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This study concerns the comparative investigation of two advanced lateral stability automotive controllers with respect to a commercial solution. The research aims to improve the stability performances achieved by a combined tracking of yaw rate and side-slip angle through the application of optimal efforts. The proposed solutions are based on Linear Quadratic Regulation and Sliding Mode Control, respectively. Both rely on the same approach for the control objective definition but differ from the action perspective. This solution involves the adoption of a differential braking actuation technique to deliver a desired yaw moment to the car body to track controlled states. Indeed, a sliding controller can also traction torques of hub-motor configurations as well as steering corrections, achieving vehicle stability and a driving response in accordance with the pilot's intentions. Calibration and validation of the controllers are performed through a Hardware-in-the-Loop simulation rig, along with a real-time static simulator, performing different close-loop maneuvers to assess achievements in terms of lateral stability. Results show that both solutions ensure higher handling performances if compared to Non-controlled or Commercial-controlled vehicle scenarios. [ABSTRACT FROM AUTHOR]

Driving simulators have boosted the vehicle design with the introduction of human beings in the simulation loop. For a realistic functioning, the steering system must provide an accurate behaviour, since the hand wheel is a crucial human interface. Despite a large diffusion of steering models, this paper deals with the creation of a specific solution for real-time applications, characterized by precise features as numerical stability and low computational cost. The proposed model is based on a physical structure and considers all the key phenomena, such as the system elasticities, the power steering effects and friction hysteresis, making the model more accurate in terms of steering wheel torque and lateral acceleration than other angle-driven models. Its two degrees of freedom design allows a proper behaviour of the power steering sub-model; another key aspect is the friction model: the use of the LuGre formulation greatly improves accuracy and stability in comparison to the lookup table friction models. Compared to the literature reference torque-driven model, it does not need the use of a torque sensor when implemented in driving simulators having an angle-driven formulation (the input of the steering wheel is its angle and the torque needed is its output), hence it is cheaper to implement; nevertheless, its accuracy is close to state-of-art reference. An original parametrization procedure is proposed since a generalized one is not available in literature; using a steering test-rig, all the model variables are defined. The validation phase combines offline and online simulations, assessing objectively and subjectively the model's capabilities and showing accurate results in terms of steering wheel torque, lateral acceleration and steering feeling. In addition, a minor contribution of this paper shows how different analyses (steering effort evaluation, experimental data comparison or simulator feedback computation) require different output torques. [ABSTRACT FROM AUTHOR]

Vehicle dynamics stability control systems rely on the amount of so-called sideslip angle and yaw rate. As the sideslip angle can be measured directly only with very expensive sensors, its estimation has been widely studied in the literature. Because of the large non-linearities and uncertainties in the dynamics, model-based methods are not a good solution to estimate the sideslip angle. On the contrary, machine learning techniques require large datasets that cover the entire working range for a correct estimation. In this paper, we propose an integrated artificial neural network and unscented Kalman filter observer using only inertial measurement unit measurements, which can work as a standalone sensor. The artificial neural network is trained solely with numerical data obtained with a Vi-Grade model and outputs a pseudo-sideslip angle which is used as input for the unscented Kalman filter. This is based on a kinematic model making the filter completely transparent to model uncertainty. A direct integration with integral damping and integral reset value allows the estimation of the longitudinal velocity of the kinematic model. A modification strategy of the pseudo-sideslip angle is then proposed to improve the convergence of the filter's output. The algorithm is tested on both numerical data and experimental data. The results show the effectiveness of the solution. [ABSTRACT FROM AUTHOR]

The performance of semi-active differential systems is deeply influenced by the way in which they are actuated. Conventional semi-active systems are actuated by pressure-controlled hydraulic actuators that, despite their good dynamical behaviour, are relatively inefficient. In this work, the authors propose an innovative controlled hydraulic pump solution that is able to drastically improve the efficiency and to reduce the overall encumbrances, maintaining very high performance in terms of dynamical behaviour and corresponding frequency response. The authors investigate design criteria of the proposed solution, describe a simulation model and validate design and simulation models with experimental data. [ABSTRACT FROM AUTHOR]

Road accident risk assessment is a complex topic due to the large number of factors determining it and to the difficulties to collect data. In addition, most exposure factors influencing crash probability, such as environment and driver characteristics, are dependent on each other, so that it is not intuitive to devise a cause-effect scenario. The use of telematics devices, recently spreading among insurance and rental companies, provides new chances to collect exposure data, to define interpretive models of accident risk, and to explain variables relationships. Using global positioning system (GPS) data available through a long term rental company, the authors studied a sample of 900 vehicles. The authors aggregated raw data (e.g., road type covered, time, speed) in exposure metrics and organized them in a relational database. The authors built a number of multivariate logistic regression models, adopting a strategy to progressively refine them. The authors obtained a relatively high model fits (up to pseudo R2 0.301, Hosmer-Lemeshow p value 0.206) acquiring insights about the nonlinear association between explanatory variables and their outcomes. Interactions between variables were also examined. The results are, in general, in accordance with similar studies; regarding certain observed discrepancies, a discussion is provided to explain their origin, starting from the differences in associating predictors, outcome and interaction variables. [ABSTRACT FROM AUTHOR]