This thesis describes the concept of a reconfigurable wheelbase for a car as a way to improve agility at higher velocities. Altering the longitudinal position of each wheel with respect to the centre of gravity, leads to a change in lateral wheel forces. This affects the car's yaw rate, therefore making it possible to influence its agility. Available literature has shown that this subject is still quite weakly investigated, which makes it an interesting topic of research. To investigate this influence of actively reconfiguring the wheelbase, simulations were done using IPG Carmaker together with MATLAB/Simulink, during which a simplified vehicle model of a Toyota Camry was used. A proportional derivative controller combined with control allocation was designed to regulate the reconfigurable wheelbase system. It features a reference generator that returns a desired yaw rate and activation logic which ensures that the controller is only activated in a velocity range of 60 km/h to 110 km/h and when a steering input is given. Furthermore, a benchmark study is designed, using a fuzzy logic controller that is based on a simplified version of one presented by Soltani et al. (2017). This controller uses the same reference generator and activation logic for consistency. The rules and membership functions of the fuzzy logic block are adopted from the mentioned paper. The performance of both these controllers and a baseline vehicle without any control, are evaluated for a step steer manoeuvre, an increasing circle manoeuvre and a double lane change, at different velocities and/or steering amplitudes. Based on the results of these tests, it is shown that both controllers are able to influence the lateral dynamics positively during most of these manoeuvres and are therefore able to improve the vehicle's agility in the velocity range in which the controller is active.

A vehicle that is travelling at high sideslip angles can still be controlled by drifting. Implemented into a vehicle, this phenomenon could lead to increased vehicle safety and performance. Additionally, it could lead to higher acceptance rates of autonomous driving.
In this work a three state vehicle model is used. Using this model, simulations are performed with varying vehicle parameters and tyre models. First, the mathematical descriptions of all used models are stated, after which phase plane representations for the identification of drift equilibria are elaborated on. Next, different steering characteristics and their influence on the drift equilibrium points are considered. These characteristics are achieved by varying the lateral rear tyre stiffness. The effect of
three different types of tyre models on the drift equilibria is discussed. The models that are considered are the linear tyre model, Dugoff tyre model and Magic Formula model.
In addition to this, the effects of the location of the centre of gravity, the vehicle mass and the cornering stiffness are investigated. Finally the outcome of the Dugoff tyre model and the Magic Formula model for construction of drift equilibria is shown and discussed.
Contrary to the Dugoff and Magic Formula model, no drift equilibria were found using the linear tyre model. Using the Dugoff model, drift equilibrium points were found for the understeering and neutral steering vehicle, whereas the oversteering vehicle showed drift equilibrium ranges. Finally, drift equilibria depend on vehicle parameters, showing change in behaviour when the latter are varied.

Accurate and robust vehicle state estimation is important for proper operation of vehicle control systems. For lateral stability control accurate estimation of the Vehicle Sideslip Angle (VSA) is of utmost importance. This thesis aims to develop an accurate and highly robust model to estimate the VSA. Common vehicle sensors such as the Inertial Measurement Unit (IMU), Global Positioning System (GPS) and steering angle sensor are used to provide measurements of vehicle states that are relatively easy to directly measure. Additionally, wheel bearing strains measurements are collected by the Load-­Sensing Bearing (LSB), which can be mapped to the tire forces that are measured by wheel force transducers. Insight regarding the tire forces could be beneficial to VSA estimation. The focus lies upon neural network estimators and hybrid structures, which combine the neural network estimator with an observer model.
A large-­scale experimental dataset composed of standardised vehicle manoeuvres is used to develop and evaluate different estimation architectures. The dataset consists of 216 manoeuvres, which corresponds to approximately two hours of driving time.

Neural network estimators are data-­driven and therefore rely on high quality data. Various neural network architectures exist for the purpose of time series estimation. In this thesis the Feedforward Neural Network (FFNN), Recurrent Neural Network (RNN) and Transformer are examined in detail. The FFNN
and RNN yield similar performance in terms of Root Mean Squared Error (RMSE) and Maximum Error (ME) on the test set. The Transformer is unable to match this performance level due to the absence of a proper positional encoding of the measurements. Due to the simpler structure and lower data requirement, the FFNN is selected for application in the hybrid structures.
To create a hybrid estimator, the uncertainty level of the VSA estimate from the neural network is required. To obtain the uncertainty using a neural network, various methods exist such as Monte Carlo Dropout (MCDO), Monte Carlo Batch Normalisation (MCBN) and the Uncertainty Deep Ensemble (UDE). These methods are compared using three metrics, the RMSE, Predictive Loglikelihood (PLL) and Continuous Ranked Probability Score (CRPS). A combination of these metrics does not only evaluate the estimation accuracy, but also the quality of the corresponding estimated uncertainty level. The UDE consisting of
FFNNs provides the best performance and even outperforms the single FFNN in terms of RMSE by a decrease of 8.6%. Therefore, the UDE is used to develop the different hybrid structures.

The Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) are built on a nonlinear bicycle model. Different sets of inputs for the observers, as well as constant or adaptive covariance matrices create different variations. These observer variations are combined with the UDE to form hybrid estimation
models. The different variations show equal performance due to the high accuracy of the UDE on the test set. The high performance of the UDE causes the measurement noise to be tuned to a very low level, which results in the observer ’trusting’ the UDE estimations. Therefore, the performance of the different
hybrid structures is almost equal to that of the original UDE.

To investigate if the observer is actually able to correct neural network estimations, a suboptimal neural network is created. This network is trained only on measurements with an absolute lateral acceleration lower than 5 m/s2. This mimics sparse data of high sideslip angles, a common problem for data­-driven
approaches in this setting. This causes the uncertainty of the neural network estimations to be higher in these operating regions. A linear scaling of the uncertainty level to match the variance of the noise on the other measurements does not yield satisfactory results. However, an exponential scaling of the
uncertainty level causes a higher differentiation between low and high confidence levels. This exponential scaling decreases the RMSE of the hybrid structure with 11.4 %. Furthermore, a method for adapting the process noise based on the quality of the observer model estimation is implemented. This equates to
similar performance in terms of RMSE, but introduces a number of additional parameters to tune.

Full vehicle automation requires complete control over all driving scenarios that can be encountered on roads in order to ensure passenger safety at all times. This extends to dangerous situations such as losing control on slippery surfaces, commonly known as drifting. This work aims to make a step toward ensuring passenger safety in these situations by gaining control over vehicle behavior in high side slip regions. As these regions tend to be highly nonlinear and drifting dynamics happen at a millisecond scale, the time availability for generating proper commands is highly restricted. This work further proposes a method based on Nonlinear Model Predictive Controller (NMPC) that conveniently splits and reformulates the drifting problem to reduce the computational burden of the control structure and make it suitable for real-time implementations. The overall performance and robustness will be evaluated in obstacle-avoidance scenarios and in more general driving situations.