Predictive model for road interference

This work presents the development of a road interference prediction model designed for early-stage testing of vehicle dynamics under varying conditions. The project aligns with Toyota's commitment to innovation, leveraging predictive testing methodologies that reduce the reliance on physical prototypes. This model focuses on simulating vehicle behaviour on a straight track, where specific conditions such as velocity range and obstacle interaction are meticulously controlled. The underlying dynamics of low-velocity behaviour are examined, emphasizing the importance of accurate vehicle response during low-speed maneuvers. Additionally, contact recovery mechanisms are explored to enhance the model's reliability in predicting interactions between the vehicle and obstacles. A pilot trial validated the model's effectiveness in assessing vehicle performance, reinforcing its potential for integration into Toyota's existing testing frameworks. This thesis concludes with recommendations for future research directions, including enhancements to the predictive capabilities and broader applications of the model across different vehicle types.