Transforming Headlamp Levelling Compliance Testing Through AI-Powered Predictive Modelling

The process of validating headlamp levelling compliance as per regulatory standards involves physical testing with various vehicle loading conditions. This traditional method is labour-intensive, time-consuming, and consumes significant resources. There is a need for a predictive solution that can simulate and validate headlamp levelling tests virtually, thereby reducing dependency on physical trials. Headlamp levelling compliance is a critical regulatory requirement to ensure optimal visibility and safety under varying vehicle loading conditions. This paper presents an Artificial Intelligence and machine learning-based (AI / ML) solution to simulate headlamp levelling tests virtually/Digitally by using Historical Headlamp designs from past vehicles. By leveraging historical test data and developing regression models, the system predicts headlamp height and dipped beam height for both unladen and laden conditions. Key vehicle parameters such as tire load, overhang, deflection, and reflector angle are used as input features. The AI/ML-based approach not only accelerates compliance validation but also enables sensitivity analysis and scalability for other automotive testing scenarios through integration with virtual simulation environments, aiming to reduce no of prototypes and testing time, cost and time to market