Intelligent-Assist Algorithm for Remote Shared-Control Driving Based on Game Theory

Qiao, Bangjun; Li, Huanghe; Wu, Xiaodong

doi:10.1007/s12204-021-2351-z

Cited by 7 publications

(4 citation statements)

References 14 publications

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“…Nash game is used to help determine the degree of intervention and driver's attention assessment. The simulation experiment verifies that the method proposed in this study can assist the driver in decision-making and improve driving safety [6]. Zhang The method is based on super torsion Sliding mode control, and Lyapunov stability analysis is carried out to ensure the gradual convergence of the error.…”

Section: Related Workmentioning

confidence: 66%

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Neural Network Intelligent Control Based on MPSO

Aijun

2023

IEEE Access

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With the increasing complexity of mechanical equipment, the control effectiveness of traditional intelligent control systems can no longer meet the needs of modern industrial production. In order to reduce errors in intelligent control systems while ensuring system performance, this study proposes a new Particle Swarm Optimization (PSO) optimization scheme. The study simplified the PSO algorithm from three aspects: algorithm parameters, speed, and position formula, and corrected the formulas for individual optimal values and global optimal values. Research will name the optimized algorithm Modified PSO (MPSO). On the basis of the MPSO algorithm, neural network intelligent control has been innovatively improved. In the experimental results, the MPSO optimized controller controlled the error within 0.01 within 0.02 seconds. At this time, the Whale Optimization Algorithm (WOA) optimized error was 0.072, and the PSO optimized error was 0.478. Compared to PSO and WOA, the control error of MPSO has decreased by 98.95% and 93.06%, respectively. In addition, the proposed method not only has the best control effect, but also has the shortest system response time, with an average time of 1.294 seconds. Compared to PSO and WOA optimization, it reduces by 61.48% and 43.07%, respectively.The results verified that the proposed method in this study can effectively improve the accuracy of intelligent control and control the error within the target range within 0.02 seconds. The research not only simplifies the calculation of the PSO algorithm, but also effectively reduces the error of the algorithm, providing a reference for research in the field of intelligent control.INDEX TERMS Intelligent control, nonlinear problems, neural network, particle swarm optimization (PSO).AIJUN KOU was born in Linyi, Shandong, China, in 1985. He received the master's degree from Jiangsu University, China. He is currently with the School of Electronic Information, Huzhou College. His research interests include artificial intelligence, machine learning, and computer vision.

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Section: Related Workmentioning

confidence: 66%

“…When the parameters a, b take different values, the probability density function can fit different function shapes. The expression of inertia weight after introducing BD is shown in equation (6).…”

Section: Neural Network Intelligent Control Based On Mpso Optimizatio...mentioning

confidence: 99%

Neural Network Intelligent Control Based on MPSO

Aijun

2023

IEEE Access

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“…Schimpe and Diermeyer [18] proposed an MPC-based shared steering control for obstacle avoidance, modeling obstacles as repulsive potential fields. Qiao et al [19] developed a humanmachine interaction model using Nash equilibrium-based noncooperative games. Schitz et al [20] introduced an MPCbased assistance approach in cruise control mode.…”

Section: A Related Workmentioning

confidence: 99%

Performance of Successive Reference Pose Tracking vs Smith Predictor Approach for Direct Vehicle Teleoperation Under Variable Network Delays

Prakash,

Vignati,

Sabbioni

2024

IEEE Trans. Veh. Technol.

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“…Firstly, the concept of direct control which represents a human operator handling the sensing data, and the planning of the vehicle trajectory, directly provides control to the vehicle [23,24]. Second, shared control allows remote drivers to gain control of autonomous vehicles through control commands such as steering, brake, and throttle [22,25]. Finally, the trajectory guidance allows the vehicle to drive through the route presented by the remote driver received from the estimated route and trajectory planning [22].…”

Section: Introductionmentioning

confidence: 99%

Authority Transfer According to a Driver Intervention Intention Considering Coexistence of Communication Delay

Lim,

Hwang,

Kim

et al. 2023

Computers

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Recently, interest and research on autonomous driving technology have been actively conducted. However, proving the safety of autonomous vehicles and commercializing autonomous vehicles remain key challenges. According to a report released by the California Department of Motor Vehicles on self-driving, it is still hard to say that self-driving technology is highly reliable. Until fully autonomous driving is realized, authority transfer to humans is necessary to ensure the safety of autonomous driving. Several technologies, such as teleoperation and haptic-based approaches, are being developed based on human-machine interaction systems. This study deals with teleoperation and presents a way to switch control from autonomous vehicles to remote drivers. However, there are many studies on how to do teleoperation, but not many studies deal with communication delays that occur when switching control. Communication delays inevitably occur when switching control, and potential risks and accidents of the magnitude of the delay cannot be ignored. This study examines compensation for communication latency during remote control attempts and checks the acceptable level of latency for enabling remote operations. In addition, supplemented the safety and reliability of autonomous vehicles through research that reduces the size of communication delays when attempting teleoperation. It is expected to prevent human and material damage in the actual accident situation.

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Intelligent-Assist Algorithm for Remote Shared-Control Driving Based on Game Theory

Cited by 7 publications

References 14 publications

Neural Network Intelligent Control Based on MPSO

Neural Network Intelligent Control Based on MPSO

Performance of Successive Reference Pose Tracking vs Smith Predictor Approach for Direct Vehicle Teleoperation Under Variable Network Delays

Authority Transfer According to a Driver Intervention Intention Considering Coexistence of Communication Delay

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