Skip to main content
Log in

Real-time self-driving car navigation and obstacle avoidance using mobile 3D laser scanner and GNSS

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Self-driving car navigation is currently attracting considerable research interests. The key problem is to guide the car to the destination in real-time with a safe and obstacle free path in real-world environments. We propose an innovative self-driving car navigation approach that incorporates a VFH (Vector Field Histogram) local path planner adapted for modern 3D laser scanners and a global path planner using satellite positioning and digital map along with a custom built PID controller (Proportional Integral Derivative controller). Since classical path planning methods such as VFH are often used on small robots with ultrasonic rangefinders or in simulation based environments, we applied the VFH method to a real self-driving car with two different LiDAR (Light Detection And Ranging) configurations. The quantitative results from extensive experiments indicate that the developed VFH method with the modern real-time 3D LiDAR generally outperform the conventional LIDAR in terms of efficiency, accuracy and reliability. In addition, the tracks produced by 3D LIDAR are more convergent, smooth and consistent than the other configuration. The maximum position deviation for the VFH with 3D LiDAR is 0.28 m and −0.16 m while the deviation for the other low-cost solution is 0.88 m and −0.49 m respectively. The global path planner can provide an accuracy of within 1 meter most of the time. The proposed approach is successfully implemented and tested on our self-driving car which took part in the national self-driving car competitions in recent years, and ranked No.3 and 4. in the future challenge 2014 self-driving car competition in China.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27

Similar content being viewed by others

References

  1. Andrej B, Anton V, Frantisek D, Dekan (2013) Navigation of robot using VFH+ algorithm. Journal of Mechanics Engineering and Automation 3:303–310

    Google Scholar 

  2. Babinec A, Duchoň F, Dekan M, Pásztó P, Kelemen M (2014) VFH*TDT (VFH* with time dependent tree): a new laser rangefinder based obstacle avoidance method designed for environment with non-static obstacles. Robotics & Autonomous Systems 62(8):1098–1115

    Article  Google Scholar 

  3. Borenstein J, Koren Y (1991) The vector field histogram-fast obstacle avoidance for mobile robots. Robotics & Automation IEEE Transactions on 7(3):278–288

    Article  Google Scholar 

  4. Broggi A, Cerri P, Debattisti S, Laghi MC (2015) Proud—public road urban driverless-car test. Intelligent Transportation Systems IEEE Transactions on 16(6):3508–3519

    Article  Google Scholar 

  5. Choi JW, Huhtala K (2015) Constrained global path optimization for articulated steering vehicles. IEEE Trans Veh Technol 65(4):1868–1879

    Article  Google Scholar 

  6. Choi S, Kim E, Oh S (2014) Real-time navigation in crowded dynamic environments using Gaussian process motion control. IEEE International Conference on Robotics and Automation:3221–3226

  7. Ulrich I, Borenstein J (2000) VFH*: local obstacle avoidance with look-ahead verification. IEEE International Conference on Robotics & Automation 3:2505–2511

    Google Scholar 

  8. Ibeo Automotive Systems GmbH (2012) Features of ibeo LUX 8 L and ibeo LUX.

  9. Kamil F, Tang SH, Khaksar W, Zulkifli N, Ahmad SA (2015) A review on motion planning and obstacle avoidance approaches in dynamic environments. Advances in Robotics & Automation 4:134. doi:10.4172/2168-9695.1000134

    Google Scholar 

  10. Katrakazas C, Quddus M, Chen WH, Deka L (2015) Real-time motion planning methods for autonomous on-road driving: state-of-the-art and future research directions. Transportation Research Part C Emerging Technologies 60:416–442

    Article  Google Scholar 

  11. Kim B, Choi B, Park S, Kim H (2016) Pedestrian/vehicle detection using a 2.5-d multi-layer laser scanner. Sensors Journal IEEE 16(2):400–408

    Article  Google Scholar 

  12. Kuwata Y, Fiore G, Teo J, Frazzoli E, How JP. (2008) Motion planning for urban driving using RRT. Intelligent Robots and Systems, 2008. IROS 2008. IEEE/RSJ International Conference on p 1681–1686 IEEE

  13. Li J, Fan X, Wang C, Bao H, Xiao Y (2015) Accuracy assessment of GPS navigation augmented by SAR and LiDAR-derived digital elevation models. International Journal of Digital Earth 8:1–16

    Article  Google Scholar 

  14. Liu J, Jayakumar P, Overholt JL, Stein JL, Ersal T. (2013) The role of model fidelity in model predictive control based hazard avoidance in unmanned ground vehicles using LIDAR sensors. Dynamic Systems and Control Conference

  15. Liu P, Eom KB (2014) Compressive sensing of noisy multispectral images. IEEE Geoscience & Remote Sensing Letters 11(11):1931–1935

    Article  Google Scholar 

  16. Liu P, Choo KKR, Wang L, Huang F (2016) Svm or deep learning? a comparative study on remote sensing image classification. Soft Computing:1–13

  17. Lu H, Wei J, Wang L, Liu P, Liu Q, Wang Y, Deng X (2016) Reference information based remote sensing image reconstruction with generalized nonconvex low-rank approximation. Remote Sensing 8(6)

  18. Ma Y, Wu H, Wang L, Huang B, Ranjan R, Zomaya A, Jie W (2015) Remote sensing big data computing: challenges and opportunities. Futur Gener Comput Syst 51:47–60

    Article  Google Scholar 

  19. Nepal K, Fine A, Imam N, Pietrocola D, Robertson N, Ahlgren DJ (2009) Combining a modified vector field histogram algorithm and real-time image processing for unknown environment navigation. In SPIE Proceedings 7252:1–8

    Google Scholar 

  20. Pepy R, Lambert A, Mounier H (2006) Path planning using a dynamic vehicle model. Information and Communication Technologies, 2006 Ictta '06 1:781–786

    Article  Google Scholar 

  21. Qu P, Xue J, Ma L, Ma C (2015) A constrained VFH algorithm for motion planning of autonomous vehicles. Intelligent Vehicles Symposium. IEEE:700–705

  22. Shim I, Choi J, Shin S, Oh TH, Lee U, Ahn B et al (2015) An autonomous driving system for unknown environments using a unified map. IEEE Trans Intell Transp Syst 16(4):1–15

    Article  Google Scholar 

  23. Thrun S, Montemerlo M, Dahlkamp H, Stavens D, Aron A, Diebel J et al (2006) Stanley: the robot that won the DARPA grand challenge: research articles. J Robot Syst 23(9):661–692

    Google Scholar 

  24. Ulrich I, Borenstein J. (1998) VFH+: reliable obstacle avoidance for fast mobile robots. In Proceedings of the 1998 I.E. International Conference on Robotics & Automation, p 1572–1577

  25. Usher K (2006) Obstacle avoidance for a non-holonomic vehicle using occupancy grids. In proceedings of Australasian Conference on Robotics and Automation, Auckland New Zealand, 6–8 December 2006

  26. Velodyne LiDAR, Inc (2015) HDL-32E User’ Manual and Programing Guide.

  27. Wang L, Song W, Liu P (2016) Link the remote sensing big data to the image features via wavelet transformation. Clust Comput 19(2):793–810

    Article  Google Scholar 

  28. Wang L, Zhang J, Liu P, Choo KKR, Huang F (2016) Spectral–spatial multi-feature-based deep learning for hyperspectral remote sensing image classification. Soft Computing:1–9

  29. Yim WJ, Park JB. (2014) Analysis of mobile robot navigation using vector field histogram according to the number of sectors, the robot speed and the width of the path. International Conference on Control, p 1037–1040 IEEE

Download references

Acknowledgements

This work was partially funded by National Natural Science Foundation of China (Grant No. 91420202 and Grant No.41101436) and Construction of Innovative Teams and Teacher Career Development for Universities and Colleges under Beijing Municipality (IDHT20140508). It was also partially funded by the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry. We thank Beijing Key Laboratory of Information Service Engineering for providing the test vehicle.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hong Bao.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, J., Bao, H., Han, X. et al. Real-time self-driving car navigation and obstacle avoidance using mobile 3D laser scanner and GNSS. Multimed Tools Appl 76, 23017–23039 (2017). https://doi.org/10.1007/s11042-016-4211-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-016-4211-7

Keywords

Navigation