Adaptive Brake Control for Sustaining Dynamic Stability of Autonomous Vehicles Using Markov Decision Process

Enduring the optimal safety of autonomous vehicles while navigating through the complexities of dynamic traffic scenarios necessitates the implementation of urbane decision-making systems that are not only capable of processing real-time information but also adept at adapting to the ever-changing environmental and operational conditions that characterize urban and rural roadways. In this research work, a comprehensive framework based on the principles of Markov Decision Process (MDP) has been proposed that aims to optimize the critical functions of braking control and collision avoidance during autonomous driving, thereby enabling them to make real-time adjustments in response to fluctuating deceleration demands and varying road conditions that may arise during braking. To further refine the decision-making capabilities of the system, a strategy that combines exhaustive enumeration with Epsilon-Greedy action selection mechanism is integrated, which effectively balances the dual imperatives of exploration and exploitation to facilitate the convergence towards an optimal policy that maximizes longitudinal stability and braking efficacy. Simulation outcomes demonstrate the system’s proficiency to dynamically allocate braking forces while executing safe lane changes whenever the situational context permits such manoeuvres. This research contributes to augment the operational wellbeing and reliability of fully automated vehicles using intelligent brake control with adaptive decision making in real-world driving scenarios characterized by uncertainty and variability.