A Multi-Modal Intelligent Vehicle System with Unified Visual Perception and Motion Control

Abstract

With the rapid development of autonomous systems, there is a growing need for versatile yet low-cost platforms that integrate perception, decision-making, and control in real-time environments. This study presents the design and implementation of a multi-functional intelligent car system based on the STM32F103C8T6 microcontroller. The system integrates an OPENMV camera for visual recognition, a DY-SV8F voice module, a dual 4-channel grayscale sensor array for high-precision path tracking, and a PS2 remote controller. A hierarchical control architecture is adopted. The core role of the actuators, which drives the vehicle’s motion in response to visual and sensory feedback, is emphasized through a tightly coupled control loop that translates perceptual data into precise PWM signals for motor speed and direction regulation. The core contribution lies in the design of a novel path tracking algorithm featuring Gaussian-weighted sensor fusion, a finite-state machine for path feature recognition, and an adaptive PID controller with curvature-based feedforward compensation. Furthermore, this paper provides a detailed exposition of a lightweight color marker recognition algorithm operating in the hue, saturation, value (HSV) color space, including its mathematical formulation and system-level integration with the motion control loop for event-triggered multi-modal task execution. Experimental results demonstrate that the proposed system achieves a mean absolute path tracking deviation of 2.1 mm, a color detection accuracy of 100% under controlled lighting, and flawless execution of complex, event-driven task sequences.