Mechanics of the tire terrain interaction

Abstract

The evolution of advanced driver assistance systems (ADAS) marks a significant shift in automotive safety. The field has progressed from passive safety mechanisms, such as seatbelts and airbags, which protect occupants during accidents but only activate after a collision, to sophisticated active and cooperative safety systems. Active safety systems aim to prevent accidents altogether using technologies such as sensors (which detect environmental conditions), actuators (devices that physically respond to sensor data), and control algorithms (software that processes data and determines actions), to assist the driver in real time. The latest advancements introduce cooperative safety systems that leverage vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, in which vehicles share information about their speed and position with one another and with traffic signals or road sensors. This boosts situational awareness and enables coordinated responses to potential hazards. At the core of this technological transformation is the integration of nonlinear control techniques and Sensor Fusion architectures. Both are critical for optimising vehicle dynamics. Nonlinear control methods, such as Sliding Mode Control (SMC), provide robust performance even in the presence of model uncertainties and varying road conditions. Sensor Fusion architectures combine data from multiple sources, such as cameras, radar, lidar, and inertial measurement units (IMUs). This process builds a comprehensive understanding of the vehicle’s environment and internal state. A specific focus of this work is the implementation of active Torque Vectoring using Sliding Mode Control algorithms. Torque Vectoring dynamically distributes torque between wheels. This function is essential for enhancing vehicle stability and agility, especially under challenging driving conditions. The choice of SMC is motivated by its strong robustness. It can handle uncertainties in the vehicle model and variations in the coefficient of adhesion across different road surfaces. The proposed methodology combines rigorous mathematical analysis with empirical validation. Eigenvalue analysis is used in theory to assess system stability and performance. Experimental validation uses datasets from inertial measurement units (IMUs). This dual approach ensures the results are sound in theory and reliable in practice. The study’s results use a Digital Twin framework that replicates real-world vehicle behaviour in a virtual environment. The Digital Twin enables extensive testing under boundary conditions. These are conditions that push the vehicle to the limits of stability and control. Findings show that the integrated SMC-based Torque Vectoring system consistently ensures convergence of vehicle states to the desired slip surface. It maintains high levels of control performance despite environmental disturbances. In summary, integrating nonlinear control and sensor fusion into advanced ADAS architectures is a key advancement in vehicle safety. The proven robustness of SMC-based active Torque Vectoring is supported by both mathematical and experimental analysis. This work paves the way for next-generation cooperative safety systems that are adaptive, reliable, and resilient in real-world conditions.