A Comprehensive Survey on Deep Learning-Based Pattern Recognition and Object Detection for Autonomous Vehicles
DOI:
https://doi.org/10.63949/Keywords:
- Autonomous Vehicles,
- Object Detection,
- Pattern Recognition,
- Deep Learning,
- Multimodal Fusion,
- 3D Object Detection
Abstract
The main concept of autonomous cars is founded on well-developed perception systems to read and properly provide feedback to the complex driving conditions. The most important among them, deep learning-based pattern recognition and object detection are now one of the major technologies that allow to reliably identify the road users, traffic signs and obstacles. The paper includes a comprehensive review of the latest advances in the field of deep learning methods of pattern recognition and object detection in autonomous vehicles. The study has thoroughly reviewed the 2D detection methods and 3D, CNN-based model, transformer model, and multi-modal fusion models. Standard, accuracy, real time performance, robustness and efficiency of computation are compared and analyzed. The questionnaire throws some light on the virtues and vices of existing approaches and some of the major issues like negative sensitivity to weather, cost of computation and the poor cross-domain generalization. Finally, possible research directions are discussed in order to lead the next-generation autonomous vehicles in the development of efficient, robust, and real-time perception systems.
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References
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