Embedded intelligent vehicle perception and control using neural network methodologies

This paper presents the design and implementation of an embedded intelligent vehicle system for resource-constrained environments. The system addresses key challenges in environmental perception, multimodal control, and dynamic obstacle avoidance. Built on the OpenMV Cam H7 platform, it deploys a quantized MobileNetV2 model optimized with TensorFlow Lite for real-time traffic-sign recognition while maintaining computational efficiency. The overall architecture integrates an STM32 microcontroller with a proportional-integral-derivative-based motion-control strategy to support multi-stage braking. To satisfy diverse operational requirements, the vehicle also provides Bluetooth remote control and voice-command interfaces, supported by a priority-based arbitration mechanism that enables safe transitions between control modes. Environmental navigation is enhanced through visual detection and adaptive path-marking recognition, enabling dynamic target tracking and accurate ground-path perception. For collision avoidance, the system fuses OpenMV visual data with ultrasonic and infrared sensing to implement a hierarchical avoidance strategy, including proportional-integral-derivative-controlled emergency braking in critical situations. Experimental results in simulated road environments show robust real-time performance. The proposed platform provides an explainable and cost-effective engineering framework for embedded intelligent vehicles in educational and inspection scenarios, balancing algorithmic efficiency with practical deployability.