Deep Transfer Learning Techniques for Intelligent Vehicle Perception: A Comprehensive Review

Saturday - February 10 2024, 05:13 UTC - 9 months ago

A comprehensive review was done on the use of deep transfer learning for intelligent vehicle perception. Deep transfer learning aims to bridge the gap between lab-training and real-testing data and has shown great success in improving task performance for autonomous driving. The review provides valuable insights and direction for future research.

An international group of scientists has published a paper in the journal Green Energy and Intelligent Transportation, summarizing a comprehensive review of deep transfer learning for intelligent vehicle perception. This new survey paper aims to introduce and explain the deep transfer learning techniques for intelligent vehicle perception, as well as offer invaluable insights and directions for future research .

Within the realm of intelligent vehicles and autonomous driving, perception is a crucial component in receiving data from sensors and extracting meaningful information from the surrounding environment. This information is then used to make critical decisions for precise motion planning. However, challenges arise in real-world scenarios due to variations in sensor types and settings, diverse data styles, and differences in trained models .

These challenges have led to the emergence of deep transfer learning, which aims to improve task performance in a new domain by leveraging knowledge from similar tasks previously learned in another domain. Transfer learning has already shown great success in the field of autonomous driving, with its ability to largely apply knowledge acquired from one task to another. Despite the remarkable achievements of deep learning-based algorithms on benchmark datasets, there is still a significant gap between lab-training and real-testing data in the real world .

Deep transfer learning is a promising solution to bridge this gap and improve intelligent vehicle perception. The researchers divided the domain distribution discrepancy into three types: sensor difference, data difference, and model difference. By acknowledging and understanding these challenges, deep transfer learning can be optimized and enhanced for use in intelligent vehicle perception. This comprehensive review provides insights and directions for future research in this rapidly developing field .