Deep Learning for Safe Autonomous Driving: Current Challenges and Future Directions
Sejong University · Sungkyunkwan University · +5 more institutions
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-the-art strategies for safe AD, with their major achievements and limitations. Furthermore, it covers major…
Citation impact
- FWCI
- 20.07
- Percentile
- 100%
- References
- 210
Authors
5- KMKhan MuhammadCorresponding
Sejong University, Sungkyunkwan University
- AUAmin Ullah
Sejong University
- JLJaime Lloret
University of Staffordshire, Universitat Politècnica de València
- JDJavier Del Ser
University of the Basque Country, Association of Electronic and Information Technologies
- VHVictor Hugo C. de Albuquerque
Instituto Federal de Educação, Ciência e Tecnologia do Ceará
Topics & keywords
- Deep learning
- Reliability (semiconductor)
- Computer science
- Collision avoidance
- Pipeline (software)
- Artificial intelligence
- Engineering
- Risk analysis (engineering)
- Sustainable cities and communities