Estimation of Vehicle Longitudinal Velocity with Artificial Neural Network

Vehicle dynamics control systems have a fundamental role in smart and autonomous mobility, where one of the most crucial aspects is the vehicle body velocity estimation. In this paper, the problem of a correct evaluation of the vehicle longitudinal velocity for dynamic control applications is approached using a neural networks technique employing a set of measured samples referring to signals usually available on-board, such as longitudinal and lateral acceleration, steering angle, yaw rate and linear wheel speed. Experiments were run on four professional driving circuits with very different characteristics, and the vehicle longitudinal velocity was estimated with different neural network training policies and validated through comparison with the measurements of the one acquired at the vehicle’s center of gravity, provided by an optical Correvit sensor, which serves as the reference (and, therefore, exact) velocity values. The results obtained with the proposed methodology are in good agreement with the reference values in almost all tested conditions, covering both the linear and the nonlinear behavior of the car, proving that artificial neural networks can be efficiently employed onboard, thereby enriching the standard set of control and safety-related electronics

Tyre Model-based Approaches for Vehicle State Estimation and Control

Tyre may be one of the most critical and complex components in vehicle dynamics, it is usually the only one interfacing with the road. The pneumatic tyre has three fundamental functions: (i) generating proper forces during vehicle cornering or traction/braking, (ii) absorbing the shock and vibration caused by surface irregularity, and (iii) supporting the weight on various terrains. During the motion, due to the multi-material interaction and to the viscoelastic rubber matrix compositions, the dynamic characteristics of such part may vary considerably, even considering to modify only one parameter among inner pressure, track and ambient temperatures, pavement surface, etc. Another variable to take into account is that the structure and compound characteristics inevitably change within his life-cycle because of ageing, leading to a modification of cornering characteristics and to a decrease of the level of available grip. Therefore, starting from the earliest phases of design of the vehicle and its control systems, the understanding of tyres is critical to govern the overall dynamics. Moreover, the ability of the vehicles to drive themselves in a safe manner highly depends on their prior capability to understand the external environment and to correctly estimate the vehicle state in all the possible operating and environment conditions, which implies adverse environmental scenarios like heavy rain, snow, or ice on the road surface. Nevertheless, the current tools to estimated the vehicle state are still not designed to exploit the entire vehicle dynamics potential, preferring to assure the minimum requirements in the worst possible operating conditions instead. Furthermore, their calibration is typically performed in a pre-defined strict range of operating conditions, established by specific regulations or OEM routines. For this reason, their performance can considerably decrease in particularly crucial safety-critical situations, where the environmental conditions, the road singularities, and the tyre thermal and ageing phenomena can deeply affect the adherence potential. Hence, in order to guarantee a greater safety-level with respect to environmental conditions, it is necessary to account for their effect since from the very beginning of the ADAS design phase, introducing advanced control strategies that could leverage both real-time measurements, coming from different in-vehicles sensors (camera, radar, lidar and combinations of those via sensor-based fusion techniques, and on-board environmental estimation modules. Indeed, only the use of sensors' measurements could be not enough to perceive properly the external environment, since the vehicle control system has also to predict and discern how heavy rain, snow, ice condition or road singularities (e.g., oil stains, puddles, holes, or disconnected cobblestone) could impact on safety, so that the driving policy is to be tuned according to the actual environmental adversities. Moreover, in extreme scenarios vehicle dynamics may be deeply affected by the non-linearity of tyres' dynamic behavior, therefore limiting the maneuverability in terms of both longitudinal and lateral accelerations and significantly reducing drive-ability and steer-ability. This thesis is focused on the evaluation of the control strategy performance when a better estimated tyre and vehicle parameters are given to the control model take into account the variations in terms of the dynamic behavior of the tyres and of the vehicle boundary conditions. For these reasons the thesis flow is the following: - MULTI-PHYSICAL TYRE MODEL ANALYSIS: starting from the study of tyre's viscoelastic properties an experimental analysis on the real tyre tread specimens has been done in order to evaluate the friction coefficient dependencies with temperature, sliding velocity and wear level. Later, a multy-physical tyre model, called MF-evo, has been presented and parametrized since data carried out by outdoor track test. - VEHICLE STATE ESTIMATOR: the real-time knowledge of the correct vehicle state is needed not only to properly feed low-level control systems commonly used in commercial cars such as ABS, ESP and traction control, but also to allow the development of more accurate advanced driver assistance systems (ADAS) up to fully autonomous driving scenarios. Therefore, a benchmark on the vehicle state estimator has been presented. - MOTION PLANNING TO TAKE INTO ACCOUNT THE ESTIMATED PARAMETERS: the objective of the work is to investigate the possibility of the physical model-based control to take into account the variations in terms of the dynamic behavior of the systems and of the boundary conditions. Different scenarios with specific tyre thermal and wear conditions have been tested on diverse road surfaces validating the designed model predictive control algorithm and demonstrating the augmented reliability of an advanced virtual driver aware of available information concerning the tyre dynamic limits. - VEHICLE FOLLOWING CONTROL STRATEGY TO EXPLOIT THE FRICTION ESTIMATION ON-BOARD: a new model-based technique is proposed for real-time road friction estimation in different environmental conditions. The results, in terms of the maximum achievable grip value, have been involved in autonomous driving vehicle-following maneuvers, as well as the operating condition of the vehicle at which such grip value can be reached. -ABS CONTROL STRATEGY TO MAKE USE OF THE TYRE THERMAL DYNAMICS: a simplified tyre thermal model has been integrated into a model predictive control technique in order to exploit the thermal dynamics dependencies in an abs system to reduce the braking distance. This thesis is intended to highlight a necessary shift in strategy development and a solid step toward greater development of driving automation systems and physical modeling of vehicle control, capable of exploiting and taking into account multi-physical variations in tire

On-Board Road Friction Estimation Technique for Autonomous Driving Vehicle-Following Maneuvers

In recent years, autonomous vehicles and advanced driver assistance systems have drawn a great deal of attention from both research and industry, because of their demonstrated benefit in reducing the rate of accidents or, at least, their severity. The main flaw of this system is related to the poor performances in adverse environmental conditions, due to the reduction of friction, which is mainly related to the state of the road. In this paper, a new model-based technique is proposed for real-time road friction estimation in different environmental conditions. The proposed technique is based on both bicycle model to evaluate the state of the vehicle and a tire Magic Formula model based on a slip-slope approach to evaluate the potential friction. The results, in terms of the maximum achievable grip value, have been involved in autonomous driving vehicle-following maneuvers, as well as the operating condition of the vehicle at which such grip value can be reached. The effectiveness of the proposed approach is disclosed via an extensive numerical analysis covering a wide range of environmental, traffic, and vehicle kinematic conditions. Results confirm the ability of the approach to properly automatically adapting the inter-vehicle space gap and to avoiding collisions also in adverse road conditions (e.g., ice, heavy rain)

On-Board Road Friction Estimation Technique for Autonomous Driving Vehicle-Following Maneuvers

In recent years, autonomous vehicles and advanced driver assistance systems have drawn a great deal of attention from both research and industry, because of their demonstrated benefit in reducing the rate of accidents or, at least, their severity. The main flaw of this system is related to the poor performances in adverse environmental conditions, due to the reduction of friction, which is mainly related to the state of the road. In this paper, a new model-based technique is proposed for real-time road friction estimation in different environmental conditions. The proposed technique is based on both bicycle model to evaluate the state of the vehicle and a tire Magic Formula model based on a slip-slope approach to evaluate the potential friction. The results, in terms of the maximum achievable grip value, have been involved in autonomous driving vehicle-following maneuvers, as well as the operating condition of the vehicle at which such grip value can be reached. The effectiveness of the proposed approach is disclosed via an extensive numerical analysis covering a wide range of environmental, traffic, and vehicle kinematic conditions. Results confirm the ability of the approach to properly automatically adapting the inter-vehicle space gap and to avoiding collisions also in adverse road conditions (e.g., ice, heavy rain)

Non-Linear Model of Predictive Control-Based Slip Control ABS Including Tyre Tread Thermal Dynamics

Vehicle dynamics can be deeply affected by various tyre operating conditions, including thermodynamic and wear effects. Indeed, tyre temperature plays a fundamental role in high performance applications due to the dependencies of the cornering stiffness and potential grip in such conditions. This work is focused on the evaluation of a potentially improved control strategy’s performance when the control model is fed by instantaneously varying tyre parameters, taking into account the continuously evolving external surface temperature and the vehicle boundary conditions. To this end, a simplified tyre thermal model has been integrated into a model predictive control strategy in order to exploit the thermal dynamics’ dependents within a proposed advanced ABS control system. We evaluate its performance in terms of the resulting braking distance. In particular, a non-linear model predictive control (NMPC) based ABS controller with tyre thermal knowledge has been integrated. The chosen topic can possibly lay a foundation for future research into autonomous control where the detailing of decision-making of the controllers will reach the level of multi-physical phenomena concerning the tyre–road interaction