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Dynamic Guidance of an Autonomous Vehicle with Spatio-Temporal GIS

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Computational Science and Its Applications – ICCSA 2017 (ICCSA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10407))

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Abstract

Most of computer-vision systems for vehicle guidance are tuned for highway scenarios. Developing autonomous or driver-assistance systems for complex urban traffic pose new algorithmic and system architecture challenges. This paper introduces a novel system that uses Spatio-Temporal GIS principles and a new traffic determination algorithm. The prototype system can be used as a part of autonomous vehicles controlling structure with analytical capabilities. For these purposes, predicting vehicle motion is done through trajectories. The trajectories are generated using spatial and aspatial information in GIS environment. Then, vehicle is navigated by GPS and a fuzzy logic map matching is used to locate the vehicle position on the map. Traffic congestion algorithm, which passed through 82.5% of evaluations cases successfully, is performed by vehicle’s velocity in the third step. Finally, heuristic real-time route finding algorithm is used for dynamic updating of planned vehicle trajectory. Furthermore, the system is open so that further extensions such as controlling autonomous vehicle for avoiding or leaving the banned regions are possible.

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Author information

Authors and Affiliations

  1. Faculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran, Iran

    Alireza Vafaeinejad

Authors
  1. Alireza Vafaeinejad
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Corresponding author

Correspondence to Alireza Vafaeinejad .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Trieste, Trieste, Italy

    Giuseppe Borruso

  5. Polytechnic University of Bari, Bari, Italy

    Carmelo M. Torre

  6. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  7. Monash University, Clayton, Victoria, Australia

    David Taniar

  8. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  9. Saint Petersburg State University, Saint Petersburg, Russia

    Elena Stankova

  10. University of Trieste, Trieste, Italy

    Alfredo Cuzzocrea

Appendix

Appendix

See (Fig. 6).

Fig. 6.
figure 6

The fuzzy logic map-matching algorithm and its rules [11]

Full size image

Rule 1:

If Δheading is small

THEN resemblance of the route is high.

Where “Δheading” is defined as the difference between the direction of the street segment and the heading of the vehicle.

Rule 2:

IF Δheading is almost 180° AND heading error is zero

THEN possibility of U-turn is high.

Where AND is a minimal operator.

Rule 3.1:

IF Δdistance is large

THEN necessity to retrieve successive segment is high.

Rule 3.2:

IF Δheading is large and heading error is zero

THEN necessity to retrieve successive segments is high.

Where Δdistance is defined as the difference between the segment length and distance which vehicle has traveled on that particular segment.

Rule 4:

IF the heading errors and the root mean square errors for the vehicle’s speed are small

THEN the motion is steady.

Rule 5:

IF the truth value of the previous candidate street pattern is high AND IF the truth value of the current candidate street pattern is high

THEN the truth value of the correspondence is high.

Rule 6.1:

IF the difference between the distances traveled along the current candidate street and the length of the candidate street is small AND IF the difference between the vehicle heading and direction of successive street is small

THEN the truth value for the successive street is high.

Rule 6.2:

IF the truth value of the candidate street is high AND IF the truth value for the successive street is high

THEN the combined truth value of the moving vehicle on the street is high.

Rule 6.3:

IF no street pattern similar to the path of travel can be found with given distance

THEN the vehicle is on-street.

Rule 7:

IF there is more than one street pattern within a given distance to the current vehicle motion

THEN the vehicle is on-street.

Rule 8.1:

IF Δheading is small AND Δdistance is small

THEN resemblance of this segment is high.

Rule 8.2:

IF resemblance of this segment is high AND resemblance path is high

THEN resemblance of the whole path is high.

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Vafaeinejad, A. (2017). Dynamic Guidance of an Autonomous Vehicle with Spatio-Temporal GIS. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2017. ICCSA 2017. Lecture Notes in Computer Science(), vol 10407. Springer, Cham. https://doi.org/10.1007/978-3-319-62401-3_36

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  • DOI: https://doi.org/10.1007/978-3-319-62401-3_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-62400-6

  • Online ISBN: 978-3-319-62401-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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