An eclectic design and adas-oriented realtime simulation of AEB with adaptive brake algorithm under Euro NCAP protocol

This thesis presents the development and evaluation of an adaptive braking algorithm integrated into an Autonomous Emergency Braking system, designed in accordance with Euro NCAP scenario protocols. The primary objective is to improve collision avoidance performance and driving comfort compared to classical fixed deceleration models. A total of seven critical driving scenarios including car to car, pedestrian, and bicyclist interactions were modeled using MATLAB/Simulink. The simulations were conducted under three different road surface conditions: dry, wet, and icy. While the classical braking model applies pre-defined deceleration values once the AEB system is triggered, the proposed adaptive model continuously calculates and applies optimal braking force in real time, based on factors such as time to collision, ego vehicle speed, relative distance, target speed, and road friction coefficient. The results show that the adaptive braking system achieves full collision avoidance in all tested scenarios and surfaces. It demonstrates a clear advantage in avoiding unnecessary braking, maintaining passenger comfort, and minimizing stress on the vehicle's mechanical components. The adaptive model also provides better responsiveness in complex or low-traction environments compared to the fixed model. This research highlights the importance of real-time, environment-aware braking strategies for next-generation Advanced Driver Assistance Systems, and suggests that data-driven AI-based decision models could further enhance the flexibility and reliability of emergency braking systems.