A Stochastic Driver Pedal Behavior Model Incorporating Road Information

Zeng, Xiangrui; Wang, Junmin

doi:10.1109/thms.2017.2674301

Cited by 48 publications

(29 citation statements)

References 27 publications

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“…Real pedal actions S vel by the human driver and the vehicle state data are sent to the DOIP algorithm for online optimization, then a real optimal control signal will be sent back to the powertrain for the energy distribution. 0.1 s is chosen according to [29] as the sampling time k, which is approved to be able to track the system dynamics while reserving enough time slot for algorithm computing. The mechanism of the DOIP for the PHEV system is shown in Fig.…”

Section: Dual-loop Online Intelligent Programmingmentioning

confidence: 99%

See 1 more Smart Citation

Dual-loop online intelligent programming for driver-oriented predict energy management of plug-in hybrid electric vehicles

Zhou

et al. 2019

Applied Energy

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Section: Dual-loop Online Intelligent Programmingmentioning

confidence: 99%

“…The DP as a representative of global optimization algorithms usually depends on a model to provide a provably optimal control strategy by searching all state and control grids exhaustively [15]. However, DP or GA is not applicable to real-time problems since the exact future driving information is seldom known in the real world [29].…”

Section: Introductionmentioning

confidence: 99%

Dual-loop online intelligent programming for driver-oriented predict energy management of plug-in hybrid electric vehicles

Zhou

et al. 2019

Applied Energy

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“…In their following study [15], the authors analyze foot trajectories from a driving simulator study, and use Functional Principal Component Analysis (FPCA) to detect unique patterns associated with early foot movements that might indicate pedal errors. Inspired by previous work, the Zeng et al [16] also incorporated vehicle and road information by looking outside the vehicle to model driver pedal behavior using an Input-Output HMM (IOHMM). Unlike most other methods that make use of potentially privacy limiting video sensors, the authors in [17] use capacitive proximity sensors to recognize four different foot gestures.…”

Section: Related Researchmentioning

confidence: 99%

Forced Spatial Attention for Driver Foot Activity Classification

Rangesh

Trivedi

2019

2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)

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This paper provides a simple solution for reliably solving image classification tasks tied to spatial locations of salient objects in the scene. Unlike conventional image classification approaches that are designed to be invariant to translations of objects in the scene, we focus on tasks where the output classes vary with respect to where an object of interest is situated within an image. To handle this variant of the image classification task, we propose augmenting the standard cross-entropy (classification) loss with a domain dependent Forced Spatial Attention (FSA) loss, which in essence compels the network to attend to specific regions in the image associated with the desired output class. To demonstrate the utility of this loss function, we consider the task of driver foot activity classification -where each activity is strongly correlated with where the driver's foot is in the scene. Training with our proposed loss function results in significantly improved accuracies, better generalization, and robustness against noise, while obviating the need for very large datasets.

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“…Supervised and unsupervised machine learning methods, including support vector machine (SVM), dynamic Bayes, neural networks, Markov model, K-means, and Gaussian mixture model (GMM) were employed to recognize brake intention and intensity [14][15][16][17][18][19][20]. For instance, cause and effect analysis of accelerator and brake pedal dynamics during an emergency braking scenario was conducted and SVM was employed to recognize emergency braking intention, so as to shorten the driver's response time and reduce the braking distance [14].…”

Section: Introductionmentioning

confidence: 99%

Control Oriented Prediction of Driver Brake Intention and Intensity Using a Composite Machine Learning Approach

et al. 2019

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Driver perception, decision, and control behaviors are easily affected by traffic conditions and driving style, showing the tendency of randomness and personalization. Brake intention and intensity are integrated and control-oriented parameters that are crucial to the development of an intelligent braking system. In this paper, a composite machine learning approach was proposed to predict driver brake intention and intensity with a proper prediction horizon. Various driving data were collected from Controller Area Network (CAN) bus under a real driving condition, which mainly contained urban and rural road types. ReliefF and RReliefF (they don’t have abbreviations) algorithms were employed as feature subset selection methods and applied in a prepossessing step before the training. The rank importance of selected predictors exhibited different trends or even negative trends when predicting brake intention and intensity. A soft clustering algorithm, Fuzzy C-means, was adopted to label the brake intention into categories, namely slight, medium, intensive, and emergency braking. Data sets with misplaced labels were used for training of an ensemble machine learning method, random forest. It was validated that brake intention could be accurately predicted 0.5 s ahead. An open-loop nonlinear autoregressive with external input (NARX) network was capable of learning the long-term dependencies in comparison to the static neural network and was suggested for online recognition and prediction of brake intensity 1 s in advance. As system redundancy and fault tolerance, a close-loop NARX network could be adopted for brake intensity prediction in the case of possible sensor failure and loss of CAN message.

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A Stochastic Driver Pedal Behavior Model Incorporating Road Information

Cited by 48 publications

References 27 publications

Dual-loop online intelligent programming for driver-oriented predict energy management of plug-in hybrid electric vehicles

Dual-loop online intelligent programming for driver-oriented predict energy management of plug-in hybrid electric vehicles

Forced Spatial Attention for Driver Foot Activity Classification

Control Oriented Prediction of Driver Brake Intention and Intensity Using a Composite Machine Learning Approach

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