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A control algorithm is developed for active/semi-active suspensions which can provide more comfort and better handling simultaneously. A weighting parameter is tuned online which is derived from two components - slow and fast adaptation to assign weights to comfort and handling. After establishing through simulations that the proposed adaptive control algorithm can demonstrate a performance better than some controllers in prior-art, it is implemented on an actual vehicle (Cadillac STS) which is equipped with MR dampers and several sensors. The vehicle is tested on smooth and rough roads and over speed bumps.

In vehicles equipped with conventional Electric Power Steering (EPS) systems, the steering effort felt by the driver can be unreasonably low when driving on slippery roads. This may lead inexperienced drivers to steer more than what is required in a turn and risk losing control of the vehicle. Thus, it is sensible for tire-road friction to be accounted for in the design of future EPS systems. This paper describes the design of an auxiliary EPS controller that manipulates torque delivery of current EPS systems by supplying its motor with a compensation current controlled by a fuzzy logic algorithm that considers tire-road friction among other factors. Moreover, a steering system model, a nonlinear vehicle dynamics model and a Dugoff tire model are developed in MATLAB/Simulink. Physical testing is conducted to validate the virtual model and confirm that steering torque decreases considerably on low friction roads.

The study of vehicle coupling state estimation accuracy especially in observer-based vehicle chassis control for improving road handling and ride comfort is a challenging task for vehicle industry under various driving conditions. Due to a large amount of life safety arising from vehicle roll behavior, how to precisely acquire vehicle roll state and rapidly provide for the vehicle control system are of great concern. Simultaneously, uncertainty is unavoidable for various aspects of a vehicle system, e.g., varying sprung mass, moment of inertia and position of the center of gravity. To deal with the above issues, a novel dual observer approach, which combines adaptive Unscented Kalman Filter (AUKF) and Takagi-Sugeno (T-S), is proposed in this paper. A full-car nonlinear model is first established to describe vehicle lateral and vertical coupling roll behavior under various road excitation.

In this paper, a consensus framework for cooperative parameter estimation within the vehicular network is presented. It is assumed that each vehicle is equipped with a dedicated short range communication (DSRC) device and connected to other vehicles. The improvement achieved by the consensus for parameter estimation in presence of sensor’s noise is studied, and the effects of network nodes and edges on the consensus performance is discussed. Finally, the simulation results of the introduced cooperative estimation algorithm for estimation of the unknown parameter of road condition is presented. It is shown that due to the faster dynamic of network communication, single agents’ estimation converges to the least square approximation of the unknown parameter properly.

In this paper the hardware and software of a real-time dynamic test bench for simulating the total road forces of vehicles fitted with Automated Manual Transmissions (AMT) is described. First, the purpose and meaning of this research are discussed. And then, we select the hardware components of the test bench system according to the application requirements and complete the system design. Statement of the structure, working principle and function of the system is also included in this part. According to the experimental procedure of simulating total road load forces of vehicle under real-time conditions on the dynamic test bench, the software system is designed using Visual C++ 6.0, CAN bus communication protocol, RS-232, and network technology. Finally, some experimental tests for the system are carried out with the results that this design corresponds to the real-time dynamic requirements.

Ride performance characteristics of a road vehicle involving different suspension system layouts are investigated. The suspension layouts consist of conventional rectangular 4-wheel, novel diamond-shaped 4-wheel, triangular 3-wheel and inverse-triangular 3-wheel. A generalized full-vehicle model integrating different suspension system layouts is formulated. The fundamental suspension properties are compared in terms of bounce-, roll- and pitch-mode. The ride dynamic responses and power consumption characteristics are explored under two measured road roughness excitations and a range of vehicle speeds. The results demonstrate that the novel diamond-shaped suspension system layout could yield significantly enhanced vehicle ride performance in an energy-saving manner.

The study of vehicle state estimation performance especially on the aspect of observer-based control for improving vehicle ride comfort and road handling is a challenging task for vehicle industry. Since vehicle roll behavior with various road excitations act an important part of driving safety, how to accurately obtain vehicle state under various driving scenes are of great concern. However, previous researches seldom consider coupling relation between vehicle vertical and lateral response with steering input under various road excitation. To address this issue, comprehension analyses on vehicle roll state estimation with coupled input are present in this paper. A full-car nonlinear Takagi-Sugeno (T-S) fuzzy model is first created to describe vehicle lateral and vertical coupling dynamics.

A compact sized vehicle that has a narrow track could solve problems caused by vehicle congestion and limited parking spaces in a mega city. Having a smaller footprint reduces the vehicle's total weight which would decrease overall vehicle power consumption. Also a smaller and narrower vehicle could travel easily through tight and congested roads that would speed up the traffic flow and hence decrease the overall traffic volume in urban areas. As an additional benefit of having a narrow track length, a driver can experience similar motorcycle riding experience without worrying about bad weather conditions since a driver sits in a weather protected cabin. However, reducing the vehicle's track causes instability in vehicle dynamics, which leads to higher possibility of rollovers if the vehicle is not controlled properly. A three wheel personal vehicle with an active tilting system is designed in MapleSim.

With the widespread development of automated driving systems (ADS), it is imperative that standardized testing methodologies be developed to assure safety and functionality. Scenario testing evaluates the behavior of an ADS-equipped subject vehicle (SV) in predefined driving scenarios. This paper compares four modes of performing such tests: closed-course testing with real actors, closed-course testing with surrogate actors, simulation testing, and closed-course testing with mixed reality. In a collaboration between the Waterloo Intelligent Systems Engineering (WISE) Lab and AAA, six automated driving scenario tests were executed on a closed course, in simulation, and in mixed reality. These tests involved the University of Waterloo’s automated vehicle, dubbed the “UW Moose”, as the SV, as well as pedestrians, other vehicles, and road debris.

Modern electrified vehicles rely on drivers to manually adjust control parameters to modify the vehicle's powertrain, such as regenerative braking strength selection or drive mode selection. However, this reliance on infrequent driver input may lead to a mismatch between the selected powertrain control modifiers and the true driving environment. It is therefore advantageous for an electric vehicle's powertrain controller to make online identifications of the current driving conditions. This paper proposes an online driving condition identification scheme that labels drive cycle intervals collected in real-time based on a clustering model, with the objective of informing adaptive powertrain control strategies. HDBSCAN and K-means clustering models are fitted to a data set of drive cycle intervals representing a full range of characteristic driving conditions.

This paper presents an optimal cooperative path planning method considering driver’s driving intention for shared control to address target path conflicts during the driver-automation interaction by using the convex optimization technique based on the natural cubic spline. The optimal path criteria (e.g. the optimal curvature, the optimal heading angle) are formulated as quadratic forms using the natural cubic spline, and the initial cooperative path profiles of the cooperative path in the Frenet-based coordinate system are induced by considering the driver’s lane-changing intention recognized by the Support Vector Machine (SVM) method. Then, the optimal cooperative path could be obtained by the convex optimization techniques. The noncooperative game theory is adopted to model the driver-automation interaction in this shared control framework, where the Nash equilibrium solution is derived by the model predictive control (MPC) approach.

This research examined the focused design, elastic design, energy decoupling, and torque roll axis (TRA) decoupling methods for mount optimization design. Requiring some assumptions, these methods are invalid for some load conditions and constraints. The linearity assumption is advantageous and simplifies both design and optimization analysis, facilitating engineering applications. However, the linearity is rarely seen in real-world applications, and there is no practical method to directly measure the reaction forces in the three locally orthogonal directions, preventing validation of existing methods by experimental results. For nonlinear system identification, there are additional challenges such as unobservable internal variables and the uncertainty of measured data.

This paper presents a road classifier based on the system response with consideration of the tire enveloping. The aim is to provide an easily applicable yet accurate road classification approach for automotive engineers. For this purpose, tire enveloping effect is firstly modeled based on the flexible roller contact (FRC) theory, then transfer functions between road input and commonly used suspension responses i.e. the sprung mass acceleration, unsprung mass acceleration, and rattle space, are calculated for a quarter vehicle model. The influence of parameter variations, vehicle velocity, and measurement noise on transfer functions are comprehensively analyzed to derive the most suitable system response thereafter. In addition, this paper proposes a vehicle speed correction mechanism to further improve the classification accuracy under complex driving conditions.

Electric Power-Assisted Steering (EPAS) is a new power steering technology that will define the future of vehicle steering. The assist of EPAS is the function of the steering wheel torque and vehicle velocity. The assist characteristic of EPAS is set by control software, which is one of the key issues of EPAS. The straight-line type assist characteristic has been used in some current EPAS products, but its influence on the steering maneuverability and road feel hasn't been explicitly studied in theory. In this paper, the straight-line type assist characteristic is analyzed theoretically. Then a whole vehicle dynamic model used to study the straight-line type assist characteristic is built with ADAMS/Car and validated with DCF (Driver Control Files) mode of ADAMS/Car. Based on the whole vehicle dynamic model, the straight-line type assist characteristic's influence on the steering maneuverability and road feel is investigated.

In order to improve the tractive ability, steering capability and directional stability, etc. of automated mechanical transmission (AMT) vehicles running on the wet and slippery road, the anti-slip regulation (ASR) technologies for AMT vehicles are developed. The significance of ASR for AMT vehicles is introduced; a road friction recognition method based on the deceleration of driving wheels is investigated; a fuzzy anti-slip control system based on adjustment of engine torque is developed and the corresponding experimental verification is conducted. The experimental results denote that the proposed method is effective to eliminate the excessive slip when the AMT vehicle travels on the low friction road.

A method to improve the computational efficiency of analyzing wheeled vehicle systems on three-dimensional (3-D) roads has been developed. This was accomplished by creating a technique to incorporate the tire on a 3-D road in a multibody dynamics model of the vehicle with an approach that formulates the governing equations using symbolic formulation. For general handling analysis performed on the vehicle, the tire forces and moments are determined using a tire model that represents the tire as a set of mathematical expressions. Since these expressions need numerical values to determine the forces and moments, a symbolic solution does not exist. Therefore, the evaluation of the tire forces and moments needs to be done during simulation. However, symbolic operations can be used when the governing equations are formulated to develop an efficient method to evaluate these forces.

Accurately predicting the future trajectories of surrounding traffic agents is important for ensuring the safety of autonomous vehicles. To address the scenario of frequent interactions among traffic agents in the highway merging area, this paper proposes a trajectory prediction method based on interactive graph attention mechanism. Our approach integrates an interactive graph model to capture the complex interactions among traffic agents as well as the interactions between these agents and the contextual map of the highway merging area. By leveraging this interactive graph model, we establish an agent-agent interactive graph and an agent-map interactive graph. Moreover, we employ Graph Attention Network (GAT) to extract spatial interactions among trajectories, enhancing our predictions. To capture temporal dependencies within trajectories, we employ a Transformer-based multi-head self-attention mechanism.