This paper introduces a novel reference model for intelligent longitudinal vehicle control, designed to enhance both safety and passenger comfort. The proposed model dynamically adjusts the follower vehicle’s acceleration based on its penetration distance relative to the lead vehicle, ensuring smooth speed transitions and adaptive deceleration. By preventing abrupt braking, the model maintains a safe inter-vehicle distance while reducing passenger discomfort. Key contributions include an analytical derivation of the follower vehicle’s dynamics and a novel formulation of the safety distance using the Lambert W function, enabling precise parameter optimization. A dedicated optimization framework ensures compliance with safety constraints while minimizing excessive acceleration and jerk. The model’s performance is validated through numerical simulations in various driving scenarios, including emergency braking, steady-speed following, variable-speed adaptation, and stop-and-go traffic. Results demonstrate its effectiveness in maintaining safety while enhancing ride comfort through gradual and controlled deceleration. The proposed approach is computationally efficient and well-suited for Advanced Driver Assistance Systems (ADAS) and autonomous vehicles. Future research will explore its integration with lateral control strategies, real-time adaptability, and machine learning techniques for further performance optimization in dynamic driving environments.