Concept of Autonomouse Car

Abstract

Autonomous car systems were developed to transform modern transportation by enabling vehicles to operate with minimal or no human intervention. The system was designed to improve road safety, reduce traffic accidents caused by human errors, and enhance driving efficiency through the integration of artificial intelligence, sensor technologies, and real-time decision-making algorithms. The study focused on the design and evaluation of an autonomous driving framework that combined computer vision, deep learning models, and sensor fusion techniques to enable accurate perception of the driving environment. The system utilized multiple sensors such as cameras, LiDAR, radar, and ultrasonic devices to capture real-time road data. These inputs were processed using machine learning algorithms to detect objects, recognize lanes, identify traffic signs, and estimate distances. A decision-making module based on artificial intelligence was used to determine optimal driving actions, while a control unit translated these decisions into physical vehicle movements such as steering, braking, and acceleration. The performance of the system was evaluated using a structured questionnaire-based assessment involving usability, safety, trust, and system performance. The results indicated that the system achieved strong user acceptance and high perceived effectiveness. The usability dimension recorded an average mean score of 4.19, safety and reliability achieved 4.31, trust level recorded 4.04, and overall system performance achieved 4.29 on a 5-point Likert scale. These results demonstrated that respondents generally agreed that the autonomous car system was effective, reliable, and safe for intelligent transportation applications. However, limitations were observed in complex weather conditions and highly unpredictable traffic scenarios, where performance slightly decreased due to sensor and environmental constraints. The study concluded that autonomous car systems have strong potential to revolutionize transportation systems by improving safety and efficiency, although further improvements are required to enhance robustness, real-time adaptability, and user trust in fully autonomous driving environments.