Collision Prevention and Autonomous Navigation through Radar and Sensor Fusion

Abstract

Autonomous navigation is fundamentally dependent on precise environmental perception to guarantee both safety and efficiency Radar technology is crucial in this regard due to its re- liability in adverse weather conditions and its proficiency in long- range object detection. It excels particularly in tracking dynamic objects and recognizing obstacles. Nevertheless, radar by itself frequently falls short in providing the resolution and contextual information necessary for navigating complex driving environ- ments.To address these shortcomings, sensor fusion—combining data from radar, LiDAR, cameras, ultrasonic sensors, and inertial measurement units (IMUs)—improves overall situational aware- ness by leveraging the advantages of each sensing technology. This research emphasizes the integration of radar with sensor fusion techniques to facilitate real-time collision avoidance and autonomous navigation.The market for autonomous vehicles (AVs) is projected to grow significantly over the next few decades, transforming the landscape of transportation and mobility. AVs are engineered to analyze their environment and execute driving tasks with minimal to no human intervention, ultimately seeking to supplant traditional vehicles. These vehicles utilize a wide array of sensors to perceive and interpret their surroundings, while also depending on advancements in 5G communication technologies to ensure seamless operation.Despite the consider- able advancements in sensor technologies in recent years, chal- lenges persist. Even with the fast growth of technology, sensors can still face issues like hardware failures, outside disturbances, or changing environmental conditions, all of which can reduce how well they perform.