Current Stage of Autonomous Driving Through A Quick Survey for Novice

  • Saman Sarraf The Institute of Electrical and Electronics Engineers, Senior Member IEEE
Keywords: Autonomous Driving, Artificial Intelligence, Machine Learning, Computer Vision

Abstract

Today, autonomous driving is considered a branch of artificial intelligence in which various technologies are employed, ranging from computer vision to machine learning-based sensor fusion technologies. This work summarizes the autonomous vehicle advances and also discusses the crucial components required to build such technology. The state-of-the-art architectures of autonomous vehicles compromise several core modules, including sensors, road scene perception, motion planning, core control system, and system management. The research showed that computer vision technologies such as object detection and tracking and localization and mapping techniques, play crucial roles in an advanced autonomous vehicle functional architecture. The current stage of this industry demonstrates the successful prototyping of autonomous vehicles without drivers’ significant interventions. However, the research centers and automobile industries’ ongoing development aim to explore the productization of such highly automated vehicles and seek to improve road scene perception to reduce the number of sensors while enhancing or maintaining the current performance.

References

. J. Wang, J. Liu, and N. Kato, “Networking and communications in autonomous driving: A survey,” IEEE Commun. Surv. Tutorials, vol. 21, no. 2, pp. 1243–1274, 2018.

. B. Zito, “2017: The year for autonomous vehicles,” Machine Learnings, 2017. https://machinelearnings.co/2017-the-year-for-autonomous-vehicles-8359fec2d2db.

. D. Patil, M. Ansari, D. Tendulkar, R. Bhatlekar, V. N. Pawar, and S. Aswale, “A Survey On Autonomous Military Service Robot,” in 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE), 2020, pp. 1–7.

. S. Grigorescu, B. Trasnea, T. Cocias, and G. Macesanu, “A survey of deep learning techniques for autonomous driving,” J. F. Robot., vol. 37, no. 3, pp. 362–386, 2020.

. S. Sarraf, D. D. Desouza, J. A. E. Anderson, and C. Saverino, “MCADNNet: Recognizing stages of cognitive impairment through efficient convolutional fMRI and MRI neural network topology models,” IEEE Access, vol. 7, pp. 155584–155600, 2019.

. S. Sarraf and M. Ostadhashem, “Big data application in functional magnetic resonance imaging using apache spark,” in 2016 Future Technologies Conference (FTC), 2016, pp. 281–284.

. S. Sarraf, “5g emerging technology and affected industries: Quick survey,” Am. Sci. Res. J. Eng. Technol. Sci., vol. 55, no. 1, pp. 75–82, 2019.

. J. Helgath, P. Braun, A. Pritschet, M. Schubert, P. Böhm, and D. Isemann, “Investigating the effect of different autonomy levels on user acceptance and user experience in self-driving cars with a VR driving simulator,” in International Conference of Design, User Experience, and Usability, 2018, pp. 247–256.

. Ö. Ş. Taş, F. Kuhnt, J. M. Zöllner, and C. Stiller, “Functional system architectures towards fully automated driving,” in 2016 IEEE Intelligent vehicles symposium (IV), 2016, pp. 304–309.

. S. Sarraf and J. Sun, “Functional brain imaging: A comprehensive survey,” arXiv Prepr. arXiv1602.02225, 2016.

. E. Yurtsever, J. Lambert, A. Carballo, and K. Takeda, “A survey of autonomous driving: Common practices and emerging technologies,” IEEE Access, vol. 8, pp. 58443–58469, 2020.

. G. Beckers et al., “Intelligent autonomous vehicles with an extendable knowledge base and meaningful human control,” in Counterterrorism, Crime Fighting, Forensics, and Surveillance Technologies III, 2019, vol. 11166, p. 111660C.

. A. Sarraf, “Binary Image Classification Through an Optimal Topology for Convolutional Neural Networks,” Am. Sci. Res. J. Eng. Technol. Sci., vol. 68, no. 1, pp. 181–192, 2020.

. A. Sarraf, A. E. Jalali, and J. Ghaffari, “Recent Applications of Deep Learning Algorithms in Medical Image Analysis,” Am. Sci. Res. J. Eng. Technol. Sci., vol. 72, no. 1, pp. 58–66, 2020.

. S. Haag, B. Duraisamy, W. Koch, and J. Dickmann, “Radar and lidar target signatures of various object types and evaluation of extended object tracking methods for autonomous driving applications,” in 2018 21st International Conference on Information Fusion (FUSION), 2018, pp. 1746–1755.

. Z. Gojcic, C. Zhou, J. D. Wegner, and A. Wieser, “The perfect match: 3d point cloud matching with smoothed densities,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019, pp. 5545–5554.

. A. Ziebinski, R. Cupek, H. Erdogan, and S. Waechter, “A survey of ADAS technologies for the future perspective of sensor fusion,” in International Conference on Computational Collective Intelligence, 2016, pp. 135–146.

. S. Sarraf, C. Saverino, and A. M. Golestani, “A robust and adaptive decision-making algorithm for detecting brain networks using functional mri within the spatial and frequency domain,” in 2016 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), 2016, pp. 53–56.

. S. Gnatzig, F. Schuller, and M. Lienkamp, “Human-machine interaction as key technology for driverless driving-A trajectory-based shared autonomy control approach,” in 2012 IEEE RO-MAN: The 21st IEEE International Symposium on Robot and Human Interactive Communication, 2012, pp. 913–918.

Published
2020-10-08
Section
Articles