A Novel Rollover Warning Approach for Commercial Vehicles Using Unscented Kalman Filter

Roll responses of the semitrailer and the tractor provide higher lead time and characterise the roll instability of the commercial vehicles subjected to directional manoeuvres at highway speeds. This paper proposes a novel rollover index based on the synthesized roll angles of the tractor and trailer. Owing to the poor measurability, the unscented Kalman filter (UKF) algorithm is used to estimate the roll angle of the track and trailer, respectively. Meanwhile, different weight coefficients are considered in the rollover index to eliminate the influence of mutual coupling between the tractor and the trailer and improve the accuracy of the warning. For the practical implementation of the algorithm, a two-stage rollover warning method triggered by the video and audio is finally proposed to reduce the possibilities of false warnings. Co-simulation is presented to prove the validity of the proposed rollover warning approach.

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).

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.

The Rollover Risk in Delta Tricycles: A New Rollover Index and Its Robust Mitigation by Rear Differential Braking

Although there are efforts to electrify and diversify small vehicles, active safety on motorcycles and tricycles (also known as auto rickshaw, tuk-tuk, mototaxi, etc.) has been relegated until a few years ago. For instance, the electric tricycles (even the combustion ones) marketed today do not have an active safety system that prevents or mitigates the risk of rollover, despite how prone they are to such a situation. The concern for the increase in its marketing is latent and, unfortunately, there are very few related studies. In this article, we present the obtaining and validation of a new rollover index for tricycles demonstrating its effectiveness in predicting and detecting the risk, even statically, by means of a simple quantity. In addition, a controller for the mitigation of the risk of rollover is presented which, by means of a Lyapunov type analysis, it is shown to be robust to changes in parameters, such as the center of gravity height, using a polytopic representation of the system and a differential braking strategy on the rear wheels. Numerical simulations, including video simulation captures, of the operation of the rollover mitigation system using widely recognized commercial software, are also presented. This work can be extended to vehicles with a suspension system or for trikes without autocamber.

An Improved Rollover Index Based on BP Neural Network for Hydropneumatic Suspension Vehicles

The 3-DOF rollover model has been established by the Lagrangian second-class equation, taking the road inclination angle, the steering strategy, and the hydropneumatic suspension characteristics into consideration. A 3-layer BP (backpropagation) neural network is applied to predict the road inclination angle and to optimize the rollover model in real-time. The number of the hidden layer neurons for the BP network is also discussed. The numerical calculation of the optimized rollover model is in good agreement with the full-scale vehicle test. Different rollover indexes are compared, and the results indicate that the rollover index of dynamic LTR optimized by the BP neural network can evaluate the rollover tendency more accurately in the ramp steering test and the snake steering test. This study provides practical meanings for developing a rollover warning system.