Research on Vehicle Rollover Warning System

2014 ◽  
Vol 590 ◽  
pp. 437-441
Author(s):  
Yi Hua Chen ◽  
Jian Kang Xu ◽  
Jian Guo Mao ◽  
Zhi Lin Jin
Keyword(s):  
Prediction Model ◽  
Warning System ◽  
Vehicle Rollover ◽  
Rollover Prediction ◽  
Rollover Warning

In this paper, a TTR vehicle rollover predication algorithm is calculated. A vehicle rollover predication model is built. Also, a vehicle rollover predication warning system is designed by using TMS320LF2407 DSP, and the warning predication theory is introduced. The results show that the rollover prediction model can predicate rollover occur time effectively and the TTR rollover predication method can be used to give direction to the driver.

Design for Vehicle Rollover Warning System

2013 ◽  
Author(s):  
Chun Hsiung Chen ◽  
Chi-Chun Yao ◽  
Yu-Sheng Liao
Keyword(s):  
Warning System ◽  
Vehicle Rollover ◽  
Rollover Warning

Parameter Identification of Heavy Commercial Vehicle Rollover Prediction Model

2010 ◽  
Vol 439-440 ◽  
pp. 854-858
Author(s):  
Tian Jun Zhu ◽  
Chang Fu Zong
Keyword(s):  
Prediction Model ◽  
Parameter Identification ◽  
Commercial Vehicle ◽  
Model Simulation ◽  
Experiment Data ◽  
Vehicle Rollover ◽  
Ground Test ◽  
Simulation Results ◽  
Rollover Prediction ◽  
Heavy Commercial Vehicle

This paper presents the parameter identification technology of heavy commercial vehicle rollover prediction. In this study, a nonlinear truck model has been established for the rollover threat prediction. In order to achieve valid and representative truck model as close to the target real truck as possible, a set of key parameters are identified from experiment data collected from real truck ground test. At the last, the vehicle prediction model simulation results compared with the experimental results, it is shown that the prediction model can be accurately predicted the rollover dangerous state.

Vehicle Rollover Warning System Based on MEMS

2013 ◽  
Vol 718-720 ◽  
pp. 1487-1492
Author(s):  
Ru Hai Ge ◽  
Jun Guan ◽  
Cun Jie Shi
Keyword(s):  
Angular Velocity ◽  
Warning System ◽  
Active Safety ◽  
System Software ◽  
Vehicle Rollover ◽  
Multi Level ◽  
Vehicle Test ◽  
Vehicle Active Safety ◽  
Real Vehicle ◽  
Rollover Warning

An ARM11-based vehicle rollover warning system is designed in this paper in order to prevent vehicle rollover occurred while driving. Monitor the vehicle real-time roll angular velocity and roll angle via sensors and Multi-level Recursive Model is used to predict the vehicle roll attitude. When the predicted roll reach to the limit conditions then trigger the alarm to remind driver to be careful and to take appropriate measures, so as to prevent vehicle rollover accidents. Vehicle rollover warning system software is designed based on VB 2005, Matlab and NI Measurement Studio, results between simulation and real vehicle test show that vehicle rollover warning system can predict vehicle rollover timely and accurately, which can improve vehicle active safety.

Vehicle rollover warning system based on TTR method with Inertial Measurement

2021 ◽  
Author(s):  
Mengmeng Wang ◽  
Jinhao Liu ◽  
Hongye Zhang ◽  
Linjie Gan ◽  
Xiangbo Xu ◽  
...  
Keyword(s):  
Steady State ◽  
Load Transfer ◽  
Warning System ◽  
Front Wheel ◽  
Roll Angle ◽  
Lateral Acceleration ◽  
Vehicle Speed ◽  
Matlab Simulink ◽  
Vehicle Rollover ◽  
Rollover Warning

Abstract This paper presents a theoretical and experimental study conducted on the rollover warning of wheeled off-road operating vehicles. The time to rollover (TTR) warning algorithm was studied with real-time vehicle roll angle and roll angle velocity as the input variables, and lateral load transfer ratio (LTR) was used as the rollover determination index. Subsequently, a vehicle dynamics model was built using CarSim software, and a warning algorithm was established in the MATLAB/Simulink environment. The rollover joint simulation in CarSim and MATLAB/Simulink was conducted under typical working conditions. Finally, combined with inertial measurements, a rollover warning system was independently developed. In addition, the rollover warning system was installed on a light forest firefighting truck to verify the feasibility of the system via a real vehicle experiment, and the law of vehicle rollover motion was also studied. The serpentine experiment and steady-state rotation experiment were conducted. The experimental results showed that at identical front-wheel steering angles, the roll angle and lateral acceleration increased with an increase in the vehicle speed. Furthermore, for identical vehicle speeds, the roll angle and lateral acceleration of the vehicle increased with an increase in the front-wheel steering angle. The dangerous vehicle speed was 50 km/h in the serpentine condition and 40 km/h in the steady-state rotation condition. The risk trend and alarm signal obtained by the rollover warning system were consistent with the actual situation. Thus, this can assist drivers in judging the rollover risk and effectively improve the active safety of special vehicles. Furthermore, it also provides a reference for further research on active rollover control technology of special vehicles.

Effectiveness of Truck Rollover Warning Systems

2001 ◽  
Vol 1779 (1) ◽  
pp. 134-140 ◽  
Author(s):  
Derek Baker ◽  
Rob Bushman ◽  
Curtis Berthelot
Keyword(s):  
North America ◽  
Warning System ◽  
Warning Systems ◽  
Commercial Vehicles ◽  
Decision Criteria ◽  
Warning Messages ◽  
System A ◽  
Different Types ◽  
Rollover Warning

Different types of intelligent rollover system deployed by road agencies across North America are investigated. The importance of weight is addressed for maximum effectiveness of rollover warning messages for commercial vehicles in a potential rollover situation on sharp curves or exit ramps. The type of information that may be used to activate a rollover is discussed to analyze the number of correctly warned vehicles compared with the number of false warnings generated by the rollover warning system. A case study of the effectiveness of an intelligent rollover system is presented. On the basis of this case study, it was found that speed-based rollover warning systems generated anywhere from 44 percent to 49 percent more false rollover warnings for commercial vehicles than did rollover warning systems that employed weight information in the rollover decision criteria.

scholarly journals A Novel Air Quality Early-Warning System Based on Artificial Intelligence

2019 ◽  
Vol 16 (19) ◽  
pp. 3505
Author(s):  
Mo ◽  
Zhang ◽  
Li ◽  
Qu
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Prediction Model ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Air Pollutant ◽  
Concentration Data ◽  
Whale Optimization ◽  
The World

The problem of air pollution is a persistent issue for mankind and becoming increasingly serious in recent years, which has drawn worldwide attention. Establishing a scientific and effective air quality early-warning system is really significant and important. Regretfully, previous research didn’t thoroughly explore not only air pollutant prediction but also air quality evaluation, and relevant research work is still scarce, especially in China. Therefore, a novel air quality early-warning system composed of prediction and evaluation was developed in this study. Firstly, the advanced data preprocessing technology Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) combined with the powerful swarm intelligence algorithm Whale Optimization Algorithm (WOA) and the efficient artificial neural network Extreme Learning Machine (ELM) formed the prediction model. Then the predictive results were further analyzed by the method of fuzzy comprehensive evaluation, which offered intuitive air quality information and corresponding measures. The proposed system was tested in the Jing-Jin-Ji region of China, a representative research area in the world, and the daily concentration data of six main air pollutants in Beijing, Tianjin, and Shijiazhuang for two years were used to validate the accuracy and efficiency. The results show that the prediction model is superior to other benchmark models in pollutant concentration prediction and the evaluation model is satisfactory in air quality level reporting compared with the actual status. Therefore, the proposed system is believed to play an important role in air pollution control and smart city construction all over the world in the future.

scholarly journals Analysis of Model Performance and Implementation of an Optimized Flood Prediction Model Using Data Mining Techniques

2021 ◽  
Vol 10 (2) ◽  
pp. 888-893
Keyword(s):  
Prediction Model ◽  
Model Performance ◽  
Warning System ◽  
Support Vector ◽  
Ann Model ◽  
Flood Prediction ◽  
River Region ◽  
Input Variables ◽  
Using Data ◽  
Artificial Neural Network Ann

Flooding is a major problem globally, and especially in SuratThani province, Thailand. Along the lower Tapeeriver in SuratThani, the population density is high. Implementing an early warning system can benefit people living along the banks here. In this study, our aim was to build a flood prediction model using artificial neural network (ANN), which would utilize water and stream levels along the lower Tapeeriver to predict floods. This model was used to predict flood using a dataset of rainfall and stream levels measured at local stations. The developed flood prediction model consisted of 4 input variables, namely, the rainfall amounts and stream levels at stations located in the PhraSeang district (X.37A), the Khian Sa district (X.217), and in the Phunphin district (X.5C). Model performance was evaluated using input data spanning a period of eight years (2011–2018). The model performance was compared with support vector machine (SVM), and ANN had better accuracy. The results showed an accuracy of 97.91% for the ANN model; however, for SVM it was 97.54%. Furthermore, the recall (42.78%) and f-measure (52.24%) were better for our model, however, the precision was lower. Therefore, the designed flood prediction model can estimate the likelihood of floods around the lower Tapee river region

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