A Novel Reference Model for Intelligent and Comfortable Longitudinal Vehicle Control: Theory, Optimization, and Validation

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Author(s)

Flavien H. Somda 1,* Desire Guel 1 Kisito K. Kabore 1 Antoine Schorgen 2

1. Université Joseph KI-ZERBO, Ouagadougou, Burkina Faso

2. AS Consulting, 128 rue la Boetie, 75008 Paris, France

* Corresponding author.

DOI: https://doi.org/10.5815/ijisa.2026.02.12

Received: 10 Nov. 2025 / Revised: 4 Jan. 2026 / Accepted: 13 Feb. 2026 / Published: 8 Apr. 2026

Index Terms

Longitudinal Vehicle Control, Nonlinear Control Model, Adaptive Deceleration, Safety Distance Optimization, Intelligent Transportation Systems, Advanced Driver Assistance Systems (ADAS)

Abstract

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.

Cite This Paper

Flavien H. Somda, Désiré Guel, Kisito K. Kaboré, Antoine Schorgen, "A Novel Reference Model for Intelligent and Comfortable Longitudinal Vehicle Control: Theory, Optimization, and Validation", International Journal of Intelligent Systems and Applications(IJISA), Vol.18, No.2, pp.167-184, 2026. DOI:10.5815/ijisa.2026.02.12

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