Explainable Fuzzy Control for Adaptive Cruise Control and Lane Change in Autonomous Highway Driving

Abstract

Highway autonomous driving demands coordinated longitudinal and lateral decision-making to ensure safety, comfort, and traffic efficiency. This paper proposes an explainable fuzzy navigation approach addressing two independently developed functions: adaptive cruise control (ACC) and lane-change decision-making. For ACC, a Sugeno-type fuzzy inference system governs longitudinal control using inter-vehicle gap error, relative velocity, and speed-limit deviation, with membership function parameters tuned via a simulation-based Bayesian ε -constraint multi-objective optimization framework that explicitly balances safety and comfort. For lane-change decisions, a Nash-product-based multiobjective fuzzy decision framework is adopted. A Safety and a Progress utility channels each evaluate lane-change opportunities through dedicated Sugeno fuzzy inference systems, generating utility scores over target-lane gap geometry, relative speed dynamics, and speed deficit. A lane change is executed if and only if both utility channels strictly exceed their disagreement-point scores - the utility of remaining in the current lane - enforcing a mutual surplus condition that prevents geometrically safe but strategically unnecessary maneuvers. The lane-change module is evaluated as a standalone decision layer across static traffic configurations. The Nash-FIS configuration outperforms both the hand tuned baseline and a MOBIL-style reference on all borderline scenarios while preserving the intended safety barrier across all hard-veto cases. Integration of both modules into a unified dynamic simulation framework, encompassing car-following, lane-change, and overtaking maneuvers, constitutes the next stage of this work.