Realization of Emergency Lane Keeping System by Adaptive Control based on the Finite State Machine

Liang, Jun; Yang, Yanding; Xiao-lin, Zhu; Sheng, Kai

doi:10.1109/cvci54083.2021.9661249

Cited by 2 publications

(1 citation statement)

References 5 publications

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“…It promotes comfort and safety by supporting the driver with lateral control tasks [4]. Lane Keeping Assist System (LKAS) solutions detect unintentional lane drift by adjusting the steering to keep the vehicle within its lane [5]. A forward-looking camera serves as a sensor input for LKAS, detecting lines and lane features.…”

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confidence: 99%

A Comprehensive Analysis of Model Predictive Control for Lane Keeping Assist System

Montoya,

Teixeira,

Murilo

et al. 2023

IEEE Access

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Lane Keeping Assist System (LKAS) enhances comfort and safety while driving. It plays a significant role in the Advanced Driver Assistance System (ADAS) and future Automated Driving (AD). The LKAS solution aims to help the driver keep the vehicle within the road lines, preventing unintentional lane departure. Despite LKAS being an important solution for comfortable driving, robust LKAS steering control is still lacking, requiring constant driver intervention or premature LKAS deactivation. LKAS require optimal control solutions with real-time constraints. This paper comprehensively analyzes Model Predictive Control (MPC) for real-time LKAS applications. Classical and parameterized MPC schemes with distinct Quadratic Programming (QP) solvers are combined to evaluate LKAS closed-loop control performance and realtime constraints. A sideslip and lateral speed bicycle modes were used to evaluate classical, trivial, and exponential MPC schemes. Experimental results highlight the three MPC and QP-appropriate solutions with satisfactory reference tracking without steering command and real-time constraints violation. INDEX TERMS Advanced Driver Assistance Systems, Lane Keeping Assist System, Model Predictive Control, MPC parameterization, Quadratic Programming. I. INTRODUCTIONV EHICLE safety encompasses regulations, actions, and technological solutions designed to prevent traffic accidents [1]. Today, there is a global effort involving multiple sectors, including transport, health, education, etc., to address safety on roads [2]. Carmakers and governments worldwide are actively seeking innovative solutions to enhance the safety of all road users. Active safety systems such as Antilock Braking Systems (ABS), Electronic Stability Control (ESC), Autonomous Emergency Braking (AEBS), and Lane Assist Systems (LAS) are employed to prevent accidents from occurring altogether, sometimes actively assisting the driver in mitigating the impact. These Advanced Driver Assistance Systems (ADAS) are often implemented to be forgiving of human error.

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confidence: 99%

A Comprehensive Analysis of Model Predictive Control for Lane Keeping Assist System

Montoya,

Teixeira,

Murilo

et al. 2023

IEEE Access

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A Novel Approach for the Safety Validation of Emergency Intervention Functions Using Extreme Value Estimation

Schrimpf,

Betschinske,

Peters

2024

SAE Technical Paper Series

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<div class="section abstract"><div class="htmlview paragraph">As part of the safety validation of advanced driver assistance systems (ADAS) and automated driving (AD) functions, it is necessary to demonstrate that the frequency at which the system exhibits hazardous behavior (HB) in the field is below an acceptable threshold. This is typically tested by observation of the system behavior in a field operational test (FOT). For situations in which the system under test (SUT) actively intervenes in the dynamic driving behavior of the vehicle, it is assessed whether the SUT exhibits HB. Since the accepted threshold values are generally small, the amount of data required for this strategy is usually very large. This publication proposes an approach to reduce the amount of data required for the evaluation of emergency intervention systems with a state machine based intervention logic by including the time periods between intervention events in the validation process. For this purpose, a proximity measure that indicates how close the system is to an intervention at each point in time during the test drive is proposed. The application of this proximity measure and the definition of a corresponding threshold value makes it possible to expand the set of observable intervention events by events in which the system is close to an intervention. Thus, a subsequent assessment of these additional events regarding HB enables the data basis to be expanded to include events in which the system is close to exhibiting HB. This additional information is intended to be leveraged in the application of an extreme value estimator for deriving an estimate of the frequency at which the system is expected to exhibit HB on longer test distances. This publication focuses primarily on deriving and demonstrating the described proximity measure and provides an outlook on further steps required to validate the proposed approach.</div></div>

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Realization of Emergency Lane Keeping System by Adaptive Control based on the Finite State Machine

Cited by 2 publications

References 5 publications

A Comprehensive Analysis of Model Predictive Control for Lane Keeping Assist System

A Comprehensive Analysis of Model Predictive Control for Lane Keeping Assist System

A Novel Approach for the Safety Validation of Emergency Intervention Functions Using Extreme Value Estimation

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