@conference{Seb2021,
Author = {Sebastian vom Dorff, Chih-Hong Cheng, Hasan Esen, Martin Fränzle},
Title = {Mixed-Neighborhood, Multi-speed Cellular Automata for Safety-Aware Pedestrian Prediction},

Journal = {Proceedings, volume 13085 of Lecture Notes in Computer Science},
Year = {2021},
Pages = {501-520},
Month = {12},
Publisher = {Springer, Cham},
Address = {Springer},
Booktitle = {19 th International Conference on Software Engineering and Formal Methods},
type = {conference},
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 …}
}
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