Mar 17, 2014 · In driving a vehicle, the driver makes his/her driving intentions and selects a series of operation behaviors that are most suitable for the current driving conditions. Even very simple driving intentions (long-term driving intention) can be subdivided into a series of simpler driving operation behaviors (short-term driving behavior); that is
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May 11, 2021 · W Yang, B Wan, X Qu. A forward collision warning system using driving intention recognition of the front vehicle and V2V communication. IEEE Access, 2020, 8: 11268-11278. Article Google Scholar [67] S Jia, F Hui, S Li, et al. Long short-term memory and convolutional neural network for abnormal driving behaviour recognition.
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Autonomous vehicles need to perform social accepted behaviors in complex urban scenarios including human-driven vehicles with uncertain intentions. This leads to many difficult decision-making problems, such as deciding a lane change maneuver and generating policies to pass through intersections. In this paper, we propose an intention-aware decision-making algorithm to solve this challenging
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Gaussian mixture model which proved that the method is able Lane change behavior estimation or intention recognition steering wheel angles, etc.) to identify driving behavior. It is
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Nevertheless, the driver's intentions are directly reflected in the accelerator pedal, brake pedal and hydraulic valve control handle. By detecting these observable signals such as the signals of acceleration pedal's displacement and velocity, brake pedal's displacement and velocity and valve status Gaussian Mixture – Hidden Markov Model(MGHMM) can recognize the unobservable driving intentions.
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Clas Veibäck, Jonatan Olofsson, Tom Rune Lauknes, Gustaf Hendeby, "Learning Target Dynamics While Tracking Using Gaussian Processes", IEEE Transactions on Aerospace and Electronic Systems, 56 (4): 2591-2602, 2020.
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intention recognition and driving behaviour prediction, in following and pedal/steering wheel operation patterns will be component Gaussian Mixture Models (GMM), applied on a 0.32-s frame length, are used for data modelling. The model
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May 11, 2021 · W Yang, B Wan, X Qu. A forward collision warning system using driving intention recognition of the front vehicle and V2V communication. IEEE Access, 2020, 8: 11268-11278. Article Google Scholar [67] S Jia, F Hui, S Li, et al. Long short-term memory and convolutional neural network for abnormal driving behaviour recognition.
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Jun 10, 2020 · Deep Learning with Attention Mechanism for Predicting Driver Intention at Intersection. In this paper, a driver's intention prediction near a road intersection is proposed. Our approach uses a deep bidirectional Long Short-Term Memory (LSTM) with an attention mechanism model based on a hybrid-state system (HSS) framework.
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Wheel Loader Driving Intention Recognition with Gaussian Mixture - Hidden Markov Model . By Guoxiang Cao, Anlin Wang and Donghuan Xu. Cite . BibTex; Full citation Abstract. Accurate recognition of driving intentions can delay upshifts under the intention of quick acceleration to maximize vehicle power performance; avoid frequent gear changes in
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The Gaussian Mixture Model (GMM) is one of the widely-used generative models in driving behavior modeling [18]. Early Recognition of Driving Intention for Lane Change Based on Recurrent Hidden
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1 INTRODUCTION. According to the statistical data of the World Health Organization, over 90% of traffic casualties were directly or indirectly caused by human factors, such as drunk, fatigue, distraction, mal-operation [1, 2].However, traffic accidents can be expected to be reduced via the effective driver activity recognition and giving hints or corrections accordingly while driving [3-5
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Wheel Loader Driving Intention Recognition with Gaussian Mixture - Hidden Markov Model. Article. brake pedal's displacement and velocity and valve status Gaussian Mixture – Hidden Markov
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Driver turning intention recognition, Gaussian mixture clustering, Gaussian mixture hidden Markov model, generalized growing and pruning algorithm for radial basis function neural network, model
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MATEC Web of Conferences (Jan 2018) . Wheel Loader Driving Intention Recognition with Gaussian Mixture - Hidden Markov Model
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Apr 28, 2021 · Considering the driver's driving style, Li et al. proposed a lane-changing intention estimation model based on Bayesian network and Gaussian mixture model, which achieved a good prospective time, but its accuracy is low. However, the lane-changing process is determined by both the driver and traffic environment, but the aforementioned
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Wheel Loader Driving Intention Recognition with Gaussian Mixture - Hidden Markov Model A double-layer brake driving intention recognition model based on hidden Markov theory was set up and
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Driving Style Recognition Using Interval Type-2 Fuzzy Inference affect the control of the vehicle's steering wheel, throttle, brake pedal, and other peripherals. As stated in Sagberg et al. (2015), detecting driving styles it can be used to predict drivers' intentions and their trajectories (Xing, Lv, & Cao, 2019; Huang, 2019). Fuel-
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Apr 03, 2019 · A braking intention identification method based on empirical mode decomposition (EMD) algorithm and entropy theory for electric vehicles is proposed. EMD algorithm is given to decompose nonstationary brake pedal signal to stationary intrinsic mode function (IMF), which is the base of data mining. After that, entropy theory is used to extract brake pedal signal features.
Learn MoreWheel Loader Driving Intention Recognition with Gaussian Mixture - Hidden Markov Model . By Guoxiang Cao, Anlin Wang and Donghuan Xu. Cite . BibTex; Full citation Abstract. Accurate recognition of driving intentions can delay upshifts under the intention of quick acceleration to maximize vehicle power performance; avoid frequent gear changes in
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