Fuzzy adaptive zoning algorithm: integrating LiDAR and fuzzy logic for enhanced autonomous electric vehicle navigation in controlled spaces

Abstract

This research presents the development and comprehensive evaluation of the fuzzy adaptive zoning algorithm (FAZA), an innovative approach to obstacle avoidance systems for autonomous electric vehicles (AEVs) operating in confined environments such as office complexes, industrial zones, and campus areas. Motivated by the need for precise yet cost-effective navigation systems in controlled environments, this study employs a light detection and ranging (LiDAR) VLP-16 sensor through a strategically designed single-sensor architecture optimized for computational efficiency and cost-effectiveness in controlled environments. The system integrates a type-1 Mamdani fuzzy inference system (FIS) with hybrid membership functions combining rectangular and triangular configurations to achieve optimal decision-making balance. The research methodology is based on dividing the detection area into eight distinctive zones (A–H) within a limited operational domain of $-2.5m⩽x⩽2.0m$ and $0m<y⩽6.25m$, each with specific response characteristics: “half braking” for objects at safe distances, “full braking”for critical situations, and left or right maneuvers for dynamic avoidance. System evaluation encompassed 96 test scenarios with 100% accuracy in decision-making, including 16 “half braking” scenarios, 11 right maneuver scenarios, 24 left maneuver scenarios, and 33 “full braking” scenarios. The system demonstrated significant reliability in handling 7 multi-object scenarios and decisiveness in rejecting inputs outside specified parameters. Validation through field testing under low-light conditions confirmed the system’s effectiveness for practical implementation. The results indicate that FAZA successfully provides an efficient and reliable solution for AEV navigation in confined environments, with significant implications for developing more affordable intelligent transportation systems.