TY - GEN
T1 - Train Here, Drive There
T2 - 21st International Workshop of Physical Agents, WAF 2020
AU - Gómez-Huélamo, Carlos
AU - Del Egido, Javier
AU - Bergasa, Luis M.
AU - Barea, Rafael
AU - López-Guillén, Elena
AU - Arango, Felipe
AU - Araluce, Javier
AU - López, Joaquín
N1 - Publisher Copyright:
© 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - This work presents the validation of our fully-autonomous driving architecture in the CARLA open-source simulator, by using some challenging driving scenarios inspired on the CARLA Autonomous Driving Challenge (CADC), focusing on our decision-making layer, based on Hierarchical Interpreted Binary Petri Nets (HIBPN). First, our ROS (Robot Operating System) based autonomous driving architecture is introduced. Second, the CARLA simulator is described, outlining the steps conducted to merge our architecture with this simulator and the advantages to create ad-hoc driving scenarios for use cases validation. Finally, the paper validates the architecture by means of some challenging driving scenarios such as: Stop, Pedestrian Crossing, Adaptive Cruise Control (ACC) and Unexpected Pedestrian. Some qualitative (video files) and quantitative (trajectory and linear velocity segmented with its corresponding Petri Net states) results are presented for each use case, validating our architecture in simulation as a preliminary stage before implementing it in our real autonomous electric car.
AB - This work presents the validation of our fully-autonomous driving architecture in the CARLA open-source simulator, by using some challenging driving scenarios inspired on the CARLA Autonomous Driving Challenge (CADC), focusing on our decision-making layer, based on Hierarchical Interpreted Binary Petri Nets (HIBPN). First, our ROS (Robot Operating System) based autonomous driving architecture is introduced. Second, the CARLA simulator is described, outlining the steps conducted to merge our architecture with this simulator and the advantages to create ad-hoc driving scenarios for use cases validation. Finally, the paper validates the architecture by means of some challenging driving scenarios such as: Stop, Pedestrian Crossing, Adaptive Cruise Control (ACC) and Unexpected Pedestrian. Some qualitative (video files) and quantitative (trajectory and linear velocity segmented with its corresponding Petri Net states) results are presented for each use case, validating our architecture in simulation as a preliminary stage before implementing it in our real autonomous electric car.
KW - Autonomous vehicles
KW - CARLA
KW - Decision-making
KW - ROS
KW - Simulation
KW - Use cases
UR - https://www.scopus.com/pages/publications/85097438707
U2 - 10.1007/978-3-030-62579-5_4
DO - 10.1007/978-3-030-62579-5_4
M3 - Conference contribution
AN - SCOPUS:85097438707
SN - 9783030625788
T3 - Advances in Intelligent Systems and Computing
SP - 44
EP - 59
BT - Advances in Physical Agents II - Proceedings of the 21st International Workshop of Physical Agents WAF 2020
A2 - Bergasa, Luis M.
A2 - Ocaña, Manuel
A2 - Barea, Rafael
A2 - López-Guillén, Elena
A2 - Revenga, Pedro
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 19 November 2020 through 20 November 2020
ER -
