TY - JOUR
T1 - SL1-Simplex
T2 - Safe Velocity Regulation of Self-Driving Vehicles in Dynamic and Unforeseen Environments
AU - Mao, Yanbing
AU - Gu, Yuliang
AU - Hovakimyan, Naira
AU - Sha, Lui
AU - Voulgaris, Petros G
N1 - Funding Information:
This work was supported by NSF (award numbers CMMI-1663460, ECCS-1739732 and CPS-1932529).
Publisher Copyright:
© 2023 Association for Computing Machinery.
PY - 2023/2/20
Y1 - 2023/2/20
N2 - This article proposes a novel extension of the Simplex architecture with model switching and model learning to achieve safe velocity regulation of self-driving vehicles in dynamic and unforeseen environments. To guarantee the reliability of autonomous vehicles, an gL1 adaptive controller that compensates for uncertainties and disturbances is employed by the Simplex architecture as a verified high-assurance controller (HAC) to tolerate concurrent software and physical failures. Meanwhile, the safe switching controller is incorporated into the HAC for safe velocity regulation in the dynamic (prepared) environments, through the integration of the traction control system and anti-lock braking system. Due to the high dependence of vehicle dynamics on the driving environments, the HAC leverages the finite-time model learning to timely learn and update the vehicle model for gL1 adaptive controller, when any deviation from the safety envelope or the uncertainty measurement threshold occurs in the unforeseen driving environments. With the integration of gL1 adaptive controller, safe switching controller and finite-time model learning, the vehicle's angular and longitudinal velocities can asymptotically track the provided references in the dynamic and unforeseen driving environments, while the wheel slips are restricted to safety envelopes to prevent slipping and sliding. Finally, the effectiveness of the proposed Simplex architecture for safe velocity regulation is validated by the AutoRally platform.
AB - This article proposes a novel extension of the Simplex architecture with model switching and model learning to achieve safe velocity regulation of self-driving vehicles in dynamic and unforeseen environments. To guarantee the reliability of autonomous vehicles, an gL1 adaptive controller that compensates for uncertainties and disturbances is employed by the Simplex architecture as a verified high-assurance controller (HAC) to tolerate concurrent software and physical failures. Meanwhile, the safe switching controller is incorporated into the HAC for safe velocity regulation in the dynamic (prepared) environments, through the integration of the traction control system and anti-lock braking system. Due to the high dependence of vehicle dynamics on the driving environments, the HAC leverages the finite-time model learning to timely learn and update the vehicle model for gL1 adaptive controller, when any deviation from the safety envelope or the uncertainty measurement threshold occurs in the unforeseen driving environments. With the integration of gL1 adaptive controller, safe switching controller and finite-time model learning, the vehicle's angular and longitudinal velocities can asymptotically track the provided references in the dynamic and unforeseen driving environments, while the wheel slips are restricted to safety envelopes to prevent slipping and sliding. Finally, the effectiveness of the proposed Simplex architecture for safe velocity regulation is validated by the AutoRally platform.
KW - Simplex
KW - anti-lock braking system
KW - gLadaptive controller
KW - model learning
KW - model switching
KW - safe velocity regulation
KW - traction control system
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U2 - 10.1145/3564273
DO - 10.1145/3564273
M3 - Article
AN - SCOPUS:85151848062
SN - 2378-962X
VL - 7
JO - ACM Transactions on Cyber-Physical Systems
JF - ACM Transactions on Cyber-Physical Systems
IS - 1
M1 - 2
ER -