Nowadays, people spend a lot of time in travel, and road safety has become an important area of research. As a result, self-driving cars have been gaining popularity in recent years because they have a lot of benefits in terms of reducing traffic congestion and improving road safety, thereby reducing accidents. They also have an environmental benefit as they reduce travel times and hence reduce the usage of fuel. Though a lot of research has already been done in this field, there are always new problems being created and new solutions being found. Apart from self-driving cars, automated navigation also has a huge number of applications. Therefore, it is important to stay up to date about the latest developments in this field. This research is a comprehensive survey that explores and evaluates the different object-detecting algorithms that have been used throughout the years, their advantages, disadvantages, and trade-offs. It also analyses the datasets and benchmarks that have been used for evaluating the models. It aims to identify the gaps in research in this growing field so that it can act as a roadmap for researchers to focus their study on.
Towards Safer Roads: A Comprehensive Review of Object Detection Techniques for Autonomous Vehicles
SN COMPUT. SCI.
SN Computer Science ; 6 , 5
2025-05-04
Article (Journal)
Electronic Resource
English
Deep learning , Object detection , YOLO , Road safety , Self-driving Psychology and Cognitive Sciences , Psychology , Computer Science , Computer Science, general , Computer Systems Organization and Communication Networks , Software Engineering/Programming and Operating Systems , Data Structures and Information Theory , Information Systems and Communication Service , Computer Imaging, Vision, Pattern Recognition and Graphics
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