# Machine-Learning-Based Rollover Risk Prediction for Autonomous Trucks: A Dynamic Stability Analysis
## 2 Methods  
### Maximum Safe Slope Angle Test  
To represent diverse commercial freight vehicles, five truck models with varying axle configurations were selected. These are  
* _540S: Five-axle tractor truck;_
* _Arocs:_ Two-axle rigid truck;
* _New Power Truck (NPT):_ Three-axle dump truck;
* _Prina:_ Four-axle cargo truck;
* _Xcient_: Four-axle cargo truck.  
Vehicle specifications were obtained from manufacturer technical datasheets and TruckSim simulation model parameters. Table 1 summarizes the key specifications of the five heavy-duty vehicles selected for the simulation study. The vehicle fleet comprisedone 5-axle tractor truck (540S), two 4-axle trucks (Prima and Xcient), one 3-axle dump truck (New Power Truck, NPT), and one 2-axle rigid truck (Arocs), representing a diverse range of axle configurations and structural designs commonly used in commercial freight transport. This diversity was intended to reflect varying stability characteristics across different vehicle types. The center of gravity (CG) height for each vehicle was estimated based on the respective track width and the static stability factor (SSF), applying the relationship defined in Equation (1). This approach allows for a standardized comparison across vehicles while maintaining fidelity to the physical dynamics influencing rollover behavior. To determine the maximum safe slope angle for each vehicle, a tilt table test was conducted using the TruckSim simulation environment.  
As illustrated in Figure 1, each virtual vehicle model was positioned on a dynamic tilt platform that incrementally increased in inclination until the onset of rollover was detected. This test method is widely accepted for evaluating a vehicle's rollover threshold under quasi-static conditions and provides a controlled setting to assess stability margins. The output from these simulations serves as a critical baseline for evaluating the effects of vehicle design and configuration on rollover resistance.  
The surface condition was set to a standard road surface, and the platform's friction coefficient was predefined. The platform width was fixed at 4 m, with the vehicle centrally positioned. The rotation speed of the tilt platform was maintained at 0.1 \({}^{\circ}\)/s.  
Software and Hardware Setup  
TruckSim 2021 for vehicle dynamic simulation;  
MATLAB R2023a with Simulink for data processing;  
TensorFlow 2.11 for machine learning model training;  
Hardware: Intel i9-12900K CPU, NVIDIA RTX 3080 GPU, 64 GB RAM;  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Model Name** & **540s** & **Arocs** & **NPT** & **Prima** & **Xcient** \\ \hline Number of axles & 5 & 2 & 3 & 4 & 4 \\ \hline Overall length (mm) & 12,900 & 11,600 & 7595 & 12,820 & 11,575 \\ \hline Overall width (mm) & 2495 & 2495 & 2495 & 2490 & 2490 \\ \hline Overall height (mm) & 3995 & 3200 & 3130 & 3305 & 3345 \\ \hline Wheelbase (mm) & 8850 & 6600 & 4590 & 7600 & 7430 \\ \hline Front track width (mm) & 2065 & 2100 & 2050 & 2165 & 2080 \\ \hline Rear track width (mm) & 1830 & 1850 & 1850 & 1850 & 1845 \\ \hline Curb weight (kg) & 12,155 & 7015 & 11,100 & 13,270 & 12,800 \\ \hline \end{tabular}
\end{table}
Table 1: The specifications of vehicles from manufacturer data sheets and _TruckSim_ simulation model parameters.  
Figure 1: Maximum tilt test using _TruckSim_.  
* Operating System: Windows 11 Pro.