Rollover Warning System of Heavy Duty Vehicle Based on Improved TTR Algorithm

Zhu, Tianjun

doi:10.3901/jme.2011.10.088

Cited by 23 publications

(17 citation statements)

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“…In addition, five sets of rollover data under different working conditions were analyzed to verify the reliability of the proposed system. Compared with other related systems [5][6][7][8], the proposed rollover monitoring and decision support system has the following advantages:…”

Section: Discussionmentioning

confidence: 99%

“…[7] proposed an emergency warning system for a rollover vehicle through the monitoring of rollover indexes using sensors. Zhu Tianjun et al [8] developed a rollover warning system for heavy-duty vehicles on the basis of improved time-to-rollover algorithm. Inspired by previous work, the lateral acceleration and roll angular velocity are selected as two rollover stability indexes in this paper, and they are real-time monitored by developed PC and mobile applications (APPs).…”

Section: Introductionmentioning

confidence: 99%

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Developing an Ontology-Based Rollover Monitoring and Decision Support System for Engineering Vehicles

Liu

Zhou

2018

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The increasing number of rollover accidents of engineering vehicles has attracted close attention; however, most researchers focus on the analysis and monitoring of rollover stability indexes and seldom the assessment and decision support for the rollover risk of engineering vehicles. In this context, an ontology-based rollover monitoring and decision support system for engineering vehicles is proposed. The ontology model is built for representing monitored rollover stability data with semantic properties and for constructing semantic relevance among the various concepts involved in the rollover domain. On the basis of this, ontology querying and reasoning methods based on the Simple Protocol and RDF Query Language (SPARQL) and Semantic Web Rule Language (SWRL) rules are utilized to realize the rollover risk assessment and to obtain suggested measures. PC and mobile applications (APPs) have also been developed to implement the above methods. In addition, five sets of rollover stability data for an articulated off-road engineering vehicle under different working conditions were analyzed to verify the accuracy and effectiveness of the proposed system.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Developing an Ontology-Based Rollover Monitoring and Decision Support System for Engineering Vehicles

Liu

Zhou

2018

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“…Ataei et al [14] proposed a new MPC-based rollover prevention system based on a new reliable RI and a more detailed vehicle model which includes the effects of the road bank and the lateral load transfer. Zhu and Zong [15] developed rollover warning and control of a heavy vehicle based on the improved TTR method. The Kalman observer is designed to estimate the vehicle roll angle in real-time to ensure the calculation accuracy of load transfer rate LTR and TTR values.…”

Section: Introductionmentioning

confidence: 99%

A Reliability Approach to Development of Rollover Prediction for Heavy Vehicles Based on SVM Empirical Model With Multiple Observed Variables

Zhu

Yin

et al. 2020

IEEE Access

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The rapid development of cooperative vehicle-infrastructure system (CVIS) improves the communication reliability between vehicles and road environment. These communications enable the accurate vehicle rollover prediction in Human-Vehicle-Road interaction. However, considering the strong nonlinear characteristics of Human-Vehicle-Road interaction and the uncertainty of modeling, the traditional deterministic method cannot meet the requirement of accurate prediction of rollover hazard for heavy vehicles. In order to improve the accuracy of vehicles rollover prediction, this paper proposes a developed rollover prediction algorithm based on the multiple observed variables by combining the failure probability in reliability and the empirical model. This approach applies the probability method of uncertainty to the design of dynamic rollover prediction algorithm for heavy vehicles and establishes a classification model of heavy vehicles based on support vector machine (SVM) with multiple observed variables. The failure probability of rollover limit state of heavy vehicles is calculated by Monte Carlo Sampling (MCS), Radial-Based Importance Sampling (RBIS), and Truncated Importance Sampling (TIS), respectively. Then the Fishhook, Double Lane Change tests, and J-turn tests, simulated in TruckSim, are carried out to validate the proposed algorithm. The simulation results show that the rollover prediction algorithm based on failure probability can effectively improve the rollover prediction accuracy for heavy vehicles. Moreover, based on the communication in CVIS, the failure probability can be obtained before entering the specific road. Meanwhile, this approach can reduce the external interference of strong non-linear characteristics of Human-Vehicle-Road interaction and the uncertainty of the modeling to the system, thus improving the prediction accuracy of active safety performance of heavy vehicles significantly. INDEX TERMS Failure probability, heavy vehicles, load transfer ratio, rollover risk prediction, SVM classification model. BIN LI received the Ph.D. degree from Shanghai Jiao Tong University, China, in 2010. He was Research Fellow on electric vehicle project with the University of Waterloo and on mobile robotic control project with McGill University. He is currently a Researcher working on stability and safety control of commercial vehicle and passenger car with Concordia University. His research interests include vehicle system modeling, dynamics and control, vehicle control system design and optimization, electrified vehicle, integrated vehicle motion control, and autonomous vehicle control. WEI MA was born in Xingtai, Hebei, China, in 1991. He received the B.S. degree in vehicle service engineering from the Hebei University of Engineering, Handan, China, in 2018, where he is currently pursuing the M.S. degree in equipment intelligence and safety engineering. His research interest includes vehicle dynamic control.

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“…J Wang et al 13 proposed a real-time vehicle roll state observer and rollover warning algorithm. TJ Zhu et al 14 designed a heavy vehicle rollover warning system based on the improved true three-dimensional reconstruction (TTR) algorithm and linear matrix inequality. Z Jin et al 15 proposed a rollover warning algorithm based on dynamic stability.…”

Section: Introductionmentioning

confidence: 99%

Research method of vehicle rollover mechanism under critical instability condition

Bei

2019

Advances in Mechanical Engineering

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In this article, a novel rollover prediction algorithm is developed for application on vehicles with large lateral velocity and high center of gravity. Lateral energy is the direct cause of rollover. Rollover prediction model is proposed by taking full account of the impact of the pavement, tire, and suspension and realizes the estimation of the vehicle lateral energy. By calculating the ratio of real-time lateral energy reserve and rollover threshold, the degree of rollover risk is obtained. The double-shift experiment and the Fishhook experiment are performed to verify the accuracy and suitability of the proposed model, and the proposed prediction is 0.2 s ahead of the actual liftoff situation and 0.45 s ahead of the actual rollover situation; therefore, the proposed rollover model can be regarded as an effective method.

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Rollover Warning System of Heavy Duty Vehicle Based on Improved TTR Algorithm

Cited by 23 publications

References 0 publications

Developing an Ontology-Based Rollover Monitoring and Decision Support System for Engineering Vehicles

Developing an Ontology-Based Rollover Monitoring and Decision Support System for Engineering Vehicles

A Reliability Approach to Development of Rollover Prediction for Heavy Vehicles Based on SVM Empirical Model With Multiple Observed Variables

Research method of vehicle rollover mechanism under critical instability condition

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