[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content

Advertisement

Log in

A Cognitively Inspired System Architecture for the Mengshi Cognitive Vehicle

  • Published:
Cognitive Computation Aims and scope Submit manuscript

Abstract

This paper introduces the functional system architecture of the Mengshi intelligent vehicle, winner of the 2018 World Intelligent Driving Challenge (WIDC). Different from traditional smart vehicles, a cognitive module is introduced in the system architecture to realise the transition from perception to decision-making. This is shown to enhance the practical utility of the smart vehicle, enabling safe and robust driving in different scenes. The collaborative work of hardware and software systems is achieved through multi-sensor fusion and artificial intelligence (AI) technologies, including novel use of deep machine learning and context-aware scene analysis to select optimal driving strategies. Experimental results using both robustness tests and road tests confirm that the Mengshi intelligent vehicle is reliable and robust in challenging environments. This paper describes the major components of this cognitively inspired architecture and discusses the results of the 2018 WIDC.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Zhang X Y, Gao H B, Guo M. A study on key technologies of unmanned driving. Caai Trans Intel Technol 2016;1(1):4–13.

    Article  Google Scholar 

  2. Marghi Y, Towhidkhah F, Gharibzadeh S. Human brain function in path planning: a task study. Cogn Comput 2017;9(1):136–149.

    Article  Google Scholar 

  3. Heide A, Henning K. The cognitive car: a roadmap for research issues in the automotive sector. Annu Rev Control 2006;30(2):197–203.

    Article  Google Scholar 

  4. Themes for a new research centre (Themen fur ein neues DFG Forschungszentrum definiert). Informationen fur die Wissenschaft No.8. Deutsche Forschungsgemeins-chaft e.V Bonn, 2004.

  5. Czubenko M, Kowalczuk Z, Ordys A. Autonomous driver based on an intelligent system of decision-making. Cogn Comput 2015;7(5):569–581.

    Article  Google Scholar 

  6. Yue L. Deep learning based scene parsing algorithm for intelligent vehicle. Beijing: Beijing Forestry University; 2016.

    Google Scholar 

  7. Amparore E, Beccuti M, Collina S. 2015. Cognitive systems in intelligent vehicles - a new frontier for autonomous driving, International conference on informatics in control, automation and robotics, pp 817–822.

  8. Li L, Wen D, Zheng N N, Shen L C. Cognitive cars: a new frontier for ADAS research. IEEE Trans Intell Transp Syst 2012;13(1):395–407.

    Article  Google Scholar 

  9. Candamo J. Understanding transit scenes: a survey on human behavior-recognition algorithms. IEEE Trans Intell Transp Syst 2010;11(1):206–224.

    Article  Google Scholar 

  10. Thrun S, Montemerlo M, Dahlkamp H. 2006. Stanley: the robot that won the DARPA grand challenge. The 2005 DARPA grand challenge, pp 1–43.

  11. Levinson J, Askeland J, Becker J. 2011. Towards fully autonomous driving: systems and algorithms. Intelligent Vehicles Symposium, pp 163–168.

  12. Ziegler J, Bender P, Schreiber M. Making bertha drive: an autonomous journey on a historic route. Int Transp Syst Magazine 2014;6(2):8–20.

    Article  Google Scholar 

  13. Chen L. The core basic scientific question of new generation artificial intelligence: the relation between cognition and computing. Think Tank Viewpoint 2018;33(10):1104–1106.

    Google Scholar 

  14. Song Y, Li Q, Kang Y. Conjugate unscented FastSLAM for autonomous mobile robots in large-scale environments. Cogn Comput 2014;6(3):496–509.

    Article  Google Scholar 

  15. Tu Z, Zheng A, Yang E, Luo B, Hussain A. A biologically inspired vision-based approach for detecting multiple moving objects in complex outdoor scenes. Cogn Comput 2015;7(5):539–551.

    Article  Google Scholar 

  16. Chen S, Zhang S, Shang J. Brain-inspired cognitive model with attention for self-driving cars. IEEE Trans Cogn Dev Syst 2017;99:1–13.

    Google Scholar 

  17. Zhao J H, Zhang X Y, Gao H B, Zhou M. 2018. Object detection based on hierarchical multi-view proposal network for autonomous driving, 2018 international joint conference on neural networks (IJCNN), pp 1–6.

  18. Zhang K, Liu H, Deng X, Sun F. Radar-image cross-modal retrieval for outdoor mobile robots. Proc. of cognitive systems and information processing; 2018.

  19. Liu H, Sun F, Fang B, Zhang X. Robotic room-level localization using multiple sets of sonar measurements. IEEE Trans Instrum Meas 2017;66(1):2–13.

    Article  Google Scholar 

  20. Li Y B, Niu L, Tong H. The design about smart car autopilot based on Global Positioning System. Automotive Electronics 2018;11:61–63.

    Google Scholar 

  21. Liu W, Zhang K, Zhang G S, Chi C. Development of vehicle dynamic state estimation in linear region based on wheel speed information. Automobile Applied Technology 2016;9:55–58.

    Google Scholar 

  22. Yu Y F, Zhao H J, Cui J S, Zha H B. Road structural feature based monocular visual localization for intelligent vehicle. Acta Automatica Sinica 2017;43(5):725–734.

    Google Scholar 

  23. Yu Z P, Zhang R X, Xiong L, Huang C J. Dynamic control for unmanned skid-steering vehicle with conditional integartors. J Mech Eng 2017;53(14):29–38.

    Article  Google Scholar 

  24. Wu T, Zhao J Y, Zhang Z L, Lu Z Y, Chang Z J. On current status and development tendency of vehicle visual odometer. Electronics Optics & Control 2017;24(10):69–74.

    Google Scholar 

  25. Zhao T T, Chen W P, Gu Y F. Design of automatic cruise unmanned ground vehicle based on environmental perception. Radio Eng 2017;47(10):73–78.

    Google Scholar 

  26. Mahmud M, Kaiser M S, Hussain A, Vassanelli S. Applications of deep learning and reinforcement learning to biological data. IEEE Trans Neural Netw Learn Syst 2018;29(6):2063–2079.

    Article  Google Scholar 

  27. Xiong F, Sun B, Yang X, Qiao H, Huang K, Hussain A, Liu Z. Guided policy search for sequential multitask learning. IEEE Trans Syst, Man, Cybern: Syst 2018;PP(99):1–11.

    Google Scholar 

  28. Scardapane S, Comminiello D, Hussain A, Uncini A. Group sparse regularization for deep neural networks. Elsevier Neurocomputing 2017;241:81–89.

    Article  Google Scholar 

  29. Zhang L, Liu Z, Zhang S, Yang X, Qiao H, Huang K, Hussain A. Cross-modality interactive attention network for multispectral pedestrian detection. Elsevier Information Fusion 2018;50:20–29.

    Article  Google Scholar 

Download references

Funding

This work was supported by the National High Technology Research and Development Program (“973”Program) of China under Grant No. 2016YFB0100903, National High Technology Research and Development Program of China under Grant No. 2018YFE0204300, Beijing Municipal Science and Technology Commission special major under Grant No. D171100005017002, National Natural Science Foundation of China under Grant No. U1664263.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Huaping Liu.

Ethics declarations

Conflict of Interest

The authors declare that they have no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed Consent

Informed consent was obtained from all individual participants included in the study.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, X., Zhou, M., Liu, H. et al. A Cognitively Inspired System Architecture for the Mengshi Cognitive Vehicle. Cogn Comput 12, 140–149 (2020). https://doi.org/10.1007/s12559-019-09692-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12559-019-09692-6

Keywords

Navigation