@Article{cmc.2026.076104,
AUTHOR = {Awais Ahmad, Fakhri Alam Khan, Awais Ahmad, Gautam Srivastava, Syed Atif Moqurrab, Abdul Razaque, Dina S. M. Hassan},
TITLE = {An Optimal Acceleration Control for Collision Avoidance in VANETs Using Convex Optimization},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {87},
YEAR = {2026},
NUMBER = {3},
PAGES = {--},
URL = {http://www.techscience.com/cmc/v87n3/66914},
ISSN = {1546-2226},
ABSTRACT = {Collision avoidance is recognized as a critical challenge in Vehicular Ad-Hoc Networks (VANETs), which demand real-time decision-making. It plays a vital role in ensuring road safety and traffic efficiency. Traditional approaches like rule-based systems and heuristic methods fail to provide optimal solutions in dynamic and unpredictable traffic scenarios. They cannot balance multiple objectives like minimizing collision risk, ensuring passenger comfort, and optimizing fuel efficiency, leading to suboptimal performance in real-world conditions. To tackle collision avoidance, this paper introduces a novel approach by defining the issue as an optimal control problem and solving it using the Convex Optimization model, which results in autonomous braking and acceleration control in smart vehicles. The model begins by collecting real-time vehicle data from its environment, which includes speed, position, and distance. It then uses this information to generate optimal driving profiles for each vehicle and calculate optimal responses. The informational data guides the vehicle to adjust its braking and acceleration autonomously, ensuring that safe distances are maintained among vehicles without compromising comfort. This approach empowers individual vehicles to make real-time decisions independently and adapt swiftly to changing traffic conditions. The performance of the proposed method is evaluated using a simulation study in MATLAB using the CVX optimization toolbox, comparing Adaptive Cruise Control (ACC) and Fuzzy Logic-Based Collision Avoidance (FLCA) using various performance metrics. Compared to these baseline protocols, our approach achieves a higher success rate in preventing collisions by maintaining smoother acceleration profiles and lower fuel consumption, yielding up to a 32% reduction in collision likelihood and a 19% improvement in fuel efficiency. Additionally, unlike existing rule-based and model-predictive approaches, the proposed method introduces a unified cost function that jointly minimizes collision risk, acceleration jerk, and energy consumption in decentralized VANET scenarios. This integration enables real-time decision-making while maintaining safety, comfort, and efficiency across varying traffic densities.},
DOI = {10.32604/cmc.2026.076104}
}