IMPLEMENTATION OF AN IOT-ENABLED SENSOR FUSION FOR ENHANCED OBSTACLE AVOIDANCE IN AUTONOMOUS VEHICLES
Keywords:
IoT (Internet of Things), Sensor Fusion, Autonomous Vehicles, Obstacle Avoidance, Real-Time NavigationAbstract
The rapid advancement of autonomous vehicle technology necessitates robust obstacle avoidance systems to ensure safety and efficiency in navigation. This study presents the implementation of an IoT-enabled sensor fusion framework designed to enhance obstacle avoidance capabilities in autonomous vehicles. By integrating various sensors, including LIDAR, cameras, and ultrasonic sensors, our approach leverages real-time data processing to achieve accurate obstacle detection and navigation. The sensor fusion algorithms optimize data interpretation and enhance situational awareness, enabling the vehicle to make informed decisions in dynamic environments. To evaluate the effectiveness of the proposed system, we conducted a series of experiments under controlled and real-world conditions. The results demonstrate a significant improvement in obstacle detection accuracy, with a reduction in false positive rates compared to traditional obstacle avoidance systems. Additionally, the real-time communication capabilities provided by IoT technologies facilitate rapid response times, further enhancing the vehicle's safety. This research contributes to the ongoing efforts in the field of autonomous navigation by demonstrating the potential of IoT-enabled sensor fusion to improve obstacle avoidance strategies. The findings suggest that integrating advanced sensor technologies and IoT frameworks can significantly enhance the reliability and performance of autonomous vehicles, paving the way for safer and more efficient transportation solutions.