High-performance intelligent and robust emergency braking mechanism for autonomous vehicles

• Development of an integrated traffic light-aware autonomous braking system capable of dynamically adjusting braking actions based on-line implementation signal interpretation. • Design of a fuzzy logic-based decision-making algorithm that integrates traffic light status and vehicle-to-signal distance into the braking logic. • Implementation and comparison of a high-order robust control strategy for the braking pedal mechanism, designed to handle unmodelled dynamics, actuator nonlinearities, and external disturbances. Ensuring reliable and safe braking in autonomous vehicles is particularly challenging in urban environments, where dynamic signal processing, such as traffic lights, requires rapid and accurate decision-making. This study introduces an advanced braking system that employs robust control techniques specifically designed for emergency response involving traffic signal interactions. The system integrates a fuzzy-logic-based decision-making algorithm that prioritizes traffic light status and detected distance within the braking framework, enabling context-sensitive, adaptive emergency responses. A mechanically modified braking mechanism is controlled using a super-twisting strategy, ensuring robustness against unmodeled dynamics, nonlinearities, and external disturbances. Online traffic light detection is integrated with adaptive braking profiles for green, yellow, and red traffic light states, achieving a minimum response time of just 0.25 seconds. The experimental results indicated an angle deviation within the range of 0.06 ∘ to 0.35 ∘ , which corresponds to a variation of less than 0.2%. These results confirm the feasibility of integrating low-cost hardware with advanced control and decision-making logic to improve safety in autonomous platforms.