Rollover Detection Research Articles

Articulated vehicle rollover detection and active control based on center of gravity position metric

In this study, the adaptive adjustment technology, based on an Attitude and Heading Reference System, is applied to loaders, aiming to improve the rollover protection performance of articulated engineering vehicles. A dynamic rollover stability index for loaders is developed, coupling the roll angular velocity and the slope and height of the center of gravity. A nonlinear predictive model of the material weight is built, in order to attain the height of the center of gravity. An adaptive attitude adjustment system is proposed, totally based on electro-hydraulic proportional control technology, to adjust the attitude of the working system, so as to achieve a higher threshold value of the vehicle’s anti-tip performance. Finally, the rollover stability index and control methods, as proposed in this article, are validated using experiments. The boundary values that trigger the system’s operation are reduced to ensure safety, while the validation results prove that the proposed method is effective.

An Intelligent Predictive Algorithm for the Anti-Rollover Prevention of Heavy Vehicles for Off-Road Applications

Rollover detection and prevention are among the most critical aspects affecting the stability and safety assessment of heavy vehicles, especially for off-road driving applications. This topic has been studied in the past and analyzed in depth in terms of vehicle modelling and control algorithms design able to prevent the rollover risk. However, it still represents a serious problem for automotive carmakers due to the huge counts among the main causes for traffic accidents. The risk also becomes more challenging to predict for off-road heavy vehicles, for which the incipient rollover might be triggered by external factors, i.e., road irregularities, bank angles as well as by aggressive input from the driver. The recent advances in road profile measurement and estimation systems make road-preview-based algorithms a viable solution for the rollover detection. This paper describes a model-based formulation to analytically evaluate the load transfer dynamics and its variation due to the presence of road perturbations, i.e., road bank angle and irregularities. An algorithm to detect and predict the rollover risk for heavy vehicles is also presented, even in presence of irregular road profiles, with the calculation of the ISO-LTR Predictive Time through the Phase-Plane analysis. Furthermore, the artificial intelligence techniques, based on the recurrent neural network approach, is also presented as a preliminary solution for a realistic implementation of the methodology. The paper finally assess the efficacy of the proposed rollover predictive algorithm by providing numerical results from the simulation of the most severe maneuvers in realistic off-road driving scenarios, also demonstrating its promising predictive capabilities.

Rollover Index for Rollover Mitigation Function of Intelligent Commercial Vehicle’s Electronic Stability Control

This paper describes a rollover index for detection or prediction of impending rollover in different driving situations using minimum sensor signals which can be easily obtained from an electronic stability control (ESC) system. The estimated lateral load transfer ratio (LTR) was used as a rollover index with only limited information such as the roll state of the vehicle and some constant parameters. A commercial vehicle has parameter uncertainties because of its load variation. This is likely to affect the driving performance and the estimation of the dynamic state of the vehicle. The main purpose of this paper is to determine the rollover index based on reliable measurements and the parameters of the vehicle. For this purpose, a simplified lateral and vertical vehicle dynamic model was used with some assumptions. The index is appropriate for various situations although the vehicle parameters may change. As part of the index, the road bank angle was investigated in this study, using limited information. Since the vehicle roll dynamics are affected by the road bank angle, the road bank angle should be incorporated, although previous studies ignore this factor in order to simplify the problem. Because it increases or reduces the chances of rollover, consideration of the road bank angle is indispensable in the rollover detection and mitigation function of the ESC system. The performance of the proposed algorithm was investigated via computer simulation studies. The simulation studies showed that the proposed estimation method of the LTR and road bank angle with limited sensor information followed the actual LTR value, reducing the parameter uncertainties. The simulation model was constructed based on a heavy bus (12 tons).

Estimation of Tire Normal Forces including Suspension Dynamics

Tire normal forces are difficult to measure, but information on the vehicle normal force can be used in many automotive engineering applications, e.g., rollover detection and vehicle and wheel stability. Previous papers use algebraic equations to estimate the tire normal force. In this article, the estimation of tire normal force is formulated as an input estimation problem. Two observers are proposed to solve this problem by using a quarter-car suspension model. First, the Youla Controller Output Observer framework is presented. It converts the estimation problem into a control problem and produces a Youla parameterized controller as observer. Second, a Kalman filter approach is taken and the input estimation problem is addressed with an Unbiased Minimum Variance Filter. Both methods use accelerometer and suspension deflection sensors to determine the vehicle normal force. The design of the observers is validated in simulation and a sensitivity analysis is performed to evaluate their robustness.

Rollover detection and prevention of a heavy-duty vehicle on banked and graded uneven road

Rollover detection and prevention of a heavy-duty vehicle on banked and graded uneven road

Rollover detection and prevention of a heavy-duty vehicle on banked and graded uneven road

The roll performance of vehicle with flexible frame and solid axle suspension has been less studied in current researches. To reveal rollover mechanism of vehicle with flexible frame and solid axle suspension on banked and graded uneven road, a novel rollover index and an integrated control strategy for vehicle rollover are proposed. Taking the coupling of roll motion of sprung mass of the front axle with that of the drive axle into consideration, 10 DOF dynamic model is established. Then, a novel rollover index is derived from rollover dynamics. Moreover, an integrated control strategy combining active steering and active braking of the rear wheel is developed and optimised offline by genetic algorithm. Finally, some numerical cases are studied under untripped and complicated tripped rollover conditions. Results state that the integrated control strategy has good robustness, which can prevent the heavy-duty vehicle rollover effectively and keep the drive intention simultaneously.

A Vehicle Rollover Evaluation System Based on Enabling State and Parameter Estimation

There is an increasing awareness of the need to reduce the traffic accidents and fatality rates due to vehicle rollover incidents. The accurate detection of impending rollover is necessary to effectively implement vehicle rollover prevention. To this end, a real-time rollover index and a rollover tendency evaluation system are needed. These should give high accuracy and be of a low application cost. In this article, we propose a rollover evaluation system taking lateral load transfer ratio (LTR) as the rollover index with inertial measurement unit as the system input. A nonlinear suspension model and a rolling plane vehicle model are established for the state and parameter estimation. An adaptive extended Kalman filter is utilized to estimate the roll angle and rate, which adjusts noise covariance matrices to accommodate the nonlinear model characteristic and the unknown noise characteristic. In the meantime, the forgetting factor recursive least squares method is utilized to identify the height of the center of gravity. The Butterworth filter is used to filter out the high-frequency noise of the acceleration signal and the index of LTR is accordingly calculated based on the estimation results. The proposed scheme is verified and compared through hardware-in-loop tests. The results show that the developed scheme performs well in a variety of operating conditions.

A Novel Extension for e-Safety Initiative Based on Developed Fusion of Biometric Traits

Safety is one of the central elements of society functioning. The eSafety aims at increasing road safety through the use of intelligent integrated safety systems. This article presents the concept and PCB implementation of an on-board compact eCall device that can be installed at the owners' request in a used vehicle. The prototype includes accident and rollover detection, passenger detection, and GSM communication with the eCall PSAP service center. Two accident detection algorithms are implemented: the first relates to the collision detection based on exceeding the threshold of acceleration using an accelerometer to measure the dynamic force caused by movement and the gravity force, the second - the rollover detection based on exceeding the threshold of the vehicle angle of inclination using data coming from the accelerometer and a gyroscope. The prototype was tested in eight different test scenarios using laboratory accident simulators as well as during driving. The proposed solution of passenger detection is based on images coming from a built-in 360° view camera. In the authors' approach the obtained images were transformed into linear ones and subject to subsequent operations - initial processing, object detection and redundant detection filtering. The authors utilize biometric methods in object detection such as Viola-Jones and Single Shot MultiBox Detector for face localization, and You Look Only Once for whole body detection. An additional testing mechanism for passenger detection are seat pressure pads that are independent of camera detection. All necessary sensors and communication components verified by practical experiments have been integrated on the custom designed motherboard with the NVIDIA Jetson Nano processing module installed. The tests confirmed the performance is sufficient for rapid accident detection, real-time image analysis and all mandatory eCall communication functions. Results of experiments were promising, providing high accident and passenger detection rates.

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

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

An Earlier Predictive Rollover Index Designed for Bus Rollover Detection and Prevention

As vehicle rollovers annually cause a great deal of traffic-related deaths, an increasing number of vehicles are being equipped with rollover prevention systems with the aim of avoiding such accidents. To improve the functionality of active rollover prevention systems, this study provided a potential enhanced method with the intention to predict the tendency of the lateral load transfer ratio (LTR), which is the most common rollover index. This will help provide a certain amount of lead time for the control system to respond more effectively. Before the prediction process, an estimation equation was proposed to better estimate the LTR; the equation was validated using Simulink and TruckSim. Further, to eliminate the influence of drawbacks and make this method practical, a buffer operator was added. Simulation results showed that grey LTR (GLTR) was able to roundly predict the future trend of the LTR based on current and previous data. Under the tests of “Sine with Dwell” (Sindwell) and double lane change (DLC), the GLTR could provide the control system with sufficient time beforehand. Additionally, to further examine the performance of the GLTR, a differential system model was adopted to verify its effectiveness. Through the Sindwell maneuver, it was demonstrated that the GLTR index could improve the performance of the rollover prevention systems by achieving the expected response.

A Novel Active Rollover Prevention for Ground Vehicles Based on Continuous Roll Motion Detection

To enhance the performance of vehicle rollover detection and prevention, this paper proposes a novel control strategy integrating the mass-center-position (MCP) metric and the active rollover preventer (ARPer) system. The applied MCP metric can provide completed rollover information without saturation in the case of tire lift-off. Based on the continuous roll motion detection provided by the MCP metric, the proposed ARPer system can generate corrective control efforts independent to tire–road interactions. Moreover, the capability of the ARPer system is investigated for the given vehicle physical spatial constraints. A hierarchical control architecture is also designed for tracking desired accelerations derived from the MCP metric and allocating control efforts to the ARPer system and the active front steering (AFS) control. Cosimulations between CarSim® and MATLAB/SIMULINK with a fishhook maneuver are conducted to verify the control performance. The results show that the vehicle with the assistance of the ARPer system can successfully achieve better performance of vehicle rollover prevention, compared with an uncontrolled vehicle and an AFS-controlled vehicle.

Rollover detection and control on the non-driven axles of trucks based on pulsed braking excitation

ABSTRACTThe major challenges for rollover detection are the accurate modelling of vehicle dynamics and the real-time estimation of the varied parameters. To circumvent the dependence on vehicle parameters, a novel rollover detection method based on the pulsed braking excitation is proposed. With the lateral load transfer ratio (LTR), the relationship between rollover risks and non-driven wheel rotational dynamics is deduced, which is the basis to apply braking excitation on wheels. The lateral acceleration is adopted as the first criterion to activate the rollover detection. Once the pulsed braking is applied to the non-driven wheels, the braking pressure and wheel angular speeds are measured to estimate the LTR on the non-driven axle. In case of emergency, the differential braking-based anti-rollover is implemented. Experiments were conducted on a Hardware-in-Loop bench. The results show that, the pulsed braking can be activated timely, and the LTR on the non-driven axle is estimated accurately. With the anti-rollover control, the roll stability is improved considerably.

A general rollover index for tripped and un-tripped rollovers on flat and sloped roads

In order to develop a rollover prevention system, it is essential to have a reliable index that properly indicates real-time rollover danger during vehicle maneuvers. The existing rollover indices are mainly for un-tripped rollovers and have limitations in detecting tripped rollovers. This study introduces a general rollover index (GRI) for the detection of rollover in both tripped and un-tripped cases and also on flat and sloped roads. Based on the lateral load transfer ratio, the proposed index is analytically derived in terms of measurable vehicle parameters and state variables. The general rollover index considers both lateral and vertical road inputs and thus can indicate tripped rollovers in the instance of curbs, soft soil or bumps. Sensitivity analysis for the proposed index is also provided to evaluate the effects of different vehicle parameters and different state variables on tripped and un-tripped rollovers. The introduced index can be used not only for the development of active rollover prevention systems, but also for rollover analysis and design of vehicles. The performance of the introduced general rollover index is validated through simulations using a high-fidelity CarSim model for a SUV.

Vehicle lift-off modelling and a new rollover detection criterion

ABSTRACTThe modelling and development of a general criterion for the prediction of rollover threshold is the main purpose of this work. Vehicle dynamics models after the wheels lift-off and when the vehicle moves on the two wheels are derived and the governing equations are used to develop the rollover threshold. These models include the properties of the suspension and steering systems. In order to study the stability of motion, the steady-state solutions of the equations of motion are carried out. Based on the stability analyses, a new relation is obtained for the rollover threshold in terms of measurable response parameters. The presented criterion predicts the best time for the prevention of the vehicle rollover by applying a correcting moment. It is shown that the introduced threshold of vehicle rollover is a proper state of vehicle motion that is best for stabilising the vehicle with a low energy requirement.

A Dynamic Instability Detection and Prediction System for High Clearance Tractor

Abstract: High clearance tractors are often operated on rough terrain with high rollover risk, which would result in operator injury. This paper introduces a high clearance tractor rollover detection and risk prediction system, developed with multi-sensing technologies and embedded device. Mathematical model was firstly established and coded to calculate the stability index of tested machine. GPS and inertial sensors were utilized to obtain the machine parameters for index calculation. Mobile software was developed with the function of image and sound warning for machine operator. Experiments were conducted to indentify the effect of velocity, slope angle, rotation velocities on tractor stability. Obtained results indicated the validity of developed model and prediction system.

Field Tests of a Tractor Rollover Detection and Emergency Notification System.

The objective of this research was to assess the feasibility of a rollover detection and emergency notification system for farm tractors using field tests. The emergency notification system was developed based on a tractor stability model and implemented on a mobile electronic device with the iOS operating system. A complementary filter was implemented to combine the data from the accelerometer and gyroscope sensors to improve their accuracies in calculating the roll and pitch angles and the roll and pitch rates. The system estimates a stability index value during tractor operation, displays feedback messages when the stability index is lower than a preset threshold value, and transmits emergency notification messages when an overturn happens. Ten tractor rollover tests were conducted on a field track. The developed system successfully monitored the stability of the tractor during all of the tests. The iOS application was able to detect rollover accidents and transmit emergency notifications in the form of a phone call and email when an accident was detected. The system can be a useful tool for training and education in safe tractor operation. The system also has potential for stability monitoring and emergency notification of other on-road and off-road motorized vehicles.

Detection of impending vehicle rollover with road bank angle consideration using a robust fuzzy observer

This article describes a method of vehicle dynamics estimation for impending rollover detection. We estimate vehicle dynamic states in presence of the road bank angle as a disturbance in the vehicle model using a robust observer. The estimated roll angle and roll rate are used to compute the rollover index which is based on the prediction of the lateral load transfer. In order to anticipate rollover detection, a new method is proposed to compute the time to rollover (TTR) using the load transfer ratio (LTR). The nonlinear model, deduced from the vehicle lateral and roll dynamics, is represented by a Takagi-Sugeno (T-S) fuzzy model. This representation is used to account for the nonlinearities of lateral cornering forces. The proposed T-S observer is designed with unmeasurable premise variables to cater for non-availability of the slip angles measurement. The proposed approach is evaluated using CarSim simulator under different driving scenarios. Simulation results show good efficiency of the proposed T-S observer and the rollover detection method.

An improvement in rollover detection of articulated vehicles using the grey system theory

A Rollover Index combined with the grey system theory, called a Grey Rollover Index (GRI), is proposed to assess the rollover threat for articulated vehicles with a tractor–semitrailer combination. This index can predict future trends of vehicle dynamics based on current vehicle motion; thus, it is suitable for vehicle-rollover detection. Two difficulties are encountered when applying the GRI for rollover detection. The first difficulty is effectively predicting the rollover threat of the vehicles, and the second difficulty is achieving a definite definition of the real rollover timing of a vehicle. The following methods are used to resolve these problems. First, a nonlinear mathematical model is constructed to accurately describe the vehicle dynamics of articulated vehicles. This model is combined with the GRI to predict rollover propensity. Finally, TruckSim™ software is used to determine the real rollover timing and facilitate the accurate supply of information to the rollover detection system through the GRI. This index is used to verify the simulation based on the common manoeuvres that cause rollover accidents to reduce the occurrence of false signals and effectively increase the efficiency of the rollover detection system.

Vehicle dynamic estimation with road bank angle consideration for rollover detection: theoretical and experimental studies

This article describes a method of vehicle dynamics estimation for impending rollover detection. This method is evaluated via a professional vehicle dynamics software and then through experimental results using a real test vehicle equipped with an inertial measurement unit. The vehicle dynamic states are estimated in the presence of the road bank angle (as a disturbance in the vehicle model) using a robust observer. The estimated roll angle and roll rate are used to compute the rollover index which is based on the prediction of the lateral load transfer. In order to anticipate the rollover detection, a new method is proposed in order to compute the time-to-rollover using the load transfer ratio. The used nonlinear model is deduced from the vehicle lateral dynamics and is represented by a Takagi–Sugeno (TS) fuzzy model. This representation is used in order to take into account the nonlinearities of lateral cornering forces. The proposed TS observer is designed with unmeasurable premise variables in order to consider the non-availability of the slip angles measurement. Simulation results show that the proposed observer and rollover detection method exhibit good efficiency.

SafeDriving: A mobile application for tractor rollover detection and emergency reporting

This paper introduces a tractor rollover detection and emergency reporting software application, SafeDriving, developed for iOS-based mobile electronic devices (smartphones and tablet computers). The SafeDriving application uses a mathematical model to calculate the stability of a tractor using the physical parameters of the tractor and the data from the inbuilt sensors of the mobile electronic devices or the data from the external sensors attached to the tractor. When the SafeDriving application detects an accident, it sends automated email and phone messages with the GPS location, date, time and other critical information. The functions of the SafeDriving application were tested on a small-scale model tractor in laboratory conditions and on a 45 HP utility tractor in field conditions. The field-upset tests with the utility tractor were conducted for 10 times at 10.8km/h and 21.6km/h. During all of the tests, the application successfully monitored the vehicle stability and reported the accidents to the emergency contacts when a rollover accident happened.