Proactive Collision Avoidance: IoT-Driven Real-Time Object and Lane Detection for Self-Driving Cars
Abstract
Ensuring road safety and optimizing traffic management are critical challenges in the development of autonomous vehicles. This research presents a real-time road safety system that integrates Internet of Things (IoT) devices with deep learning techniques to enhance autonomous vehicle navigation and overall traffic efficiency. The proposed system utilizes a network of IoT-enabled cameras and sensors to continuously monitor road conditions, detect objects such as vehicles and pedestrians, and identify lane boundaries with high accuracy. To achieve real-time object and lane detection, the system employs Convolutional Neural Networks (CNNs) and the YOLO (you only look once) algorithm, which enable efficient and precise recognition of obstacles, traffic signals, and road markings. Additionally, proximity alerts are generated using ultrasonic sensors to prevent potential collisions, ensuring safer navigation. The system incorporates environmental sensors such as DHT11 (for temperature and humidity) and rain sensors to dynamically adjust vehicle responses based on changing weather conditions, further enhancing road safety. By leveraging the power of IoT and deep learning, this intelligent system not only improves vehicle autonomy but also contributes to optimizing traffic flow through data-driven decision-making. The integration of real-time monitoring, predictive analysis, and automated responses enhances situational awareness, reducing the likelihood of accidents and enabling safer transportation systems. This research underscores the potential of AI-driven IoT solutions in shaping the future of intelligent transportation and smart mobility. The system achieved high accuracy rates, including 92.4% for object detection using YOLO, 94.5% for lane detection using CNN, and 96.3% for proximity alerts. These results underscore the effectiveness of integrating IoT and AI in real-time collision avoidance systems for autonomous vehicles.
