Rollover prediction and control in heavy vehicles via recurrent neural networks

Sánchez, Edgar N.; Ricalde, Luis J.; Langari, Reza; Shahmirzadi, Danial

doi:10.1109/cdc.2004.1429635

Cited by 13 publications

(2 citation statements)

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“…Therefore, here proposed is a neural network indicator developed to identify tripped and untripped rollovers. Neural networks known for adaptable and nonlinear information processing capability perform fittingly in the areas of predication, expert system, and mode identification [32][33][34][35][36][37]. By taking the approach to be introduced afterward into account, the estimation algorithms to estimate unknown parameters such as roll angle, height of center of gravity of a vehicle, or vehicle mass are not required.…”

Section: Introductionmentioning

confidence: 99%

Vehicle Rollover Detection in Tripped and Untripped Rollovers using Recurrent Neural Networks

Treetipsounthorn¹,

Sriudomporn²,

Phanomchoeng³

et al. 2020

Adv. sci. technol. eng. syst. j.

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Comparing to other types of vehicle accidents, fatality rate of tipped rollover accidents shows significant number. Thus, tripped rollover prevention systems are important in order to keep driver safe. In other hands, different rollover indices are defined to handle the risk. The variable unknown parameters of each index, for instance, current load of the vehicle or center of gravity, are considered as a difficulty. In this work, the recurrent neural networks, which are designed to work on sequential data in order to provide data estimation without additional estimation algorithm, are investigated in purpose to estimate the tripped and untripped rollover index. The vehicle simulation software with industrial standard CarSim is applied to validate the result. The Tanh recurrent neural network is stated in the result to be the most accurate tripped rollover index estimator for the uncertain parameters, for example, sprung mass and the height of the center of gravity. The suitable input features for tripped and untripped rollover index and neural network structure are verified. To prevent and provide warning of rollover, an advance future prediction can also be designed for the future tripped and untripped rollover prediction.

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Section: Introductionmentioning

confidence: 99%

Vehicle Rollover Detection in Tripped and Untripped Rollovers using Recurrent Neural Networks

Treetipsounthorn¹,

Sriudomporn²,

Phanomchoeng³

et al. 2020

Adv. sci. technol. eng. syst. j.

View full text Add to dashboard Cite

show abstract

“…[1,2]. As another example, many researchers propose using vehicle rollover predictions as anti-rollover measures [3–5]. The benefit is that the rollover prediction can trigger the control input earlier than the approaches without prediction.…”

Section: Introductionmentioning

confidence: 99%

Vehicle Dynamic Prediction Systems with On-Line Identification of Vehicle Parameters and Road Conditions

Hsu

Chen

2012

Sensors

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This paper presents a vehicle dynamics prediction system, which consists of a sensor fusion system and a vehicle parameter identification system. This sensor fusion system can obtain the six degree-of-freedom vehicle dynamics and two road angles without using a vehicle model. The vehicle parameter identification system uses the vehicle dynamics from the sensor fusion system to identify ten vehicle parameters in real time, including vehicle mass, moment of inertial, and road friction coefficients. With above two systems, the future vehicle dynamics is predicted by using a vehicle dynamics model, obtained from the parameter identification system, to propagate with time the current vehicle state values, obtained from the sensor fusion system. Comparing with most existing literatures in this field, the proposed approach improves the prediction accuracy both by incorporating more vehicle dynamics to the prediction system and by on-line identification to minimize the vehicle modeling errors. Simulation results show that the proposed method successfully predicts the vehicle dynamics in a left-hand turn event and a rollover event. The prediction inaccuracy is 0.51% in a left-hand turn event and 27.3% in a rollover event.

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