Adaptive emergency braking for highway safety: a simulation framework for performance and reliability evaluation

Highway safety is a critical concern for automated vehicles, particularly in dynamic and high-risk scenarios requiring precise braking and trajectory adjustments. This paper presents a comprehensive evaluation of an Automated Emergency Braking (AEB) system designed to enhance safety in diverse highway conditions. Using a Matlab Simulink-based test bench, the study explores Car-to-Car Rear Moving, Car-to-Car Rear Stationary, and Car-to-Car Rear Braking scenarios, adhering to ISO 8855 standards and leveraging a 3 Degrees of Freedom (3DOF) vehicle model. The analysis highlights the AEB system’s ability to mitigate collisions through adaptive Model Predictive Control and dynamic Path Planning, ensuring safe and efficient navigation. Results demonstrate the AEB system’s effectiveness in handling challenges such as deadlocks, rapid deceleration, and complex lane-change scenarios while balancing safety and performance trade-offs. Despite its strengths, limitations related to real-world unpredictability and computational demands are acknowledged, with recommendations for future work to refine integration with predictive algorithms and enhance scalability. This study underscores the importance of advanced AEB systems in improving highway driving safety and provides a robust framework for evaluating their performance in simulated environments. By addressing critical gaps in current research, this work contributes to the development of more reliable and adaptable braking systems for automated highway driving.

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Publication Details

Journal
Autonomous Intelligent Systems
Published
2026-09-16
DOI
https://doi.org/10.1007/s43684-026-00140-5
Primary Topic
Traffic control and management
Type
article
Field-Weighted Citation Impact
0.00

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article

Adaptive emergency braking for highway safety: a simulation framework for performance and reliability evaluation

Nuno Pombo, Ricardo Ribeiro, Catarina Gonçalves
Autonomous Intelligent Systems
Traffic control and management
article

Adaptive emergency braking for highway safety: a simulation framework for performance and reliability evaluation

Nuno Pombo, Ricardo Ribeiro, Catarina Gonçalves
article en

Abstract

Highway safety is a critical concern for automated vehicles, particularly in dynamic and high-risk scenarios requiring precise braking and trajectory adjustments. This paper presents a comprehensive evaluation of an Automated Emergency Braking (AEB) system designed to enhance safety in diverse highway conditions. Using a Matlab Simulink-based test bench, the study explores Car-to-Car Rear Moving, Car-to-Car Rear Stationary, and Car-to-Car Rear Braking scenarios, adhering to ISO 8855 standards and leveraging a 3 Degrees of Freedom (3DOF) vehicle model. The analysis highlights the AEB system’s ability to mitigate collisions through adaptive Model Predictive Control and dynamic Path Planning, ensuring safe and efficient navigation. Results demonstrate the AEB system’s effectiveness in handling challenges such as deadlocks, rapid deceleration, and complex lane-change scenarios while balancing safety and performance trade-offs. Despite its strengths, limitations related to real-world unpredictability and computational demands are acknowledged, with recommendations for future work to refine integration with predictive algorithms and enhance scalability. This study underscores the importance of advanced AEB systems in improving highway driving safety and provides a robust framework for evaluating their performance in simulated environments. By addressing critical gaps in current research, this work contributes to the development of more reliable and adaptable braking systems for automated highway driving.

Autonomous Intelligent SystemsVol. 6(1)
University of Beira Interior (PT)
Ministério da Ciência, Tecnologia e Ensino Superior, Fundação para a Ciência e a Tecnologia, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa
Affordable and clean energy
Openalex Percentile: Top 16%
Traffic control and management
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