Software Development for Controlling Glare-Free Adaptive Driving Beam for Motorcycles

  • Teo Cagil Oral

Student thesis: Master's Thesis

Abstract

This thesis focuses on the design and development of control software for an Adaptive Driving Beam (ADB) system utilizing matrix LED headlights, aimed at enhancing night-time motorcycle safety. The primary objective is to interpret traffic information detected via camera inputs and telemetry data from the motorcycle’s sensors to dynamically adjust the headlight beam pattern in real-time. This adjustment ensures optimal visibility for the rider while minimizing glare for oncoming traffic, thereby reducing the risk of night-time driving accidents and improving overall road safety. The developed software incorporates key features such as glare-free high-beam functionality through object masking, which selectively dims specific pixels to prevent blinding oncoming drivers while maintaining sufficient illumination for the rider. Additionally, it enhances the low beam during braking and cornering by selectively activating relevant pixels of the high beam, thus maximizing road visibility under varying driving conditions. The development process follows a model-based approach using Simulink and MATLAB for software function development, complemented by real-time testing protocols to ensure synchronized and consistent communication between the light control unit and sensor components. The system’s performance is evaluated through measurable metrics, with a particular focus on the effectiveness of illumination coverage under dynamic conditions. The results demonstrate that the ADB system effectively enhances road illumination and safety by dynamically adjusting to real-time conditions. However, challenges related to hardware limitations and execution delays were identified, highlighting areas for potential improvement. This research contributes to the field of automotive lighting systems, offering a foundation for the development of more intelligent and responsive motorcycle lighting solutions in the future
Date of Award2024
Original languageEnglish
SupervisorGerald Steinmaurer (Supervisor) & Harald Kirchsteiger (Supervisor)

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