Deep Learning in Autonomous Vehicles: A Review

Main Article Content

Bhupesh Patra
Abha Mahalwar

Abstract

Autonomous vehicles (AVs) represent a transformative technology that has the potential to revolutionize transportation systems worldwide. Deep learning, a subset of machine learning, plays a crucial role in the development and operation of AVs by enabling perception, decision-making, and control. This paper provides a comprehensive review of the role of deep learning in AVs, covering topics such as object detection, lane detection, traffic sign recognition, localization, mapping, behaviour planning, and control systems. The paper also discusses the challenges and limitations of deep learning in AVs, including data quality and quantity, safety and security concerns, environmental factors, and regulatory and legal challenges. Furthermore, the paper examines recent advancements in deep learning models for AVs, integration with other technologies, potential impacts on society and industry, and future research directions. By providing a detailed overview of deep learning applications in AVs, this review aims to contribute to the ongoing discourse surrounding the development and deployment of autonomous vehicles.

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

Article Details

How to Cite
Patra, B. ., & Mahalwar, A. . (2020). Deep Learning in Autonomous Vehicles: A Review. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 11(1), 1686–1690. https://doi.org/10.61841/turcomat.v11i1.14634
Section
Research Articles

References

Bojarski, M., et al. (2016). End to end learning for self-driving cars.

Ren, S., et al. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks.

Li, Y., et al. (2018). Deep learning for traffic sign recognition: A review.

Stallkamp, J., et al. (2012). Man vs. computer: Benchmarking machine learning algorithms for traffic sign

recognition.

Mur-Artal, R., et al. (2017). ORB-SLAM2: An open-source SLAM system for monocular, stereo, and RGBD cameras.

Kuipers, J. (2018). Visual place recognition: A survey.

Zhang, W., et al. (2016). Towards driverless cars: A review of visual-based ground vehicle motion control.

Peng, H., et al. (2018). Deeploco: Fully convolutional localization networks for precise vehicle localization.

Sun, L., et al. (2017). A robust vehicle detection and classification system for traffic surveillance in daytime.

Shokri, R., et al. (2017). Deep models under the GAN: information leakage from collaborative deep

learning.

Chen, X., et al. (2018). Deepdriving: Learning affordance for direct perception in autonomous driving.

Schmidhuber, J. (2015). Deep learning in neural networks: An overview.

Goodfellow, I., et al. (2016). Deep learning.

Zhang, W., et al. (2017). Visual-based ground vehicle motion control: A review.

Redmon, J., et al. (2016). You only look once: Unified, real-time object detection.

Zou, W., et al. (2019). Object detection in 20 years: A survey.

Wang, X., et al. (2018). Deep learning for sensor-based activity recognition: A survey.

Su, S., et al. (2018). A survey of deep learning-based object detection.

Li, S., et al. (2019). A review of deep learning techniques for image segmentation.

Tang, J., et al. (2018). A survey on deep learning in medical image analysis.

Wang, S., et al. (2017). Deep learning for sensor-based human activity recognition: A survey.

Nguyen, A., et al. (2015). A survey of techniques for event detection in video.

Parkhi, O. M., et al. (2015). Deep face recognition.

Feichtenhofer, C., et al. (2016). Convolutional two-stream network fusion for video action recognition.

Simonyan, K., et al. (2014). Two-stream convolutional networks for action recognition in videos.

Wang, L., et al. (2016). Temporal segment networks: Towards good practices for deep action recognition.

He, K., et al. (2016). Deep residual learning for image recognition.

Szegedy, C., et al. (2015). Going deeper with convolutions.

Lin, T. Y., et al. (2017). Focal loss for dense object detection.

Girshick, R. (2015). Fast R-CNN.

Dai, J., et al. (2016). R-FCN: Object detection via region-based fully convolutional networks.

Redmon, J., et al. (2017). YOLO9000: Better, faster, stronger.