Abstract—This paper proposes a Nonlinear Model Predictive Control (NMPC) application for automated drifting, with exper- imental verification on a standard production vehicle, without hardware modifications. The controller stabilizes the vehicle on a high sideslip angle, which implies an equilibrium condition beyond tyre friction limits. The proposed control strategy shows feasible results that succesfully brings the vehicle towards a high sideslip state, for a variety of drifting scenarios. Simulations show that the control structure is able to sustain an automated drift along a desired path, with maximal lateral path deviation of 1 meter. An experimental implementation on a production vehicle testbench without hardware modifications has shown feasible control of bringing the vehicle into a high sideslip state, for both low- and high μ situations.

The automotive industry is continuously improving the safety and comfort of the vehicles. To improve the safety and comfort of the vehicle more focus is put on improving driver assistance systems and also on making the vehicles more autonomous. In order for the safety systems to work properly and also to drive autonomously, it becomes essential that the vehicle has accurate knowledge of all the states of the vehicle. Two of such states are the roll and pitch angle. They are used to correct the images of a stereo camera, in roll control systems and for comfort assessment in autonomous vehicles. Measuring the roll and pitch angle is in practice problematic. Gyroscopes are mostly used to calculate the roll and pitch angle by integrating the angular rates with respect to time. If low quality gyroscopes are used, this leads to inaccurate calculation of the roll and pitch angle, because the measurements contain noise or bias accumulated during the integration process. To use these low quality gyroscopes, they need to be incorporated in an robust algorithm together with other sensors. Only then the roll and pitch angle can be estimated accurately. In this thesis a dynamic observer is developed, which has an internal vehicle dynamic model that calculates the tyre forces using an exponential tyre model. The performance of the observer is not only assessed using the true roll and pitch angle, but also compared against the performance of a kinematic observer. To test the performance of both observers various test manoeuvres were executed in IPG CarMaker, which is a highly advanced vehicle simulation environment. The manoeuvres consisted of rapid steering inputs and hard braking to execute large roll and pitch angles. Sensor noise was added to the inputs of the observers to simulate low quality sensors. In this thesis it has been shown that incorporating dynamics into an observer significantly increases its estimation performance and outperforms the kinematic observer during all manoeuvres when sensor noise was added to the inputs of the observers. The contribution of this thesis to the scientific field is that an exponential tyre model is used for the calculation of the roll and pitch angle in the internal vehicle dynamic model of the dynamic observer. Another contribution is that this thesis not only compares the performance of the dynamic observer against the real roll and pitch angles, but also against a kinematic observer, to see what the estimation improvement is when including dynamic relations.

Among the trends that are going to shape the automotive industry in the coming years, autonomous vehicles stand out as having the potential to completely change the automotive industry as we know it. One of the critical tasks in this framework includes robust execution of the steering control action to maintain a pre-defined path. The main shortcomings of the state of the art steering controllers are controller robustness, cross-comparison and performance validation. To address this aspect, firstly, this study focuses on implementing a sensor model that geometrically measures the lateral and the heading error of the vehicle with respect to the path. Secondly, the focus was to implement existing lateral controllers like Stanley, Path Control with Preview (PCwP), Linear Quadratic Regulator (LQR), Immersion and Invariance (II), Passivity Based Control (PBC) and evaluate path-tracking performance. The Sliding Mode Control (SMC) methodology has proven effective in dealing with complex dynamical systems affected by disturbances, uncertainties and unmodeled dynamics. However, the application of SMC and its algorithms to lateral control in vehicles is not effectively analysed. Finally, this master thesis includes a novel design of three variants of Sliding Mode Control; namely Sliding Mode Control with Super Twisting Algorithm, Modified Super Twisting Algorithm and Non-singular Terminal Modified Super Twisting algorithm. These algorithms were then tested against external disturbances such as localisation error, cross-wind, and parametric variations. This thesis successfully illustrates in detail, the aspects involved in path-tracking control. Results effectively suggest a betterment of the novel steering controllers designed, over the previously existing Sliding Mode Control Super Twisting algorithm and other bench-marking controllers at higher speeds. The work ends with vital conclusions and recommendations for future researchers in this domain to further increase robustness and achieve better performance.

Self-driving vehicles are the future of automotive engineering. Systems that take over control from the driver are developed to be able to interact with the conditions of the road and other obstacles. To develop these systems, developers use vehicle models to simulate the behaviour of the moving vehicle. The systems developed using these models are transferred the vehicle and are expected to perform accordingly. Therefore it is important that the vehicle models reflect the behaviour of the actual vehicle. This thesis investigates the methods used to estimate the model parameters which influence the resulting vehicle simulation. Experimental tests were carried out to acquire vehicle body inertial measurement data. This data was used to minimize lateral acceleration and yaw-rate error produced by the output of a single track vehicle model using a simplified Magic formula developed by H. Pacejka. Deterministic gradient-based algorithms, such as multi-objective Sequential Quadratic Programming (SQP), are frequently used as a way to optimize an initial approximation of the model parameter set. It is shown that this initial parameter strongly influences results of the algorithm due to local minima. As an alternative, genetic algorithms were investigated to minimize the influence of the initial parameter set. An adaptive component, Temporal Difference Q Learning (TDQL), was also added to further reduce the input of the user and to increase the performance of the algorithm. Four algorithms, of various complexity, were implemented in Matlab and executed. The performance of the resulting parameter sets, as well as the performance of the algorithms themselves, were analysed and compared. It is concluded in this thesis that the adaptive genetic algorithm performs slightly better than a simple gradient-based algorithm with respect to the objectives and the duration of the algorithm. However, a genetic algorithm without TDQL is recommended for its good performance and the simulation flexibility of the results for this simple vehicle and tire model.

Haptic feedback from the steering wheel is one of the most important cues for driver to vehicle interaction. The right feedback is provided by ensuring that the haptic controller provides the required steering feel. Steering feel assessment and design is divided into a subjective and objective approach. The subjective approach entails experiments on the proving ground during which steering parameters can be tuned by steering experts. However, using only subjective assessment is time-consuming, costly and non-repetitive. Since there is no direct method to tune the steering feel objectively, a driver model is required to find a mathematical justification in the mechanical interaction between driver and vehicle during steering. A 3-dimensional multibody arm model is constructed to investigate the influence of driving posture on the nonlinear steering response. It was found that the torque acting in the shoulder joint is higher than in the elbow. The relation between joint torque and joint angles is linear in the shoulder, whereas nonlinearities were found in the elbow joint. Nevertheless, a change of driving posture (i.e. a change of haptic interface) leads to a different steering response. Findings from the driver model were validated by two steering experiments. Muscle contraction was measured in order to analyse the forces acting on the joints. This study shows promise to lead to a different approach for tuning steering parameters. Further investigation and detailed experiments are required to convert this driver model into a method to tune steering feel objectively.

Extensive advancements in the field of mobility and safety in transportation in last few decades have acted as a catalyst for the expansion of automobile industry. The research for the evolution of new technologies, vehicle safety regulations and specifications and the market requirements has given this progress a greater push. The passenger cars these days are equipped with ample collection of active safety systems such as Anti-lock Braking Systems (ABS), Electronic Stability Program (ESP), Active Front Steering (AFS) and many more. The amalgamation of these active safety systems in the vehicle chassis allows the vehicle to accomplish the desired stability. Both academia and industry understood the requirement of more research in the field of vehicle industry. This leads to the comprehensive enhancement in the field of research and development with respect to integrated chassis control (ICC). The new research delivered some remarkable advantages to this industry, such as cost reduction of the complete system, multi-objective performance growth, hardware entanglement, fault-tolerant capability and system plausibility. Despite the huge diversity in research of integrated chassis, there are certain adaptive design methods which can be used to enhance the ICC performance. This dissertation intends to provide a methodology to fully exploit the nonlinear dynamics of the vehicle concerning the forces in between tire and road surface and to discuss the integration of AFS and ESP. It establishes a novel adaptive control approach in order to attain asymptotic tracking for switched affine vehicle models with parameter uncertainties and disturbances acting on the model. This, therefore, will help to attain the desired performance. Further, comparisonwith strategies that merely exploits the linear region of the vehicle dynamics are discussed, and performance improvements of the proposed methodology are assessed. Finally, analytically the endurance of the prospective control strategy is proven and simulations are conducted to validate the theoretical analysis.

The world is becoming increasingly congested due to a continuous growth of world population and overall wealth. Current road capacity limitations will lead to a significant decrease of traffic flow accompanied by a serious increase in global fuel consumption and air pollution. Ever-expanding road infrastructure is not expected to be a sustainable and long-term solution, thus other solutions are sought. Amongst these solutions is the concept letting AVs drive closer together, cooperatively. A flock of vehicles that coordinates collective movement while maintaining short inter-vehicle distances is defined as platooning. Evidently, in order to achieve safe platooning, a thorough understanding of both longitudinal and lateral behavior of the platoon system and its dynamics is required. The longitudinal aspect of platooning concerned with the design of spacing policies (i.e., distance keeping) has been broadly researched in the past decades. As a consequence, numerous valid applications exist. Whereas for the lateral aspect, the subject has not been researched as extensive. Hence a considerable amount of knowledge on this side is still needed to meet strict conditions and requirements for platooning applications. One of the major bottlenecks obstructing robust lateral platoon control is the ability to assure the lateral string stability for the complete platoon. String stability implies that errors propagating in an upstream direction of inter-connected vehicles forming the vehicular platoon, do not amplify. Specifically, lateral string stability implies that initially bounded lateral errors will remain bounded between ever pair of consecutive vehicles along the string of vehicles. Propagating errors are therefore attenuated during, e.g. the execution of a lane change. Henceforth, when Lateral String Stability for a platoon can be guaranteed, so is the reassurance that a platoon can safely perform certain manoeuvres such as a collective lane change. This thesis endeavours to develop a string stable, lateral controller for a homogeneous platoon of vehicles using a Model Predictive Control (MPC) approach. In the process two different control strategies tightly linked with the information flow topology, being centralized and distributed, are designed and compared in terms of reference tracking performance, noise- and disturbance rejection and practical implementation. Lastly, the developed controllers are simulated and validated using Siemens' Simcenter Prescan software, after which the results are thoroughly discussed. Results have indicated that for the application discussed in this work, the centralized controller outperformed its competitor in the field of tracking performance and noise rejection, but not by a great margin. Furthermore, the novel developed definition of Practical Lateral String Stability (PLSS) guarantees stability for a platoon of n=5 vehicles while using both controllers. To this end, the distributed controller is seen as worthy competitor and more workable solution due to the centralized controller's issue of practical implementation. As part of anticipated future work, we plan testing the proposed approach with field experiments to validate the proposed method in real life.

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.

This study proposes an LCA system that provides haptic guidance during lane changes. This system is fully integrated with LKA functionality to provide continuous lateral support during highway driving. Two different system configurations of this LCA are investigated. One is a generalized LCA that provides lane change reference trajectories based on a fixed lane change duration value of 4 seconds. The other is an adaptive LCA that provides personalized lane change reference trajectories through trial-by-trial adaptation to lane change duration of previously driven lane changes. The effects of these systems with respect to mental workload, lateral control performance and user acceptance are investigated. This is observed in an experiment with three different driving sessions for each participant. A manual driving session, a driving session in which the generalized LCA is active and a driving session in which the adaptive LCA is active. The experiments are conducted on a 6 DoF motion-based simulator with 34 participants, driving in a three-lane highway simulation environment with a scripted traffic scenario. To measure mental workload, an auditory cognitive secondary N-back task is introduced. The results show that the introduction of a generalized LCA or adaptive LCA does not have significant influence on mental workload compared to the manual driving session. When the adaptive LCA is introduced, lateral control performance is enhanced compared to the generalized LCA and manual driving. Additionally, user acceptance expressed as subjective usefulness is increased by introducing the adaptive LCA compared to the generalized LCA. Furthermore, inter-driver variability of the lateral control performance during lane changes is reduced by the proposed trial-by-trial adaptive LCA system compared to the generalized LCA system and manual driving.

Steering feel is an important variable that contributes to drivers’ assessment of safety and comfort. As such, creating acceptable steering feel is one of the goals of automotive manufacturers. Prediction of subjective steering feel based on objective measures, for example, driver torque characteristics, can benefit the automotive industry by decreasing time and cost of the vehicle design process. This thesis project examined the effect of steering column friction, a sole objective parameter, on subjective steering feel and on the drivers’ performance. A driving simulator study was conducted with 17 non-professional drivers, who drove and assessed 11 dimensions of steering feel for five steering configurations. The experiment was carried out in a fixed base driving simulator. The steering configurations differed only in the level of column friction: 0 Nm, 0.15 Nm, 0.27 Nm, 0.39 Nm, 0.50 Nm. The driving task was divided into two sections: Drive on a highway without prescribed manoeuvres to evaluate the steering feel and lane following to collect objective measurements. Data collected during the experiment were tested with Friedman’s ANOVA. The results showed that the subjective assessments of the group were influenced by the level of friction. A more pronounced effect occurred between configurations with the lowest (0 Nm, 0.15 Nm) and the highest (0.50 Nm, 0.39 Nm) steering friction. Statistically significant differences were primarily found for the dimensions steering effort, steering effort in the neutral area, centering, and reluctant feel. Drivers assessed reluctant feel as increasing and returnability as decreasing with steering friction. In term’s of drivers’ performance, a higher workload was observed with an increase in steering friction, as measured by steering reversal rate. The same effect is present in another measure of workload, standard deviation of steering angle (STS; corrective movements become larger with friction). Lane keeping performance was not significantly affected by column friction in the present study. In summary, the results of the present study indicate that column friction is an important factor influencing a driver’s subjective steering feel, especially for configurations with at least 0.27 Nm. The findings suggest that friction can be tuned according to the driver’s preferences without incurring a decrease in performance. The potential effect of steering friction on drivers’ safety needs further study.

Running-specific prostheses enable amputee athletes to perform at the highest level. However, current prosthesis design still impairs sprinting performance in multiple ways. An example is the constant mechanical stiffness of the prosthetic devices. The dynamic behaviour of the blade is determined by this property, but it is static and cannot change according to the gait phase-specific sprinting dynamics. During acceleration a different stiffness might be required as opposed to steady state running. Therefore, it is hypothesised that amputee athletes can improve their performance with prostheses that have a gait phase-specific stiffness. In order to investigate the effect of stiffness on amputee sprinting performance a novel and unprecedented modelling approached is used. Modelling is economical and straightforward in comparison to other research methods such as laboratory experiments. In this thesis, an extension of the established Spring-Loaded Inverted Pendulum model is proposed that qualitatively describes amputee sprinting motion. The Actuated Spring-Loaded Inverted Pendulum (ASLIP) is capable of predicting stable forwardly integrated sprinting motion with the inclusion of the start and acceleration phase. Optimisation of the model predicts that phase-specific spring stiffness leads to a significant time reduction on the 100m sprint for given physiological parameters. In general it can be said that a stiffer spring results in better performance. More specifically, the model benefits from a stiff spring during acceleration and a more compliant one in steady state. Although it has its limitations, the ASLIP model additionally provides a valuable insight into the mechanics of amputee sprinting. For example, it seems that optimal phase-specific stiffness is strongly dependent on biomechanical parameters such as touchdown angle, force angle and CoM velocity. Future work in this direction can provide a better understanding of the underlying mechanisms that determine amputee sprinting performance. The outcomes of this study suggest that amputee sprinters might be able to achieve a reduction in finishing time with prosthetic devices that have a phase-specific stiffness. The modelling approach used in this thesis is promising and lends itself well to investigate this opportunity in more detail.

The modern era is witnessing a crucial revolution regarding the driving concept, towards a more assisted and automated way of driving. Despite the advantages such conception have been extensively predicted and supported in literature, a direct transition from manual to fully automated operation is at the current moment not feasible, both for technological and ethical restraints, related to lack of driver’s acceptance and trust. Within this frame of reasoning the Advanced Driver-Assistance Systems (ADAS) provide a useful intermediate step on the way towards full vehicle autonomy. Nonetheless, as a bridge in between the old and the new driving era, such systems shall optimize the driving safety and performance and at the same time convey a pleasant driving experience. In this framework the current research proposes to investigate the application of a lane tracing ADAS from a different point of view, shifting the focus from the pure vehicle performance assessment to the evaluation and optimization of the driver-system interaction in the steering task. Considering the state of the art, the research proposes as a resolutive approach the design of a comprehensive experimental methodology, by means of which extensively investigate the supported on-centre steering phenomenon from the haptic point of view and propose a meaningful list of KPIs for the assistance optimization. In this sense, the diversification of the steering feel for the same vehicle dynamic event, in terms of swaying path, reveals crucial: therefore, a modular approach for the definition of the steering strategies is proposed, based on human resource-optimization approach prescribed by the reaching theory of kinaesthetic control. As a testing platform the fixed base driving simulator of the European Toyota facility is chosen, allowing to perform driving experiments with high-end vehicle and steering model. The latter for the research purpose has been integrated with a seamless steering command, validated with proving ground data, to promote the realism of assistance and interaction. In this regard, the choice of an evaluation team composed only of expert drivers aims at fostering the evaluation precision and consistency at all the times, performed by means of a tailored steering feel questionnaire. As main outcome of the experimental activity, the collection of subjective ratings and objective steering characteristics is exploited to identify statistical correlation models, representatives of Driver’s appreciation with respect to steering objective characteristics. Among these the most relevant ones shall be selected as KPIs. The designed methodology is hence validated by means of a Pilot Study, through which the main method flaws, related to dataset dimension and lack of correlation validation, have been highlighted and solved by means of a revised method, and a newly applied driver’s impedance identification scheme. These last two details in the context of a Final Experiment eventually allowed the delineation of a concise list of KPIs, providing design guidelines both in control- and interaction-oriented way. In the acknowledgement of the achieved results and the overall potential of the designed approach, several scenarios for future improvement and application are delineated, in view of an always better rendered driver assistance.

2getthere specialises in autonomous people transport through their GRT vehicle, used for transporting people at the airport from the parking to the terminal to ensure the GRT can operate comfortably and safely in a mixed traffic environment. The vehicle needs to plan a smooth and collision-free path. An essential aspect of a safe path is to predict the motion of the surrounding vehicles of the GRT to guarantee a smooth adaption to the constantly changing traffic scenario. The smooth adaption to the changing traffic scenario is essential for the GRT. The GRT cannot brake with the same magnitude as surrounding traffic due to standing people inside the vehicle. The importance of the smooth adaption to the traffic scenario resulted in the following problem statement; is it possible to create a motion prediction model for the surrounding vehicles of the ego vehicle to allow for pro-active velocity planning of the ego vehicle itself. From the literature, motion prediction models can be divided into physics-based motion models, manoeuvre-based motion models, and interaction-aware motion models. Each level has a certain amount of building blocks, with each level being an extra addition of situation awareness to the previous level. An interaction-aware motion model is the most advanced prediction model taking the infrastructure and interaction between the vehicles in the traffic scene into account. Therefore this principle of motion prediction is chosen for the motion prediction of the surrounding vehicles of the GRT. First, all possible state and route information of the surrounding vehicles are gathered. Secondly, a tree is created based on the vehicles' routes in the traffic situation, with the unique combination of manoeuvres known as a single branch of the scenario tree. Hereafter, the possible conflict areas between the individual manoeuvres are determined based on the desired trajectories for a specific route. The first vehicle passing the conflict area will influence the second vehicle's trajectory for passing the same conflict area, which will influence the third vehicle's trajectory and so on. Each vehicle can avoid a collision by passing in front or behind the previous vehicle(s) at the conflict area. Evaluating all passing possibilities will result in the velocity trajectory with the lowest cost, constrained by comfort. The sum of all costs for each branch of manoeuvres is used as a metric to determine the possible velocity profiles for each vehicle in the current traffic situation, resulting in a better understanding of the traffic scenario and ensuring a comfortable adaption to a changing traffic scenario. This thesis will evaluate several methods found in the literature to built a motion prediction model suitable for the application at 2getthere. The chosen motion model will be evaluated by two scenarios in a T-junction intersection.

The highly competitive nature of Formula one makes every team eager to find areas of improvement on their cars. In the case of Scuderia Toro Rosso, it was found that there is a gap to improve performance while driving on kerb stones. Analysis of competitors showed that they can take wider driving lines with more speed. In order to improve this performance, the need for a high fidelity dynamic car model arose. This model can be used to understand the car dynamics, which can be used to choose a better suspension setup. The baseline model used for this study was developed in MSC.Adams. After a discussion with field experts, two areas of model improvement were identified. In the baseline model, the tire is modeled using a MF-Tire model, where the vertical dynamics was modeled as a single spring and damper. In order to simulate the tire dynamics on kerb stones, a more advanced model is necessary. For this research, FTire was chosen as the best option. The other area of improvement is modeling the compliance of the suspension system. In the baseline model, all suspension members are modeled as rigid links, without any compliance. To model this compliance, an ’equivalent stiffness’ model was proposed, where all stiffnesses are combined into flexible joints. To use the FTire model, a set of parameters needs to be found for the Formula one tires. To find these parameters, measurements were done to capture the dynamics of the tire. A method is developed to filter out any rig disturbance, and make the data useful for parameter estimation. The estimation was done using Matlab, with MSC.Adams and FTire in-the-loop, and made use of a Genetic Algorithm (GA). Comparing the simulated behavior with the measurement showed good correlation for various conditions. Since every part in the car contains some compliance, modeling and measuring every single part would be an extensive task. In order to reduce the complexity, all compliances are combined into a simplified model. In this simplified model, all compliances are modeled in the joints. A Pattern-Search algorithm in Matlab is used in combination with MSC.Adams to find the joint stiffnesses in such way the model matches measurements of the overall car compliance. To measure the overall improvement, both models are combined into a full car model. The baseline and proposed model are validated against measured track data. From this compare, it can be concluded that the proposed model shows a significant better correlation on kerb simulations. This is mainly due to the fact that the baseline tire model is not capable of simulating the harsh road input. The proposed model provides a more powerful tool for engineers to better understand the car dynamics, and to find the best suspension setup for kerb riding.

To promote automation in vehicles, autonomous driving should feel comfortable. To achieve low discomfort, a comfort oriented nonlinear model predictive controller is created. We know humans are sensitive for discomfort in certain frequencies in acceleration. By penalizing the frequencies for discomfort a higher comfort performance can be achieved. Two band pass filters are created to penalise the frequencies. A band pass filter (0.03-0.2 Hz) for the frequency of motion sickness and a band pass filter (1-2 Hz) for general discomfort. Due to the MPC framework the filters can be implemented on the predicted accelerations. The filtered accelerations are penalised within the MPC. The MPC is made for path following control. To test the MPC a reference generator is built. The reference generator creates reference signals about the path ahead for the controller. To test the performance of the filters, tests are done with different controllers. In the different controllers the filters are penalised individually and together and compared against other controllers. The controllers are tested on multiple scenarios (e.g. double lane change and a sinusoidal trajectory). On the scenarios multiple disturbances are tested (e.g. wind disturbance and sensor noise). We conclude that the MPC design that relies on the motion sickness filter has a significant decrease of motion sickness in scenarios where a lot of motion sickness is present, with improvements up to 30.7% compared to the basic controller. The MPC design that relies on the general discomfort filter helps bring the general discomfort down. On the double lane change maneuver with a changing velocity the general discomfort filter has improvements up to 10.7% compared to the basic controller.

The inertial parameters of a vehicle, which include the mass, centre of gravity position and the moments of inertia, influences the dynamics of the vehicle. Currently, the modelling of the vehicle is done by assuming fixed, conservative, values for the inertial parameters. Knowing the exact values may increase the performance, safety and comfort of the vehicle. A literature review has been conducted, where different methods for online inertial parameter estimation have been graded based on the amount of parameters it is able to estimate, the sensors used and the accuracy of the methods. Rozyn's method seems best for online inertial parameter estimation. Rozyn proposes a method which can estimate the inertial parameters from vertical acceleration data using a state variable method, modal analysis and a simple vehicle model. Rozyn's method can be summarised in four steps: •Extract the free decay response from acceleration data. •Construct the state transition matrix. •Construct the system characteristic matrix. •Estimate the inertial parameters using the constructed characteristic matrix and simplified vehicle model. The main shortcoming of Rozyn's method is the road profile which is used for the simulation, which is described in the ISO 8608 norm. The ISO 8608 description is a stationary Gaussian process. This means that the road profile random variables are normally distributed. Furthermore, the properties of the road profile (mean and variance) does not change over time. In practice however, road profiles never follow a stationary Gaussian process, but are much more random, with more variance between different sections. Another, more realistic, road profile description is proposed by Bogsjö where the road profile follows a non-stationary Laplace distribution. Another shortcoming of Rozyn's paper is that it only shows results for only one condition. For the simulation, the vehicle is driving 100 km/h and a measurement period of 1,000 seconds is used. Unknown is the influence of the velocity of the vehicle on the results. It is to be expected that the accuracy decreases for shorter measurement periods, but by how much is also unknown. In this thesis, Rozyn's algorithm is explained and implemented using a half car vehicle model. Rozyn's algorithm is validated using the ISO 8608 road profile description on similar conditions. The algorithm is then tested using the ISO 8608 road profile description where the velocity of the vehicle is varied between 30 and 100 km/h and the measurement periods between 30 and 120 seconds. This is done 100 times for each condition. This results in 100 estimates of the inertial parameters of each condition. From these results, the average and standard deviation between the estimates can be calculated. This is also done for the alternative Laplace road description. The resulting standard deviations are plotted in surface plots, as function of the varying velocity and measurement period. The results show that the standard deviation between the different estimated parameters when using the Laplace description for the road profile are up to 5 times higher compared to the ISO 8608 road profile description. The results also show that the performance of the algorithm is heavily dependent on the measurement time. A measurement time of at less than 60 seconds is not recommended, due to the large deviation in the estimated parameters. For the mass and centre of gravity position, the performance is independent of the velocity of the vehicle. However, the pitch moment of inertia shows a slight dependency on the velocity, with lower deviations between the different estimates for higher velocities. The algorithm can still be used on non-stationary road profiles. However, more and longer measurements are needed for the algorithm to return with an accurate estimation of the inertial parameters. Even then, some errors in the estimated parameters in the order of 10% are present.

An issue in driving simulation is that behaviour displayed in simulation does not exactly replicate behaviour in real-life. For example, roadside vegetation density impacts a driver’s speed and lateral position in on-road studies, but not in driving simulator studies. In this study it is investigated if the increase in fidelity and presence that head mounted display based virtual reality bring forth, yields behavioural adaptation as shown in real-life by evaluating the effect of three different roadside vegetation density conditions in a novel head mounted display based virtual reality driving simulator. Twenty-nine participants drove a 2m wide car over a 11km long 3.5m wide winding road. Participants completed three trials driving through three different roadside vegetation density conditions per trial, the density conditions being light (one tree per 40m), medium (one tree per 20m) and dense (one tree per 5m). The effect of the density conditions was evaluated with respect to speed, lateral position, steering reversals, subjective workload and self-reported risk. Increased vegetation density increased self-reported risk, but did not affect speed or lateral positioning. This finding is congruent with findings in con- ventional simulator studies, which could indicate that despite the advantages of head mounted display based virtual reality regarding fidelity and presence the resulting driving behaviour still has a closer connection to conventional simulated driving than real-life driving. In future work the built and implemented simulator can be improved and optimized, furthermore the relative validity of the simulator should be investigated.

The automotive sector has seen a rapid transition towards autonomous driving with an aim to achieve SAE Level 5 vehicle. The incessant drive for innovation has resulted in modern passenger cars equipped with plethora of control technologies such as ABS, VSC, AFS via EPS assist etc. all working respectively in various critical and non-critical scenarios to ensure safe and smooth driving at all times. These efforts have reduced the number of road accidents significantly over the past years, yet it was observed that the number of rear-end crashes have not decreased but has rather maintained its proportion which is about 1/3rd of all road accidents. An evasive maneuver, a limit-handling situation, involves quick steering or braking action to avoid colliding with the vehicle in-front (thus avoiding a rear-end crash). A normal human driver is not trained to drive and control car in such demanding and high stress inducing task. This warrants the needs of an autonomous controller design that can itself take over the control of car and perform the maneuver successfully. The aim of this research was to verify this statement by designing a novel control scheme that can steer and brake simultaneously ensuring collision avoidance and passenger safety at all times. The controller designed uses the state-of-the-art Model Predictive Control (MPC) scheme such that best possible performance can be extracted even in critical driving scenarios. Variety of simulations were performed to successfully conclude that MPC worked well and is robust in design as well.