TY - GEN
T1 - Evaluating Location Compliance Approaches for Automated Road Vehicles
AU - Zhu, Alexander
AU - Manzinger, Stefanie
AU - Althoff, Matthias
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/18
Y1 - 2018/10/18
N2 - This work presents techniques for efficiently checking location compliance of automated road vehicles. We refer to location compliance as an allowed translational and rotational positioning of a vehicle on a road network, i.e., the vehicle does not enter forbidden lanes or regions reserved for other traffic participants, such as bike lanes or dedicated bus routes. Previous work has mostly focused on efficient collision detection between traffic participants and static obstacles represented as bounded sets. We formulate location compliance as a set enclosure problem, which cannot be solved directly with collision detection; thus, different algorithms from computational geometry have to be applied. We present polygon enclosure and boundary mesh generation approaches and evaluate them using existing road geometries from the CommonRoad database. For a fair comparison, we generate thousands of random instances which are evaluated statistically.
AB - This work presents techniques for efficiently checking location compliance of automated road vehicles. We refer to location compliance as an allowed translational and rotational positioning of a vehicle on a road network, i.e., the vehicle does not enter forbidden lanes or regions reserved for other traffic participants, such as bike lanes or dedicated bus routes. Previous work has mostly focused on efficient collision detection between traffic participants and static obstacles represented as bounded sets. We formulate location compliance as a set enclosure problem, which cannot be solved directly with collision detection; thus, different algorithms from computational geometry have to be applied. We present polygon enclosure and boundary mesh generation approaches and evaluate them using existing road geometries from the CommonRoad database. For a fair comparison, we generate thousands of random instances which are evaluated statistically.
UR - http://www.scopus.com/inward/record.url?scp=85055621940&partnerID=8YFLogxK
U2 - 10.1109/IVS.2018.8500720
DO - 10.1109/IVS.2018.8500720
M3 - Conference contribution
AN - SCOPUS:85055621940
T3 - IEEE Intelligent Vehicles Symposium, Proceedings
SP - 642
EP - 649
BT - 2018 IEEE Intelligent Vehicles Symposium, IV 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE Intelligent Vehicles Symposium, IV 2018
Y2 - 26 September 2018 through 30 September 2018
ER -