Szczegóły publikacji
Opis bibliograficzny
Promises and challenges of reinforcement learning applications in motion planning of automated vehicles / Nikodem PANKIEWICZ, Tomasz WRONA, Wojciech TURLEJ, Mateusz ORŁOWSKI // W: Artificial Intelligence and Soft Computing : 20th International Conference, ICAISC 2021 : virtual event, June 21–23, 2021 : proceedings, Pt. 2 / eds. Leszek Rutkowski, [et al.]. — Cham : Springer, cop. 2021. — (Lecture Notes in Computer Science ; ISSN 0302-9743 ; LNCS 12855. Lecture Notes in Artificial Intelligence). — ISBN: 978-3-030-87896-2; e-ISBN: 978-3-030-87897-9. — S. 318–329. — Bibliogr., Abstr. — N. Pankiewicz, T. Wrona, W. Turlej, M. Orłowski – dod. afiliacja: Aptiv, Krakow, Poland
Autorzy (4)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 137089 |
|---|---|
| Data dodania do BaDAP | 2021-10-19 |
| DOI | 10.1007/978-3-030-87897-9_29 |
| Rok publikacji | 2021 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Konferencja | International Conference on Artificial Intelligence and Soft Computing 2021 |
| Czasopismo/seria | Lecture Notes in Computer Science |
Abstract
As automated driving development progresses forward, novel methods are required to handle the vastness of possible road situations and to face end user’s high demands. Trying to solve the problem of motion control involving decision making and trajectory planning it is reasonable to take into consideration reinforcement learning as a viable approach. In this paper, we present the promises reinforcement learning can bring to an automated driving domain and the list of challenges we encountered during our work. We address the issues related to the environment definition, sample efficiency, safety and explainability.
