Details
| Original language | English |
|---|---|
| Title of host publication | 2023 IEEE International Conference on Robotics and Automation |
| Subtitle of host publication | ICRA |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 7147-7153 |
| Number of pages | 7 |
| ISBN (electronic) | 9798350323658 |
| ISBN (print) | 979-8-3503-2366-5 |
| Publication status | Published - 2023 |
| Event | 2023 IEEE International Conference on Robotics and Automation, ICRA 2023 - London, United Kingdom (UK) Duration: 29 May 2023 → 2 Jun 2023 |
Publication series
| Name | Proceedings - IEEE International Conference on Robotics and Automation |
|---|---|
| Volume | 2023-May |
| ISSN (Print) | 1050-4729 |
Abstract
Graph networks have recently been used for decision making in automated driving tasks for their ability to capture a variable number of traffic participants. Current high-level graph-based approaches, however, do not model the entire road network and thus must rely on handcrafted features for vehicle-to-vehicle edges encompassing the road topology indirectly. We propose an entity-relation framework that intuitively models the road network and the traffic participants in a heterogeneous graph, representing all relevant information. Our novel architecture transforms the heterogeneous road-vehicle graph into a simpler graph of homogeneous node and edge types to allow effective training for deep reinforcement learning while introducing minimal prior knowledge. Unlike previous approaches, the vehicle-to-vehicle edges of this reduced graph are fully learnable and can therefore encode traffic rules without explicit feature design, an important step towards a holistic reinforcement learning model for automated driving. We show that our proposed method outperforms precomputed handcrafted features on intersection scenarios while also learning the semantics of right-of-way rules.
ASJC Scopus subject areas
- Computer Science(all)
- Software
- Engineering(all)
- Control and Systems Engineering
- Engineering(all)
- Electrical and Electronic Engineering
- Computer Science(all)
- Artificial Intelligence
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2023 IEEE International Conference on Robotics and Automation: ICRA. Institute of Electrical and Electronics Engineers Inc., 2023. p. 7147-7153 (Proceedings - IEEE International Conference on Robotics and Automation; Vol. 2023-May).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Deep Reinforcement Learning for Autonomous Driving using High-Level Heterogeneous Graph Representations
AU - Schier, Maximilian
AU - Reinders, Christoph
AU - Rosenhahn, Bodo
N1 - Funding Information: VI. ACKNOWLEDGEMENTS This work was supported by the Federal Ministry of Education and Research (BMBF) Germany under the project LeibnizKILabor (grant no. 01DD20003), the Center for Digital Innovations (ZDIN), and the Deutsche Forschungs-gemeinschaft (DFG) under Germany’s Excellence Strategy within the Cluster of Excellence PhoenixD (EXC 2122).
PY - 2023
Y1 - 2023
N2 - Graph networks have recently been used for decision making in automated driving tasks for their ability to capture a variable number of traffic participants. Current high-level graph-based approaches, however, do not model the entire road network and thus must rely on handcrafted features for vehicle-to-vehicle edges encompassing the road topology indirectly. We propose an entity-relation framework that intuitively models the road network and the traffic participants in a heterogeneous graph, representing all relevant information. Our novel architecture transforms the heterogeneous road-vehicle graph into a simpler graph of homogeneous node and edge types to allow effective training for deep reinforcement learning while introducing minimal prior knowledge. Unlike previous approaches, the vehicle-to-vehicle edges of this reduced graph are fully learnable and can therefore encode traffic rules without explicit feature design, an important step towards a holistic reinforcement learning model for automated driving. We show that our proposed method outperforms precomputed handcrafted features on intersection scenarios while also learning the semantics of right-of-way rules.
AB - Graph networks have recently been used for decision making in automated driving tasks for their ability to capture a variable number of traffic participants. Current high-level graph-based approaches, however, do not model the entire road network and thus must rely on handcrafted features for vehicle-to-vehicle edges encompassing the road topology indirectly. We propose an entity-relation framework that intuitively models the road network and the traffic participants in a heterogeneous graph, representing all relevant information. Our novel architecture transforms the heterogeneous road-vehicle graph into a simpler graph of homogeneous node and edge types to allow effective training for deep reinforcement learning while introducing minimal prior knowledge. Unlike previous approaches, the vehicle-to-vehicle edges of this reduced graph are fully learnable and can therefore encode traffic rules without explicit feature design, an important step towards a holistic reinforcement learning model for automated driving. We show that our proposed method outperforms precomputed handcrafted features on intersection scenarios while also learning the semantics of right-of-way rules.
UR - http://www.scopus.com/inward/record.url?scp=85168659259&partnerID=8YFLogxK
U2 - 10.1109/ICRA48891.2023.10160762
DO - 10.1109/ICRA48891.2023.10160762
M3 - Conference contribution
AN - SCOPUS:85168659259
SN - 979-8-3503-2366-5
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 7147
EP - 7153
BT - 2023 IEEE International Conference on Robotics and Automation
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE International Conference on Robotics and Automation, ICRA 2023
Y2 - 29 May 2023 through 2 June 2023
ER -