# Machine-Learning-Based Rollover Risk Prediction for Autonomous Trucks: A Dynamic Stability Analysis
## 5 Conclusions  
### Effects of the Maximum Safe Roll Angle on the Dynamic Stability of Heavy Trucks  
This study conducted a series of simulations to investigate the influence of the maximum safe roll angle on the dynamic stability of heavy trucks, with a particular focus on the effectiveness of electronic stability control (ESC). Key variables included vehicle type, static stability factor (SSF), ESC presence, and internationally standardized test procedures. The results showed that the maximum safe roll angle increased nearly linearly with SSF. Nevertheless, at identical SSF values, inter-vehicle variability in roll angle was observed to reach up to 7 degrees, indicating that factors beyond SSF--such as vehicle-specific characteristics--play a non-negligible role in rollover dynamics. Among the six global test procedures analyzed, the J-turn, SWD, and fishhook tests consistently induced rollover in the absence of ESC, limiting their utility for quantitatively assessing LTR (load transfer ratio) reduction. In contrast, the circle, DLC, and SIS tests provided measurable reductions in LTR due to ESC installation, with the following decrease rates: 8.8-45.3% (circle), 43.4-55.5% (DLC), and 22.0-50.9% (SIS). The DLC test demonstrated the most pronounced ESC effect. Additionally, for the same LTR values, the maximum safe roll angle increased by 8-17 degrees when ESC was installed in the circle, DLC, and SIS tests. As a result, to effectively evaluate the safety range of the maximum safe roll angle with ESC installation, a less stringent version of the J-turn, SWD, and fishhook tests may be required compared to existing regulations. Furthermore, the results indicate that the maximum safe roll angle varies even for the same SSF depending on the truck's specifications. Thus, a sensitivity analysis is necessary to determine which specific vehicle parameters influence the maximum safe roll angle. In this study, factors such as suspension characteristics, road surface conditions, and tire friction coefficients were held constant. To further explore methods for improving the maximum safe roll angle, additional full-scale vehicle experiments will be required. Simulation-Based Driving Data Generation and Performance Verification of a Machine Learning Model for Predicting Truck Rollover Stability  
The second phase of this study focused on the prediction of heavy truck rollover stability through simulation-based data generation and machine learning. Recurrent neural network (RNN) models were employed to predict the LTR index using input variables derived from simulated driving data. The major findings are summarized as follows:  
* The LTR index during truck operation can be reliably predicted based on the intended driving path and vehicle speed. * Transforming raw input variables--specifically vehicle speed and path curvature--into lateral acceleration significantly enhanced the prediction accuracy of the machine learning model. * The proposed LTR prediction model offers the potential to compute optimal driving speeds in real time, thereby improving rollover risk management during operation. Practical deployment considerations are as follows:
* The trained RNN models offer real-time rollover risk estimation capabilities, potentially enabling dynamic route planning and speed control for autonomous trucks. * Latency issues are minimal due to lightweight RNN architectures, but further optimization is required for deployment on embedded platforms. Interpretability remains a challenge; future work must focus on explainable AI techniques to ensure transparency and regulatory acceptance in safety-critical systems. * Sensor noise and real-world environmental variability must be accounted for during system integration. A notable limitation of the current work lies in the relatively short simulation sequences used for model training, which resulted in varying prediction accuracies across different driving scenarios. To improve model robustness, future studies should incorporate extended driving datasets.