Probabilistic VRU Trajectory Forecasting for Model-Predictive Planning -- A Case Study: Overtaking Cyclists
J. Schneegans, J. Eilbrecht, S. Zernetsch, M. Bieshaar, K. Doll, O. Stursberg, und B. Sick.
IV 2021 Workshop From Benchmarking Behavior Prediction to Socially Compatible Behavior Generation in Autonomous Driving, (2021)(accepted).

This work considers (deep) artiﬁcial feed-forward neural networks as parametric approximators in optimal control of discrete-time switched linear systems with controlled switching. The proposed approach is based on approximate dynamic programming and allows the fast computation of (sub-)optimal discrete and continuous control inputs, either by approximating the optimal cost-to-go functions or by approximating the optimal discrete and continuous input policies. An important property of the approach is the satisfaction of polytopic state and input constraints, which is crucial for ensuring safety, as required in many control applications. A numeric example is provided for illustration and evaluation of the approaches.

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@inproceedings{SEZ+21,
  abstract = {This work considers (deep) artiﬁcial feed-forward neural networks as parametric approximators in optimal control of discrete-time switched linear systems with controlled switching. The proposed approach is based on approximate dynamic programming and allows the fast computation of (sub-)optimal discrete and continuous control inputs, either by approximating the optimal cost-to-go functions or by approximating the optimal discrete and continuous input policies. An important property of the approach is the satisfaction of polytopic state and input constraints, which is crucial for ensuring safety, as required in many control applications. A numeric example is provided for illustration and evaluation of the approaches.},
  added-at = {2021-06-29T15:01:47.000+0200},
  author = {Schneegans, Jan and Eilbrecht, Jan and Zernetsch, Stefan and Bieshaar, Maarten and Doll, Konrad and Stursberg, Olaf and Sick, Bernhard},
  biburl = {https://puma.uni-kassel.de/bibtex/2f5e742c1a830fed9d830656e4befd425/04068750},
  booktitle = {IV 2021 Workshop From Benchmarking Behavior Prediction to Socially Compatible Behavior Generation in Autonomous Driving},
  interhash = {39c15c7db8337c7bd3aca181d9cf526f},
  intrahash = {f5e742c1a830fed9d830656e4befd425},
  keywords = {imported isac-www},
  note = {(accepted)},
  timestamp = {2021-10-27T09:57:12.000+0200},
  title = {{Probabilistic VRU Trajectory Forecasting for Model-Predictive Planning -- A Case Study: Overtaking Cyclists}},
  year = 2021
}

%0 Conference Paper
%1 SEZ+21
%A Schneegans, Jan
%A Eilbrecht, Jan
%A Zernetsch, Stefan
%A Bieshaar, Maarten
%A Doll, Konrad
%A Stursberg, Olaf
%A Sick, Bernhard
%B IV 2021 Workshop From Benchmarking Behavior Prediction to Socially Compatible Behavior Generation in Autonomous Driving
%D 2021
%K imported isac-www
%T Probabilistic VRU Trajectory Forecasting for Model-Predictive Planning -- A Case Study: Overtaking Cyclists
%X This work considers (deep) artiﬁcial feed-forward neural networks as parametric approximators in optimal control of discrete-time switched linear systems with controlled switching. The proposed approach is based on approximate dynamic programming and allows the fast computation of (sub-)optimal discrete and continuous control inputs, either by approximating the optimal cost-to-go functions or by approximating the optimal discrete and continuous input policies. An important property of the approach is the satisfaction of polytopic state and input constraints, which is crucial for ensuring safety, as required in many control applications. A numeric example is provided for illustration and evaluation of the approaches.

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