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Exploiting Roadside Sensor Data for Vehicle Manoeuvring Assistance

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European Robotics Forum 2024 (ERF 2024)

Part of the book series: Springer Proceedings in Advanced Robotics ((SPAR,volume 32))

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Abstract

This paper introduces a novel approach for tracking and predicting the movements of vehicles at an intersection using only roadside sensor data. The prediction of the vehicles’ movements is used to estimate their manoeuvres and to assist other vehicles, that are approaching the intersection, by suggesting suitable manoeuvres to follow. The solution introduced in this paper was tested in a real road intersection with everyday traffic and results showed a limited error that is acceptable for the goal of this system which is to predict the vehicles’ manoeuvres.

This work was supported by the PoDIUM project (101069547) funded by the European Commission.

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References

  1. Multi-access edge computing (mec). https://www.etsi.org/technologies/multi-access-edge-computing

  2. Statistical outlier removal. https://pcl.readthedocs.io/projects/tutorials/en/latest/statistical_outlier.html

  3. Fu, Y., Li, C., Luan, T.H., Zhang, Y., Mao, G.: Infrastructure-cooperative algorithm for effective intersection collision avoidance. Emerg. Technol. Transport. Res. Part C (2018)

    Google Scholar 

  4. Schubert, R., et al.: Comparison and evaluation of advanced motion models for vehicle tracking. In: 2008 11th International Conference on Information Fusion (2008)

    Google Scholar 

  5. Tang, B., et al.: Turn prediction at generalized intersections. In: 2015 IEEE Intelligent Vehicles Symposium (IV) (2015)

    Google Scholar 

  6. Tsogas, M., et al.: Unscented kalman filter design for curvilinear motion models suitable for automotive safety applications. In: 2005 7th International Conference on Information Fusion (2005)

    Google Scholar 

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Correspondence to Federico Princiotto .

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© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Princiotto, F. et al. (2024). Exploiting Roadside Sensor Data for Vehicle Manoeuvring Assistance. In: Secchi, C., Marconi, L. (eds) European Robotics Forum 2024. ERF 2024. Springer Proceedings in Advanced Robotics, vol 32. Springer, Cham. https://doi.org/10.1007/978-3-031-76424-0_50

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