Computer vision is at the core of enabling driverless vehicles, as it enables machines to see and comprehend the world in the same way that human drivers do. Computer vision includes a wide range of abilities in detecting objects, detecting lane markings, detecting signs, and following pedestrians, all of which are critical to safety and efficiency in transport. This article discusses how computer vision is utilized in autonomous cars, its unseen techniques, latest advancements, and challenges that it still poses. Owing to advanced expert systems like artificial intelligence and deep learning, autonomous systems are becoming advanced, processing real-time data and nanosecond-level decisions. Nevertheless, the performance of such systems is primarily dependent on the quality of the training data, weather, and model architecture. Furthermore, safety needs and morality are making up non-technical barriers. The present study is going to present an in-depth account of how computer vision is changing the future of transport and what should be considered to shift from semi-autonomous to fully autonomous transportation systems. Computer vision systems will transform transport by minimising traffic accidents, streamlining traffic flow, and increasing the inclusivity of the mobility system through ongoing industry-academia research collaboration.
Computer Vision, Self-Driving Cars, Object Detection, Lane Detection, Neural Networks, Autonomous Vehicles, Deep Learning, Sensor Fusion
Keval Bopaliya, Chintan D, Niraj Bhagchandani(2025); Vision-Based Perception Systems for Autonomous Vehicles — Advancements, Challenges, and the Road Ahead, International Journal for Innovative Research in Multidisciplinary Field, ISSN(O): 2455-0620, Vol-11, Issue-4, Pp.145-150.     Available on –  https://www.ijirmf.com/
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