Vehicle Deceleration Prediction Based on Deep Neural Network at Braking Conditions

Min, K.; Yeon, Kyuhwan; Jo, Yuhyeok; Sim, Gyubin; Sunwoo, Myoungho; Han, Manbae

doi:10.1007/s12239-020-0010-2

Cited by 11 publications

(6 citation statements)

References 28 publications

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“…In their research, Min et al [11] presented an LSTM model to predict vehicle deceleration in braking situations where stopping before a traffic light is needed.…”

Section: Related Workmentioning

confidence: 99%

Synthesizing Vehicle Speed-Related Features with Neural Networks

Krepelka,

Vrany

2023

Vehicles

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In today’s automotive industry, digital technology trends such as Big Data, Digital Twin, and Hardware-in-the-loop simulations using synthetic data offer opportunities that have the potential to transform the entire industry towards being more software-oriented and thus more effective and environmentally friendly. In this paper, we propose generative models to synthesize car features related to vehicle speed: brake pressure, percentage of the pressed throttle pedal, engaged gear, and engine RPM. Synthetic data are essential to digitize Hardware-in-the-loop integration testing of the vehicle’s dashboard, navigation, or infotainment and for Digital Twin simulations. We trained models based on Multilayer Perceptron and bidirectional Long-Short Term Memory neural network for each feature. These models were evaluated on a real-world dataset and demonstrated sufficient accuracy in predicting the desired features. Combining our current research with previous work on generating a speed profile for an arbitrary trip, where Open Street Map data and elevation data are available, allows us to digitally drive this trip. At the time of writing, we are unaware of any similar data-driven approach for generating desired speed-related features.

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“…In their research, Min et al [11] presented an LSTM model to predict vehicle deceleration in braking situations where stopping before a traffic light is needed.…”

Section: Related Workmentioning

confidence: 99%

Synthesizing Vehicle Speed-Related Features with Neural Networks

Krepelka,

Vrany

2023

Vehicles

View full text Add to dashboard Cite

show abstract

“…Another example is a deep NN-based approach of EV energy demand estimation proposed in [21], which is based on a driving cycle series served as a static input to the NN. In [24], the HES-based braking system implements automatic control of the EV regeneration torque aiming to improve energy efficiency along with driver's comfort. To apply this system, the accurate prediction of the vehicle deceleration states was produced using the longshort-term memory and a two-layer conventional NN model.…”

Section: Introductionmentioning

confidence: 99%

Neural Network Control of Green Energy Vehicles with Blended Braking Systems

Vodovozov¹,

Petlenkov²,

Aksjonov³

et al. 2024

RE&PQJ

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A neural network-based control system is offered, which ensures high quality blended braking of the green energy vehicles in both the intensive and the gradual deceleration scenarios, with energy recovery at the changing road pavement. In this study, a neural network controller provides the torque gradient control without a tire model resulting in returning maximal energy to the hybrid energy source during the braking process. To meet the conflicting requirements of different braking modes and road surfaces, an allocation algorithm determines how to distribute the driver’s torque request between the friction and electrical brakes. Simulation demonstrates effectiveness of the proposed braking system. Model states and inputs are used as a guidance to learn a coupled two-layer neural network capable to capture various dynamic behaviours that could not be included in the simplified physics-based model. An experimental part of the research proves the model and simulation validity.

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“…[15][16][17][18] In recent years, the control methods of vehicle active suspension have developed rapidly, which include modern, intelligent and composite control method. 19,20 Some active suspension control strategies have become research hotspots, such as PID control, 21 fuzzy control, 22,23 neural network control, 24 optimal control, 25 and composite control strategy. 26 However, these control strategies have their own deficiency.…”

Section: Introductionmentioning

confidence: 99%

Vibration control of improved LQG for wheel drive electric vehicle based on uncertain parameters

Liu

Wang

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part D:

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To improve the ride comfort of wheel drive electric vehicle, a quarter dynamic model is established for vehicle active and passive suspension. Based on the response root mean square (RMS), the evaluation index of vehicle ride comfort is derived. According to the influence of uncertain parameters on system minimum RMS, the optimal vehicle parameters are determined. To avoid experience value of uncertain weight coefficient affecting the LQG control, analytic hierarchy process (AHP) is adopted to determine the weighting coefficient of vehicle performance evaluation index. And the LQG controller of vehicle active suspension is designed based on optimal control theory. Compared with the analysis results of passive suspension, the effectiveness of active control scheme is verified.

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Vehicle Deceleration Prediction Based on Deep Neural Network at Braking Conditions

Cited by 11 publications

References 28 publications

Synthesizing Vehicle Speed-Related Features with Neural Networks

Synthesizing Vehicle Speed-Related Features with Neural Networks

Neural Network Control of Green Energy Vehicles with Blended Braking Systems

Vibration control of improved LQG for wheel drive electric vehicle based on uncertain parameters

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