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

Zhou, Jintang; Sun, Jing; Ding, Yi; Cao, Hanzhang; Zhao, Wanzhong

doi:10.3390/en12132483

Cited by 8 publications

(5 citation statements)

References 34 publications

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“…Width of the time windows could have an effect on the accuracy and computation cost of the algorithm. In this study, the sampling interval was set to 0.02 s. We tested nine different widths of time window, namely, 5,10,15,20,25,30,35,40,45.…”

Section: (A) Trainingmentioning

confidence: 99%

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Data-Driven Detection Methods on Driver’s Pedal Action Intensity Using Triboelectric Nano-Generators

et al. 2020

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Driver’s driving actions on pedals can be regarded as an expression of driver’s acceleration/deceleration intention. Quickly and accurately detecting driving action intensity on pedals can have great contributions in preventing road traffic accidents and managing the energy consumption. In this paper, we report a pressure-sensitive and self-powered material named triboelectric nano-generators (TENGs). The generated voltage data of TENGs, which is associated with the pedal action, can be collected easily and stored sequentially. According to the characteristics of the voltage data, we have employed a hybrid machine learning method. After collecting signals from TENGs and driving simulator simultaneously, an unsupervised Gaussian mixture model is used to cluster the pedal events automatically using data from simulator. Then, multi-feature candidates of the voltage data from TENGs are extracted and ranked. A supervised random forest model that treats voltage data of TENGs as input data is trained and tested. Results show that data from TENGs can have a high accuracy of more than 90% using the random forest algorithm. The evaluating results demonstrate the accuracy of the proposed data-driven hybrid learning algorithm for recognition of driver’s pedal action intensity. Furthermore, technical and economic characteristics of TENGs and some common sensors are compared and discussed. This work may demonstrate the feasibility of using these data-driven methods on the detection of driver’s pedal action intensity.

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Section: (A) Trainingmentioning

confidence: 99%

“…Firstly, data from the Controller Area Network (CAN) is used. Zhou [10] employed a random forest algorithm to classify the brake intention into four levels: slight, medium, intensive and emergency braking. Lv [11] used a hybrid-learning method.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Detection Methods on Driver’s Pedal Action Intensity Using Triboelectric Nano-Generators

et al. 2020

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“…The NARX network was applied as a dynamic neural network with feedback and memory functions to characterize the brake intensity influenced by the driver's sequential actions, demonstrating long-term dependencies. The braking moment values are significantly related to the driver's behavior and driving maneuvers [11]. Moreover, the authors of [12] used a neural network to model different systems in an autonomous vehicle: the steering, acceleration, and braking systems.…”

Section: Introductionmentioning

confidence: 99%

Predicting the Torque Demand of a Battery Electric Vehicle for Real-World Driving Maneuvers Using the NARX Technique

Alhanouti,

Gauterin

2024

WEVJ

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An identification technique is proposed to create a relation between the accelerator pedal position and the corresponding driving moment. This step is beneficial to replace the complex physical model of the vehicle control unit, especially when the sufficient information needed to model certain functionalities of the vehicle control unit are unavailable. We utilized the nonlinear autoregressive exogenous model to regenerate the electric motor torque demand, given the accelerator pedal position, the motor’s angular speed, and the vehicle’s speed. This model proved to be extremely efficient in representing this highly complex relationship. The data employed for the identification process were chosen from an actual three-dimensional route with sudden changes of a dynamic nature in the driving mode, different speed limits, and elevations, as an attempt to thoroughly cover the driving moment scope based on the alternation of the given inputs. Analyzing the selected route data points showed the widespread coverage of the motor’s operational scope compared to a standard driving cycle. The training outcome revealed that linear modeling is inadequate for identifying the targeted system, and has a substantial estimation error. Adding the nonlinearity feature to the model led to an exceptionally high accuracy for the estimation and validation datasets. The main finding of this work is that the combined model from the nonlinear autoregressive exogenous and the sigmoid network enables the accurate modeling of highly nonlinear dynamic systems. Accordingly, the maximum absolute estimation error for the motor’s moment was less than 10 Nm during the real-world driving maneuver. The highest errors are found around the maximum motor’s moment. Finally, the model is validated with measurements from an actual field test maneuver. The identified model predicted the driving moment with a correlation of 0.994.

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“…This method, to some extent, solves the problem of low accuracy of traditional identification methods in distinguishing slight braking from normal braking intention, but it requires a higher quality of the pedal signal. Novel hybrid learning methods which combine unsupervised learning and supervised learning were put forward by Lv et al 6,31 The unsupervised GMM and FCM were used respectively to label the braking intensity level and braking intention according to the brake pressure of master cylinder, and then the labeling results and other vehicle state information were used to train the supervised random forest model for braking intention classification. Similarly, Yang et al 32 adopted GMM to cluster the braking intensity level and took the brake pushrod stroke and its velocity as the input of the adaptive-network-based fuzzy inference system to quantitatively recognize the braking intensity.…”

Section: Introductionmentioning

confidence: 99%

“…Liu et al 37,38 identified the driving intention based on LSTM, with an accuracy of more than 95%. To estimate the precise value of the braking pressure in advance, the nonlinear autoregressive with external input network 31 and integrated time series model based on RNN-LSTM 39 were applied in braking intensity prediction, which has a great effect on the braking intention identification.…”

Section: Introductionmentioning

confidence: 99%

Research on characteristic parameter selection and attention-GRU-based model for braking intention identification

Zhao

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part D:

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The braking intention is of great significance to the realization of driver assistant features, the improvement of braking safety, and the maximization of energy recovery efficiency for electric vehicles. With the aim of accurate identification of braking intention, an identification model based on Gated Recurrent Unit (GRU) Network with Attention mechanism is proposed in this paper. Based on numerous vehicle braking test data, braking process analysis, characteristic parameters selection, identification model training, and verification are carried out. Through the difference analysis based on the Kruskal-Wallis test and the importance evaluation based on random forest, combined with the real-time requirements of practical application, the appropriate characteristic parameters are selected as the model input. The attention mechanism is introduced into the proposed model, which can improve identification accuracy by capturing valuable feature information. The comparative verification results show that the Attention-GRU model performs better than the other three comparison models, and its identification accuracy is 96.7%, of which the accuracy of slight braking, normal braking, and emergency braking are 96.3%, 95.8%, and 100% respectively. The identified braking intention can provide an effective basis for the establishment of vehicle control strategies.

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

Cited by 8 publications

References 34 publications

Data-Driven Detection Methods on Driver’s Pedal Action Intensity Using Triboelectric Nano-Generators

Data-Driven Detection Methods on Driver’s Pedal Action Intensity Using Triboelectric Nano-Generators

Predicting the Torque Demand of a Battery Electric Vehicle for Real-World Driving Maneuvers Using the NARX Technique

Research on characteristic parameter selection and attention-GRU-based model for braking intention identification

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