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Mixed-Neighborhood, Multi-speed Cellular Automata for Safety-Aware Pedestrian Prediction

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Part of the book series:Lecture Notes in Computer Science ((LNTCS,volume 13085))

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Abstract

Predicting pedestrian movement in unregulated traffic areas, such as parking grounds, marks a complex challenge in safety for automated vehicles. Without the ability to make certifiable predictions and judgments about safe interactions with other traffic agents in a real-time capable and economical fashion, the goal of self-driving vehicles cannot be reached. We propose a computationally efficient model for pedestrian behavior prediction on a short finite time horizon to ensure safety in automated driving. The model is based on a cellular automaton, working on an occupancy grid map and assumes a physical pedestrian capability constraint. It is enriched by a variable update rate with a mixed neighborhood, overcoming the limitations of vanilla cellular automata and coming closer to the results of state-of-the-art algorithms, while keeping the benefits of its straightforward parallelizability. The approach is evaluated on synthetic benchmarks outlining the general performance parameters as well as in an implementation on potential real-world situations.

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Author information

Authors and Affiliations

  1. Corporate R&D Department of DENSO Automotive Deutschland GmbH, Freisinger Street 21–23, 85386, Eching, Germany

    Sebastian vom Dorff, Chih-Hong Cheng & Hasan Esen

  2. Department of Computing Science, Carl von Ossietzky University, 26111, Oldenburg, Germany

    Sebastian vom Dorff & Martin Fränzle

Authors
  1. Sebastian vom Dorff

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  2. Chih-Hong Cheng

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  3. Hasan Esen

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  4. Martin Fränzle

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Corresponding author

Correspondence toSebastian vom Dorff.

Editor information

Editors and Affiliations

  1. University of York, York, UK

    Radu Calinescu

  2. Carnegie Mellon University, Moffett Field, CA, USA

    Corina S. Păsăreanu

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Dorff, S.v., Cheng, CH., Esen, H., Fränzle, M. (2021). Mixed-Neighborhood, Multi-speed Cellular Automata for Safety-Aware Pedestrian Prediction. In: Calinescu, R., Păsăreanu, C.S. (eds) Software Engineering and Formal Methods. SEFM 2021. Lecture Notes in Computer Science(), vol 13085. Springer, Cham. https://doi.org/10.1007/978-3-030-92124-8_28

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