Shared Control for Intelligent Vehicle Based on Handling Inverse Dynamics and Driving Intention

Deng, Huifan; Zhao, You Qun; Feng, Shilin; Wang, Qiuwei; Lin, Fanghua

doi:10.1109/tvt.2022.3143665

Cited by 18 publications

(4 citation statements)

References 36 publications

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“…Deng et al [24] presented a shared control framework that integrates inverse dynamics and driving intention for lane-changing systems. The Gaussian pseudo-spectral method is utilised to resolve optimisation issues in the planning process.…”

Section: Related Workmentioning

confidence: 99%

Emergency Vehicle Driving Assistance System Using Recurrent Neural Network with Navigational Data Processing Method

Anjum

Shahab

2023

Sustainability

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Emergency vehicle transportation is important for responding to and transporting individuals during emergencies. This type of transportation faces several issues, such as road safety, navigation and communication, time-critical operations, resource utilisation, traffic congestion, data processing and analysis, and individual safety. Vehicle navigation and coordination is a critical aspect of emergency response that involves guiding emergency vehicles, such as ambulances, to the location of an emergency or medical centre as quickly and safely as possible. Therefore, it requires additional effort to reduce driving risks. The roadside units support emergency vehicles and infrastructure to decrease collisions and enhance optimal navigation routes. However, during the emergency vehicle’s data communication and navigation process, communication is interrupted due to vehicle outages. Therefore, this study proposes the Navigation Data Processing for Assisted Driving (NDP-AD) method to address the problem. The proposed approach assimilates infrastructure and neighbouring location information during driving. The integrated information is processed for distance and traffic during the previous displacement interval. The NDP-AD method employs a recurrent neural network learning approach to analyse opposing vehicle distance and traffic to provide accurate, independent guidance. This effective learning-based guidance process minimises false navigations and deviation in displacement. System efficiency is evaluated based on processing latency, displacement error, data utilisation, false rate, and accuracy metrics.

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Section: Related Workmentioning

confidence: 99%

Emergency Vehicle Driving Assistance System Using Recurrent Neural Network with Navigational Data Processing Method

Anjum

Shahab

2023

Sustainability

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“…In all of these developments, the most important factor that manufacturers try to optimize is the driving experience evaluated by the driver. Driving experience is a multidimensional construct [1][2][3] , which from a human factor's perspective is shaped by many different aspects, including driving performance 4 , driving experience 5 , age 5,6 , personality traits 5 , as well as fatigue 7 , emotions 8 , behaviors 8 , and intention 9 . Importantly, however, the first step for any of these aspects consists of the various perceptual inputs generated by the driving environment and processed via the different sensory modalities.…”

Section: Failure To Integrate? Investigating Multisensory Acceleratio...mentioning

confidence: 99%

Failure to integrate? Investigating multisensory acceleration perception on a cable-robot simulator

Kang¹,

Yang

Boss

et al. 2022

Preprint

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Current powertrains in vehicles offer a broad spectrum of driving modes that shape the driving experience. One crucial factor for determining the human factors aspects of this driving experience is the perception and evaluation of acceleration-a complex, multisensory process that integrates auditory, vestibular, and visual input. Two important questions that need to be answered in order to better characterize this percept include: How do individual sensory inputs influence acceleration perception? and How does acceleration perception change at different acceleration levels? To address these questions, here we used a unique setup based on a cable-robot simulator that allowed us to manipulate the different modalities at different levels of acceleration with real-world, in-car data for maximum realism. Specifically, we measured the just noticeable differences (JNDs) of acceleration perception in five different modality combinations with the same set of participants. Our results showed that auditory acceleration perception was less sensitive compared to other modality combinations. In addition, we found evidence for the validity of Weber’s law with JNDs increasing linearly with increasing acceleration level. Interestingly, the multisensory data showed little evidence for effective cue integration of auditory information in this setup. These findings lay the groundwork for a better understanding of how different modalities work together in acceleration perception.

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“…Examples include a sliding mode control algorithm, 21 a robust indirect control approach, 22 a Lyapunov-based controller with a metaheuristic optimization method, 23 and an anti-disturbance algorithm 24 that addresses model uncertainty and unknown marine environmental disturbances that affect lateral vehicle motion. In addition, a novel shared control technique was suggested in Deng et al 25 to address inverse dynamics and driving intentions. Jiang and Astolfi 26 suggested backstepping and forwarding control designs to address the underactuated issues of a class of nonlinear, underacted systems concerning nonlinear lateral dynamics control.…”

Section: Introductionmentioning

confidence: 99%

Robust model-free adaptive iterative learning control for an autonomous bus trajectory tracking system

Liu,

Huang,

et al. 2024

Science Progress

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A robust model-free adaptive iterative learning control (R-MFAILC) algorithm is proposed in this work to address the issue of laterally controlling an autonomous bus. First, according to the periodic repetitive working characteristics of autonomous buses, a novel dynamic linearized method used in the iterative domain is utilized, and a time-varying data model with a pseudo gradient (PG) is given. Then, the R-MFAILC controller is designed with a proposed adaptive attenuation factor. The proposed algorithm's advantage lies in the R-MFAILC controller, which solely utilizes the input and output data of the regulated entity. Moreover, the R-MFAILC controller has strong robustness and can handle the nonlinear measurement disturbances of the system. In simulations based on the Truck-Sim simulation platform, the effectiveness of the proposed algorithm is verified. A rigorous mathematical analysis is employed to demonstrate the stability and convergence of the proposed algorithm.

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Shared Control for Intelligent Vehicle Based on Handling Inverse Dynamics and Driving Intention

Cited by 18 publications

References 36 publications

Emergency Vehicle Driving Assistance System Using Recurrent Neural Network with Navigational Data Processing Method

Emergency Vehicle Driving Assistance System Using Recurrent Neural Network with Navigational Data Processing Method

Failure to integrate? Investigating multisensory acceleration perception on a cable-robot simulator

Robust model-free adaptive iterative learning control for an autonomous bus trajectory tracking system

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