The work undertaken in this research aims to develop a mathematical model which can replicate the behaviour of a racing driver controlling a vehicle at its handling limit. Most of the models proposed in the literature assume a perfect driver. A formulation taking human limitations into account would serve as a design and simulation tool for the automotive sector. A nonlinear vehicle model with five degrees of freedom under the action of external disturbances controlled by a Linear Quadratic Regulator (LQR) is first proposed to assess the validity of state variances as stability metrics. Comparison to existing stability and controllability criteria indicates that this novel metric can provide meaningful insights into vehicle performance. The LQR however, fails to stabilise the vehicle as tyres saturate. The formulation is extended to improve its robustness. Full nonlinear optimisation with direct transcription is used to derive a controller that can stabilise a vehicle at the handling limit under the action of disturbances. The careful choice of discretisation method and track description allow for reduced computing times. The performance of the controller is assessed using two vehicle configurations, Understeered and Oversteered, in scenarios characterised by increasing levels of non- linearity and geometrical complexity. All tests confirm that vehicles can be stabilised at the handling limit. Parameter studies are also carried out to reveal key aspects of the driving strategy. The driver model is validated against Driver In The Loop simulations for simple and complex manoeuvres. The analysis of experimental data led to the proposal of a novel driving strategy. Driver randomness is modelled as an external disturbance in the driver Neuromuscular System. The statistics of states and controls are found to be in good agreement. The prediction capabilities of the controller can be considered satisfactory.

With the growing complexity of vehicle control systems it is becoming increasingly important to understand the interaction between drivers and vehicles. Existing driver models do not adequately characterise limitations resulting from drivers’ physical systems. In particular, sensory dynamics limit the ability of drivers to perceive the states of real or simulated vehicles. Therefore, the aim of this thesis is to understand the impact of sensory dynamics on the control performance of a human driver in real and virtual environments. A new model of driver steering control is developed based on optimal control and state estimation theory, incorporating models of sensory dynamics, delays and noise. Some results are taken from published literature, however recent studies have shown that sensory delays and noise amplitudes may increase during an active control task such as driving. Therefore, a parameter identification procedure is used to fit the model predictions to measured steering responses of real drivers in a simulator. The model is found to fit measured results well under a variety of conditions. An initial experiment is designed with the physical motion of the simulator matching the motion of the virtual vehicle at full scale. However, during more realistic manoeuvres the physical motion must be scaled or filtered, introducing conflicts between measurements from different sensory systems. Drivers are found to adapt to simple conflicts such as scaled motion, but they have difficulty adapting to more complicated motion filters. The driver model is initially derived for linear vehicles with stochastic target and disturbance signals. In later chapters it is extended to account for transient targets and disturbances and vehicles with nonlinear tyres, and validated once again with experimental results. A series of simulations is used to demonstrate novel insights into how drivers use sensory information, and the resulting impact on control performance. The new model is also shown to predict difficulties real drivers have controlling unstable vehicles more reliably than existing driver models.

La miniaturisation de circuits intégrés, les hautes fréquences de fonctionnement, la baisse des potentiels d'alimentation, les fortes densités d'intégration rendent les signaux numériques propagés sur les interconnexions très susceptibles à la dégradation voire à la corruption. En vue d’évaluer la compatibilité électromagnétique et l’intégrité du signal il est nécessaire de disposer dès les premières phases de développement de modèles précis de ces interconnexions pour les insérer dans les simulateurs temporels. Nos travaux s'inscrivent dans ce contexte et concernent plus particulièrement la modélisation comportementale des buffers et drivers de ligne de transmission. Ils ont abouti à une approche originale de modélisation notamment basée sur les séries de Volterra-Laguerre. Les modèles boites noires développés disposent d’une implémentation SPICE assez simple autorisant ainsi une très bonne portabilité. Ils sont faciles à identifier et disposent d’une complexité paramétrique permettant un gain important de temps de simulation vis-à-vis des modèles transistors des drivers. En outre les méthodes développées permettent une modélisation dynamique non linéaire plus précise du port de sortie, et une gestion plus générale des entrées autorisant notamment une très bonne prise en compte du régime de sur-cadencement ce que par exemple ne fait pas le standard IBIS. / Integrated circuits miniaturization, high operating frequencies, lower supply voltages, high-density integration make digital signals propagating on interconnects highly vulnerable to degradation. Assessing EMC and signal integrity in the early stages of the design flow requires accurate interconnect models allowing for efficient time-domain simulations. In this context, our work addressed the issue of behavioral modeling of transmission line buffers, and particularly that of drivers. The main result is an original modeling approach partially based on Volterra-Laguerre series. The black box models we developed have a fairly simple implementation in SPICE thus allowing a very good portability. They are easy to identify and have a parametric complexity allowing a large gain in simulation time with respect to transistor driver models. In addition, the developed methods allow a more accurate output port nonlinear dynamics modeling, and a more general management of inputs. A very good reproduction of driver behaviour in overclocking conditions provides a significant advantage over standard IBIS models.

When further demands are placed on emissions and performance of cars, trucks and busses, the vehicle manufacturers are looking to have cheap ways to evaluate their products for specific customers' needs. Using simulation tools to quickly compare use cases instead of manually recording data is a possible way forward. However, existing traffic simulation tools do not provide enough detail in each vehicle for the driving to represent real life driving patterns with regards to road features. For the purpose of this thesis data has been recorded by having different people drive a specific route featuring highway driving, traffic lights and many curves. Using this data, models have then been estimated that describe how human drivers adjust their speed through curves, how long braking distances typically are with respect to the driving speed, and the varying deceleration during braking sequences. An additional model has also been created that produces a speed variation when driving on highways. In the end all models are implemented in Matlab using a traffic control interface to interact with the traffic simulation tool SUMO. The results of this work are promising with the improved simulation being able to replicate the most significant characteristics seen from human drivers when approaching curves, traffic lights and intersections.