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Vehicle-trailer systems have various unstable modes including trailer snaking, jack-knifing, and roll-over, which should be considered in their stability control. For stability control design purposes, various techniques have been proposed to estimate vehicle-trailer system states and parameters. Some of these techniques rely on vehicle kinematic/dynamic models while others are data-driven and do not require a model. This review paper provides a comprehensive overview of different model-based and non-model-based techniques/algorithms developed for estimating vehicle-trailer system states and parameters. The main features, limitations, and assumptions for each estimation method are discussed. The trailer parameter estimation feasibility is also investigated for different possible vehicle-trailer on-board sensor settings. This paper can be used as a review and reference resource for engineers working in vehicle with semi-trailer state estimation and safety systems. [ABSTRACT FROM AUTHOR]

Trailer mass is one of the important trailer parameters that affects the stability of the tractor-trailer systems. In this paper, two different approaches are proposed to estimate trailer mass for arbitrary tractor-trailer configurations; dynamic system model-based and Machine Learning (ML) approaches. The stability of the dynamic system model-based estimation algorithm is analyzed, establishing the convergence of the estimation error to zero. In the proposed ML-based approach, a Deep Neural Network (DNN) is designed to estimate trailer mass. The inputs of the ML-based method have been selected based on the tractor-trailer dynamic model, and are considered to be normalized by the tractor mass, tire sizes, and geometry so that re-training of the network is not needed for different towing vehicles. The simulation and experimental results justify the accuracy of the trailer mass estimation in various cases and demonstrate that the trailer mass can be estimated with less than 10% error. [ABSTRACT FROM AUTHOR]

An opinion dynamics approach is proposed to enhance the reliability of the vehicle velocity estimators, which are required for autonomous driving as well as advanced vehicle active safety systems, such as traction and stability control. The corners’ estimates of a velocity observer, which is formed by combining the kinematic and model-based estimation schemes, are used as opinions with different levels of confidence in the developed algorithm. This is to find more reliable estimates robust to disturbances and time delay via solving a convex optimization problem. To bypass the effect of failure in velocity estimation, a fault rejection policy is used concurrently with the opinion dynamics. Road tests confirm the validity and robustness of the algorithm on slippery and dry roads independent of the powertrain configuration in different driving scenarios, especially for combined-slip and low-excitation maneuvers, which are demanding for the current vehicle state estimators. [ABSTRACT FROM AUTHOR]

This paper presents longitudinal and lateral velocity estimators by considering the effect of the suspension compliance (SC) at each corner (tire) for ground vehicles. The estimators are developed to be resilient to sensor measurement inaccuracies, model and tire parameter uncertainties, switchings in observer gains, and measurement failures. More particularly, the stability of the observer is investigated, and its robustness to road condition uncertainties and sensor noises is analyzed. The sensitivity of the observers’ stability and performance to the model parameter changes is discussed. Moreover, the stability of the velocity observers for two cases of arbitrary and stochastic switching gains is investigated. The stochastic stability of the observer in the presence of faulty measurements is also studied, and it is shown that if the probability of a faulty measurement occurring is less than a certain threshold, the observer error dynamics will remain stochastically stable. The performance of the observer and the effect of the SC are validated via several road experiments. [ABSTRACT FROM PUBLISHER]

We consider a graph-theoretic approach to the performance and robustness of a platoon of vehicles, in which each vehicle communicates with its $k$ -nearest neighbors. In particular, we quantify the platoon’s stability margin, robustness to disturbances (in terms of system \mathcal {H}_{\infty } norm), and maximum delay tolerance via graph-theoretic notions, such as nodal degrees and (grounded) Laplacian matrix eigenvalues. The results show that there is a trade-off between robustness to time delay and robustness to disturbances. Both first-order dynamics (reference velocity tracking) and second-order dynamics (controlling inter-vehicular distance) are analyzed in this direction. Theoretical contributions are confirmed via simulation results. [ABSTRACT FROM PUBLISHER]

Driver-in-the-loop stability is a central issue in vehicle control systems. However, since a general human behavior model to explore it in a quantitative fashion has been lacking, little is known about how the vehicle can be controlled while considering the driver effects. Indeed, applying a control method without considering the driver effects, and instead separating human level and machine dynamic layers, guaranteeing stability of a vehicle, is impossible. Here, a new formulation of the problem that involves a driver model and a linear vehicle model is proposed. Given that practical controllers usually do not have access to the future road preview data, this information is also modeled in terms of bounded uncertainties. The design allows the tools of robust control to stabilize the system, offering an implementable approach to overcome ranges of delay and uncertainties of closed-loop modeling due to the human presence. The formulation can further deepen the understanding of the effects of a driver during vehicle steering. To evaluate the proposed controller, a nonlinear full vehicle model along with a driver model in CarSim are used. The simulations performed for a standard harsh double-lane-change scenario under different driver and vehicle conditions demonstrate that vehicle stability is enhanced using the proposed controller. [ABSTRACT FROM PUBLISHER]