Dynamic Conflict Mitigation for Cooperative Driving Control of Intelligent Vehicles

Oudainia, Mohamed Radjeb; Sentouh, Chouki; Nguyen, Anh‐Tu; Popieul, Jean‐Christophe

doi:10.1109/iv51971.2022.9827379

Cited by 3 publications

(2 citation statements)

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“…In, (Wada et al, 2016;Saito et al, 2018) based on cooperative status detection, a conflict free smooth transition of authority between human driver and autonomous controller was proposed. Similarly in (Oudainia et al, 2022), conflict mitigation by adapting the cost function objective was proposed. Extending the work of (Nguyen et al, 2015), a cooperative control approach employing T-S models was proposed in (Nguyen et al, 2017) to perform lane keeping and conflict minimization simultaneously.…”

Section: State Of the Artmentioning

confidence: 99%

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Online driver model parameter identification using the Lyapunov approach based shared control

Oudainia¹,

Sentouh²,

Nguyen³

et al. 2022

Front. Control Eng.

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The work described in this paper proposes a new conflict minimisation strategy in shared driving control for lane keeping systems (LKS) in intelligent vehicles. This strategy takes into account a dynamic driver model, where the driver’s parameters are identified online using the Lyapunov approach. The design of an adaptive shared controller is based on the dynamic parameters of the driver model which changes according to the driver and the situation encountered. Based on Lyapunov stability arguments, the overall asymptotic stability of the closed-loop control system with the adaptive driver model and the variation of the vehicle speed is proved and an LMI optimization is used to formulate the control design. The simulation results, conducted with the SHERPA dynamic car simulator under real-world driving situations, show the importance of integrating a dynamic driver model in the controller design in order to decrease the conflict between the driver and the lane keeping system and to ensure the safety of the vehicle as well as to increase the confidence and acceptability of the driver.

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Section: State Of the Artmentioning

confidence: 99%

“…It aims to integrate the driver's adaptation in the level of sharing over the driving task using cybernetic driver model. The control authority transition from automated functionality to a human driver is particularly studied in (Lv et al, 2021;Oudainia et al, 2022). This helps drivers to take control of the vehicle progressively.…”

Section: State Of the Artmentioning

confidence: 99%

Online driver model parameter identification using the Lyapunov approach based shared control

Oudainia¹,

Sentouh²,

Nguyen³

et al. 2022

Front. Control Eng.

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Adaptive Cost Function-Based Shared Driving Control for Cooperative Lane-Keeping Systems With User-Test Experiments

Oudainia,

Sentouh,

Nguyen

et al. 2024

IEEE Trans. Intell. Veh.

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Multi-Objective Collaborative Control Method for Multi-Axle Distributed Vehicle Assisted Driving

Wang

et al. 2023

Applied Sciences

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For human–machine collaborative driving conditions, a hierarchical chassis multi-objective cooperative control method is proposed in this paper. Firstly, based on the phase plane theory, vehicle dynamics analysis is carried out to complete the definition of vehicle stability region. Secondly, based on the linear time-varying (LTV) system model, a cooperative control strategy combining fuzzy control with model predictive control (MPC) is proposed in the upper layer. In this strategy, the assisted driving weight adjustment coefficient and the stability weight adjustment coefficient are obtained by fuzzy mapping combining human–machine cooperation index and the vehicle stability region, respectively, and the optimization objectives of MPC are designed based on the above coefficients. In the lower layer torque allocation strategy, the stability weight adjustment coefficient is introduced to achieve multi-objective optimization of tire load rate and energy efficiency. For energy efficiency optimization, an optimal energy efficiency point-based tracking method is proposed to avoid nonlinearity caused by the introduction of motor loss models. Simulation analysis results show that the proposed strategy can effectively alleviate human–machine conflicts and improve vehicle handing stability. It also can achieve smaller tire load rate optimization through torque allocation and can reduce energy consumption by approximately 8% compared with the inter-axle torque allocation strategy. This study helps to promote the improvement of the comprehensive performance of assisted driving vehicles in human–machine cooperation, handling stability, and energy-saving torque distribution.

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Dynamic Conflict Mitigation for Cooperative Driving Control of Intelligent Vehicles

Cited by 3 publications

References 0 publications

Online driver model parameter identification using the Lyapunov approach based shared control

Online driver model parameter identification using the Lyapunov approach based shared control

Adaptive Cost Function-Based Shared Driving Control for Cooperative Lane-Keeping Systems With User-Test Experiments

Multi-Objective Collaborative Control Method for Multi-Axle Distributed Vehicle Assisted Driving

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