as vehicle speed, longitudinal acceleration, lateral acceleration, vertical acceleration, roll rate, yaw rate, pitch rate, and steering angle into a machine learning model to estimate roll angle and LTR for both the front and rear axles. Similarly, [PERSON] et al. [9] developed an improved time-to-rollover (TTR) prediction model using neural networks to prevent rollovers in new energy vehicles.