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A Time-To-Rollover (TTR) metric is proposed as the basis to assess rollover threat for an articulated heavy vehicle. The TTR metric accurately "counts-down" toward rollover regardless of vehicle speed and steering patterns, so that the level of rollover threat is accurately presented. There are two conflicting requirements in the implementation of...
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... training set should include three types of maneuvers: mild, bad, and worst-case (see Fig. 4). The motivation for using the worst-case [17,18] maneuver is to help train the NN to be as complete as possible so that the NN can handle the unseen maneuvers as well. After training, the NN will be used to generate a corrected TTR (NN-TTR) across all vehicle speeds and steering patterns. ...
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... evaluation scenarios (shown in Table 2) were then used to validate the performance of the NN. These maneuvers can be grouped into three categories (see Fig. 4) according to the time at which a rollover develops: Mild (ramp steering), Bad (ramp entering and obstacle avoidance), and Worst-Case. Statistical results are reported in Table 3 for these three categories, and representative results for the four driving patterns are shown in Fig. 14. Dynamic Systems, Measurement and Control, Vol.127, ...
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... The analysis of the dynamic stability of a WMRT can be achieved using several models: Zero Moment Point [31,[35][36][37]; Dynamic Energy Stability Measure [38]; Time-To-Rollover Metric [39,40]; Rollover Stability Advisor [41]; Lateral Load Transfer [42][43][44]. In the case of wheeled mobile platforms, the most adequate model is the one based on the Stability Moment (SM) [45,46]. ...
This paper focuses on the minimum-time trajectory planning in a structured environment for a wheeled mobile robot with a trailer. The constraints which are accounted for include: obstacle avoidance, boundary conditions on the position/orientation of both the tractor and the trailer, maximum articulation angle between the two platforms, kinodynamic capacities of the actuators, and dynamic stability of the system. The proposed technique is based on the random-profile approach, which uses a versatile master–slave optimization scheme. It is robust enough to handle different scenarios (free or imposed path) and different structures of the system (on-axle or off-axle articulation). Simulation results show that it converges, in reasonable runtimes, towards good-quality solutions which are saturating the constraints. A preliminary experimental test suggests that the planned trajectories are realistically achievable.
... The analysis of the dynamic stability of a WMRT can be achieved using several models: Zero Moment Point [31,[35][36][37]; Dynamic Energy Stability Measure [38]; Time-To-Rollover Metric [39,40]; Rollover Stability Advisor [41]; Lateral Load Transfer [42][43][44]. In the case of wheeled mobile platforms, the most adequate model is the one based on the Stability Moment (SM) [45,46]. ...
This paper focuses on the minimum-time trajectory planning in a structured environment for a wheeled mobile robot with a trailer. The constraints which are accounted for include: obstacle avoidance, boundary conditions on the position/orientation of both the tractor and the trailer, maximum articulation angle between the two platforms, kinodynamic capacities of the actuators, and dynamic stability of the system. The proposed technique is based on the random-profile approach, which uses a versatile master–slave optimization scheme. It is robust enough to handle different scenarios (free or imposed path) and different structures of the system (on-axle or off-axle articulation). Simulation results show that it converges, in reasonable runtimes, towards good-quality solutions which are saturating the constraints. A preliminary experimental test suggests that the planned trajectories are realistically achievable.
... These indexes are the critical static-rollover value measured in the vehicle static test, neglecting the vehicle dynamic characteristics. In recent years, rollover prevention energy reserves [15], time to rollover (TTR) [16], and other dynamic rollover prediction indexes have been presented. Among them, the TTR algorithm is simple and easy to transplant for controlling circuits and has thus been extensively researched. ...
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.
... Rollover prevention control design is commonly based on a rollover index (RI). RI is used to evaluate rollover risk, and various definitions are provided in literature, such as a rollover threshold determined by a linear combination of roll angle and roll rate [26], zero-moment point (ZMP) [27], time-to-rollover (TTR) [28], lateral load transfer ratio (LTR) [29], etc. To prevent dependence on varied parameters, a rollover detection approach based on pulsed braking excitation was proposed in [30] to implement anti-rollover control, in which LTR was used as the RI to improve the roll stability. ...
Most controllers concerning lateral stability and rollover prevention for autonomous vehicles are designed separately and used simultaneously. However, roll motion influences lateral stability in cornering maneuvers, especially at high speed. Typical rollover prevention control stabilizes the vehicle with differential braking to create an understeering condition. Although this method can prevent rollover, it can also lead to deviation from a reference path specified for an autonomous vehicle. This contribution proposes and implements a coupled longitudinal and lateral controller for path tracking via model predictive control (MPC) to simultaneously enforce constraints on control input, state output, lateral stability, and rollover prevention. To demonstrate the approach in simulation, an 8 degrees of freedom (DOF) vehicle model is used as the MPC prediction model, and a high-fidelity 14-DOF model as the plant. The MPC-based lateral control generates a sequence of optimal steering angles, while a PID speed controller adjusts the driving or braking torque. The lateral stability envelope is determined by the phase plane of yaw rate and lateral velocity, while the roll angle threshold is derived from the load transfer ratio (LTR) and tire vertical force under the condition of quasi-steady-state rollover. To track the desired trajectory as fast as possible, a minimum-time velocity profile is determined using a forward-backward integration approach, subject to tire friction limit constraints. We demonstrate the approach in simulation, by having the vehicle track an arbitrary course of continuously varying curvature thus highlighting the accuracy of the controller and its ability to satisfy lateral and roll stability requirements. The MATLAB® code for the 8-DOF and 14-DOF vehicle models, along with the implementation of the proposed controller are available as open source in the public domain.
... He has proposed lateral There are more recent efforts on the development of dynamic rollover threat indicators that utilize a model of the vehicle for online extrapolation of the vehicle motion into the future until a future rollover event posibility is detected. The Time to Rollover (TTR) metric has been used quite frequently (Chen and Peng 2001, 1999a, 1999b) as a basis for model based rollover threat determination. The critical rollover event is usually defined as tire lift-off or the sprung mass roll angle exceeding a pre-defined value. ...
... As commercial vehicles have slower dynamics as compared to SUV's and cars, they also have larger TTR values. Most SUV's have TTR values of 0.5 sec or less (Chen and Peng, 1999a) necessitating the use of a rollover avoidance controller. ...
Road vehicle safety systems can be broadly classified into the two categories of passive and active systems. The aim of passive safety systems is to reduce risk of injury to the occupants of the vehicle during and after an accident like a crash or rollover. Passive safety systems include the design of safety restraints, design for crashworthiness, seat belts and air bags. In contrast to passive systems, the aim in active safety is to prevent an accident from occurring in the first place. As such, it makes sense to call them preventive systems also. Here, the concentration is on preventive and active safety systems. The current state of the art in some key preventive and active safety systems is presented in this paper, wherein the various techniques used are also explained briefly. In some cases, the presentation is complemented with results obtained in the research group of the author. A road map of expected future developments in the area of preventive and safety applications is also presented.
... 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. ...
... Being cost e ective due to reduction in labour requirements and fuel consumption, and large load carrying capacity compared to single unit vehicles, Articulated Heavy Vehicles (AHVs) are among the most widely used means of road transport [1,2]. On the other hand, AHVs impose higher highway safety concerns which are di erent from single unit vehicles; heavy weights, large dimensions, geometrical con guration, high centres of gravity, and inner forces acting on their articulation points are the main sources for these di erences [3]. ...
... Equations (1) to (3) correspond to the tractor's lateral, yaw, and roll motions, respectively. ...
Articulated heavy vehicles have some specific performance limitations and safety risks due to their special dynamic characteristics. They show poor manoeuvrability at low speeds and may lose their stability in different manners at high speeds. In this study, the potential of active steering control of the semitrailer on manoeuvrability and stability of tractor-semitrailer combinations is investigated. A linear bicycle model and a nonlinear version are used for controller design and vehicle dynamic simulation in MATLAB environment. The Linear Quadratic Regulator optimal state feedback control is used to minimise the tracking error at low-speed, and regulate Rearward Amplification ratio and roll at high-speed. Quantum Particle Swarm Optimisation is used for optimising the weighting factors. Three different control algorithms are introduced and it is demonstrated through simulations that the vehicle with the proposed steering control exhibits desirable improvements compared to the baseline vehicle.
... The accelerometer of the device is ADXL103 with measurement range ±1.7g and its low-pass filter bandwidth can be selected with capacitor placed at the output pin. The acceleration acquisition circuit is shown in figure 3 [7]. ...
In view of the disadvantages of high fault rate of tie-line and angle dynamometer and high-power consumption of the wireless dynamometer in normal mode, a kind of wireless dynamometer with low-power mode is designed. The dynamometer can measure dynamometer card and transmit data not only according to commands received but also automatically according to the established procedure. A kind of comprehensive filtering algorithm is designed, which can effectively remove high-frequency and peak interference of the acceleration data. The hardware circuit, working mode and filtering algorithm are detailed. Tests of energy consumption and dynamometer card are given. The field application shows that the device has the advantages of high reliability, accurate measurement and low-power consumption.
... Nowadays, many studies accounting for the roll dynamics have been published [21,22]. Many vehicle roll stability metrics, such as the static stability factor (SSF) [23], stability moment (SM) [24], load transfer ratio (LTR) [25], time-to-rollover (TTR) [26], zero-moment-point (ZMP) [27], etc., have also been discussed. While these metrics do provide important results under certain conditions, they are limited by their inherent assumptions. ...
This paper presents a model predictive control (MPC) scheme for the stabilization of high-speed autonomous ground vehicles (AGVs) considering the effect of road topography. Accounting for the road curvature and bank angle, a single-track dynamic model with roll dynamics is derived. Variable time steps are utilized for vehicle model discretization, enabling collision avoidance in the long-term without compromising the prediction accuracy in the near-term. Accordingly, safe driving constraints including the sideslip envelope, zero-moment-point and lateral safety corridor are developed to handle stability and obstacle avoidance. Taking these constraints into account, an MPC problem is formulated and solved at each step to determine the optimal steering control commands. Moreover, feedback corrections are integrated into the MPC to compensate the unmodeled dynamics and parameter uncertainties. Comparative simulations validate the capability and real-time ability of the proposed control scheme.
... To circumvent difficulties in building an accurate yet an efficient vehicle dynamic model for real-time application, substitutive models are studied. Chen and Peng [18] constructed a Neural Network TTR (NN-TTR) system to predict rollover risk for articulated heavy vehicles. The NN took the TTR generated by a simple vehicle model, the roll angle and its variation to generate an enhanced NN-TTR index, and was trained and verified under a variety of driving patterns. ...
The 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.
