TY - GEN
T1 - Fail-Safe Decision Architecture for Positioning Failures on Automated Vehicles
AU - Rodriguez-Arozamena, Mario
AU - Aranguren-Mendieta, Inigo
AU - Perez, Joshue
AU - Zubizarreta, Asier
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Precise localization is essential for the operation of Connected and Automated Vehicles (CAVs) in urban scenarios. Camera and LiDAR-based solutions are currently used in some of the CAVs around the world, but they entail an expensive performance in terms of computational time and economic cost. Due to this, Global Navigation Satellite System (GNSS) based solutions remains the preferred solution for positioning Automated Vehicles in different Operational Design Domains (ODDs). Although several improvements in system reliability have been made recently, in case of positioning failures most of the architectures rely on system redundancy or automation disengagements to achieve minimal risk conditions. This paper presents a fail-safe decision architecture for mitigating positioning failures of automated vehicles in urban scenarios. The proposed architecture utilizes a combination of sensor fusion localization and decision-making modules to ensure the safe and efficient operation of the vehicle in the event of a positioning failure. The proposed approach is evaluated through simulation in a representative urban scenario and is shown to effectively handle positioning failures, improving the localization accuracy provided by each information source.
AB - Precise localization is essential for the operation of Connected and Automated Vehicles (CAVs) in urban scenarios. Camera and LiDAR-based solutions are currently used in some of the CAVs around the world, but they entail an expensive performance in terms of computational time and economic cost. Due to this, Global Navigation Satellite System (GNSS) based solutions remains the preferred solution for positioning Automated Vehicles in different Operational Design Domains (ODDs). Although several improvements in system reliability have been made recently, in case of positioning failures most of the architectures rely on system redundancy or automation disengagements to achieve minimal risk conditions. This paper presents a fail-safe decision architecture for mitigating positioning failures of automated vehicles in urban scenarios. The proposed architecture utilizes a combination of sensor fusion localization and decision-making modules to ensure the safe and efficient operation of the vehicle in the event of a positioning failure. The proposed approach is evaluated through simulation in a representative urban scenario and is shown to effectively handle positioning failures, improving the localization accuracy provided by each information source.
KW - automated driving
KW - fail-safe operation
KW - fallback strategy
KW - positioning failure
KW - safe state
UR - https://www.scopus.com/pages/publications/85187336384
U2 - 10.1109/SWC57546.2023.10449319
DO - 10.1109/SWC57546.2023.10449319
M3 - Conference contribution
AN - SCOPUS:85187336384
T3 - Proceedings - 2023 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Autonomous and Trusted Vehicles, Scalable Computing and Communications, Digital Twin, Privacy Computing and Data Security, Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PCDS/Metaverse 2023
BT - Proceedings - 2023 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Autonomous and Trusted Vehicles, Scalable Computing and Communications, Digital Twin, Privacy Computing and Data Security, Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PCDS/Metaverse 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th IEEE Smart World Congress, SWC 2023
Y2 - 28 August 2023 through 31 August 2023
ER -