Deep Learning for Safe Autonomous Driving: Current Challenges and Future Directions

Abstract

Advances in information and signal processing technologies have a significant impact on autonomous driving (AD), improving driving safety while minimizing the efforts of human drivers with the help of advanced artificial intelligence (AI) techniques. Recently, deep learning (DL) approaches have solved several real-world problems of complex nature. However, their strengths in terms of control processes for AD have not been deeply investigated and highlighted yet. This survey highlights the power of DL architectures in terms of reliability and efficient real-time performance and overviews state-of-theart strategies for safe AD, with their major achievements and limitations. Furthermore, it covers major embodiments of DL along the AD pipeline including measurement, analysis, and execution, with a focus on road, lane, vehicle, pedestrian, drowsiness detection, collision avoidance, and traffic sign detection through sensing and vision-based DL methods. In addition, we discuss on the performance of several reviewed methods by using different evaluation metrics, with critics on their pros and cons. Finally, this survey highlights the current issues of safe DL-based AD with a prospect of recommendations for future research, rounding up a reference material for newcomers and researchers willing to join this vibrant area of Intelligent Transportation Systems.

The Implications of Weather and Reflectivity Variations on Automatic Traffic Sign Recognition Performance

“…However, the developed system was tested solely on images extracted from diverse real-world settings and not continuous detection from real-world driving condition videos that would include the added complexity from continuous sign tracking. e analysis performed by Muhammad et al [43] on multiple state-of-the-art approaches for sign detection from Swedish Traffic Sign Dataset [44] also suffered from similar limitations. Tabernik and Skocaj developed a convolutional neural network-(CNN-) based system capable of recognizing 200 categories of signs.…”

Evaluation Method of Deep Learning-Based Embedded Systems for Traffic Sign Detection

“…Therefore, different researchers have carried out various studies that address these problems to find solutions [22], [25]. Other more specific investigations have focused on certain applications such as autonomous vehicles [26], [27], and more specifically their tasks such as object detection, semantic segmentation, among others [28]- [30].…”

Recent Advances of Artificial Intelligence in Manufacturing Industrial Sectors: A Review

“…Autonomous driving (AD) is a thriving field of study where AI is actively taking part. The main objectives of AD consist of road detection, lane detection, vehicle detection, pedestrian detection, drowsiness detection, collision avoidance, and traffic sign detection [9]. These tasks mainly involve image-based object detection, localization, and segmentation in the context of computer science, and they are enhanced through the use of multiple sensors and appropriately fusing collected data from them.…”