The European Commission has mandated the requirement of advanced safety features in all vehicles sold in the EU from 2022 onwards. This will help drivers get gradually used to ADS, eventually transitioning to fully autonomous driving in the distant future. There are more vehicles on road now equipped with lane-keeping systems and other driver assistance systems due to the added benefit of safety. One such Advanced Driver Assistance System (ADAS) feature is the lane-keeping system which has plenty of performance evaluation research being done on it but lacks research from an Operational Design Domain (ODD) perspective in simulation. ODD is the set of conditions for which the vehicle is designed to function. There are many ODD taxonomies present in academic literature for the classification of ODD attributes in the ODD definition. These are recommended to be used by Original Equipment Manufacturers (OEM) to classify their ODD definition of the system for effective comparison between the same ADAS feature offered by two different OEMs. However, this is not seen to be done by the OEMs and causes a disparity between the ODD definitions, leading to misunderstanding or misinterpretation of the defined ODD. The OEMs do not exactly specify the ODD boundaries in publicly available instruction manuals of the vehicles. The conditions where the system cannot function properly are very vaguely mentioned. Furthermore, the different OEMs provides the same ADAS feature with different names with differently defined ODD boundaries. Bringing these ODD definitions of all these OEMs under one umbrella is currently being attempted by policymakers and regulatory organisations. A standard way of testing the impact of the different ODD attributes on the performance of a lane-keeping system is still absent. Therefore, this research aims to provide a method to assess the ODD boundaries of a lane-keeping system using the simulation software Prescan by Siemens and is intended to be used by researchers, policymakers and OEMs to refine the ODD and to test the effect of the ODD attributes on the performance of the system. It was found in this research that there is a strong interdependency between the tested ODD attributes. This can lead to a possibility that the ODD definition may not exactly be a straightforward set of specific values in the form of a table, but instead can be dynamic considering this interdependency. It was also observed that some test cases classified inside ODD from the initial ODD assessment had poor lane-keeping performance and some test cases classified outside ODD had good lane-keeping performance. Another observation from the test cases was the ability of the test vehicle to move at higher speeds in narrow lane widths with good lane-keeping performance compared to wider lane widths. This falls in line with findings from previous literature and will require validation. The research approach of this study is used to develop a conceptual framework to assess the ODD boundaries of an ADAS feature in a simulation environment.

As the demand for transport of goods and passengers increases, the capacity of railway networks is becoming more and more saturated. Railway infrastructure managers aim to increase the rail network capacity with the minimum possible investments in infrastructure. Next generation signalling concepts such as Moving Block (MB) and Virtual Coupling (VC) are expected to bring significant capacity benefits to railway corridors. The aim of this thesis is to identify capacity benefits of Virtual coupling over ETCS L3 MB considering a realistic operational setup, where different rolling stock characteristics, driving behavior and traffic information updates exist. Inspired by safety distance car-following models, the multi-state train-following model developed by Quaglietta et al. (2020) is enhanced with a dynamic term, called Dynamic Safety Margin (DSM). The DSM ensures that the following train can come to a standstill at any time applying service braking, even in the worst-case scenario when the leading train applies emergency braking. The DSM accounts for positioning errors, the Vehicle-to-Vehicle (V2V) communication update delay, different train control delay times, and braking capabilities of trains running as a convoy. An One-at-a-time (OAT) sensitivity analysis of the proposed model is conducted to evaluate the importance of selected model parameters in railway capacity. The model parameters under investigation concern the V2V update delay, the train control delay time, and the braking rate of the following train. The braking rate of the following train is proved to have a significant impact on railway capacity, implying that trains with better braking performance imply shorter time headways. To identify potential capacity benefits of the proposed model over ETCS L3 MB, operational scenarios are executed through microscopic simulation in the EGTRAIN tool. The scenarios consider three different rolling stock types, being suburban, regional, and intercity trains. For trains running on open track without stops, the proposed model promises the highest capacity benefits over ETCS L3 MB, for trains with improved braking and acceleration characteristics. For train services with stops, all the train convoy combinations show promising capacity benefits, which are higher for heterogeneous train convoys. Moreover, headways can be reduced to the minimum possible when faster trains follow slower trains. Finally, this model enables multiple train convoy formation based on a predecessor-follower communication topology, where a safety distance is always ensured between trains.

Driving simulators are widely used for understanding Human-Machine Interaction, driver behavior and driver training. The effectiveness of such simulators in this process depends largely on their ability to generate realistic motion cues. Though the conventional filter-based motion cueing strategies have provided reasonable results, these methods result in poor workspace management. To address this issue, linear MPC-based strategies have been applied in the past. However, since the kinematics of the motion platform itself is non-linear and the required motion varies with the driving conditions, this approach tends to produce sub-optimal results. In this thesis, a nonlinear MPC-based algorithm is presented which incorporates the non-linear kinematics of the Stewart platform within the MPC algorithm to increase the effectiveness and utilize maximum workspace. Further, adaptive weights-based tuning is used to smoothen the movement of the platform near its physical limits. Full-track simulations were carried out and performance indicators were defined to objectively compare the response of the proposed algorithm with classical washout filter and linear MPC-based algorithms. The results indicate a better reference tracking with lower root mean square error and higher shape correlation for the proposed algorithm. Lastly, the effect of the adaptive weights based tuning was also observed in the form of smoother actuator movements and better workspace utilization.

Significant developments in the field of Advanced Driver Assistance Systems (A prove traffic management and prediction, driver comfort, and vehicle safety. However, a detailed knowledge of the way vehicles move is crucial for these applications. The mobility behavior has also changed over the last decades significantly. Both individual mobility and the mobility of goods is nowadays an essential part of our economy. The latest trends in vehicle automation are the predicted first signs of a partially/autonomous future of mobility. Whether this
future will be with or without individual vehicle ownership will become apparent in the coming years. For the moment, all researchers can do is to work on new topics tomake the future of mobility faster, more efficient, quieter, and safer. Detailed knowledge of state-of-the-art automation systems is essential in order to gain further insights into their effects, e.g., on traffic, the travel behavior of people, the security gaps caused by these systems, and passenger comfort.

The bottleneck of the maximum road volume in urban areas is the maximum capacity of the traffic flow on the intersection, which is coordinated with Traffic Light Controllers (TLCs). A promising method to decrease the number of stops are Green Light Optimal Speed Advice (GLOSA) systems. These systems will give a speed advice to arriving vehicles based on the schedule of TLCs, which needs to be known and fixed. However, most on-street controllers change their schedule until the last moment to maximize the performance. In this thesis a predictive controller is developed that is suitable for real-world application based on DIRECTOR; a state-of-the-art predictive controller. A prediction model is used to predict future arrivals based on available measurements to optimize and fix the schedule in advance. The proposed controller can enable GLOSA systems to improve performance. Appropriate pre-processing steps are implemented and the optimal input features are selected to improve the performance of a Long Short-Term Memory (LSTM) network to predict future arrivals. All detection data is stationary over time by using the differenced series. The time data is divided into workdays and weekend days to create a binary input and undesirable jumps during midnight are removed. The combination of stop line detectors, queue detectors, arrival detectors and signal states of the controlled and preceding intersections as input maximized the performance. The prediction horizon of the proposed prediction model could be extended. The Normalized Root Mean Square Error (NRMSE) decreased with 17% compared to DIRECTOR. The proposed controller extends the control horizon and uses multiple prediction models to predict the arrivals for the entire control horizon. The proposed controller outperforms DIRECTOR with 14 - 38% reduction in terms of vehicle delay and 5 - 32% reduction in terms of numbers of stops based on the scheduling mode. The GLOSA system is an add-on of the controller and is able to operate without the GLOSA system. The control horizon of the proposed controller always has a fixed length which is needed to determine the time until green. The implemented GLOSA system will determine the optimal speed based on the time until green and the expected delay due to the surrounding vehicles. The proposed controller is a cloud controlled application. Therefore, it is possible to adjust the setup (i.e. scheduling modes) during the day. Enabling GLOSA all day except during rush hours will lead to 3 - 4% reduction in terms of vehicle delay and 29 - 32% reduction in terms of numbers of stops based on the scheduling mode. This setup of the proposed controller is also competitive with the hand-crafted non-predictive on-street controller. Compared with this controller, the proposed controller will reduce the number of stops with 26% at the cost of 16% increase in vehicle delay. The proposed controller is designed conform the safety standards used on-street. Permission is received from the provincial government to do on-street pilots with the proposed controller. These results could give new insights into the performance of the proposed controller.

Advanced Driving Assistance Systems (ADAS) technologies like Adaptive Cruise Control (ACC) are becoming the normality for many users, and many major car manufacturers are introducing SAE level 2 and 3 automation systems into the market. The main advantage of Automated Vehicles (AV) will be the significant decrease in road accidents and casualties. However, a significant shift from conventional to automated vehicles must occur before it can have a positive impact on society. If the behaviour of the vehicle is not perceived as natural, the user will most likely not activate the ADAS features again. During this study a naturalistic dataset is used to investigate the driver behaviour, in the hope of bringing the current ACC logic to a more human-like behaviour that will feel more natural to the driver. The research question summarizes the final objective of this study: How can Naturalistic Driving Study (NDS) datasets be used in target performance setting for ACC systems? This study will answer the research question by studying human behaviour in the scene of following an accelerating vehicle. The main body of this thesis is divided in three chapters, one for each step of the research. First the information about the used datasets are provided together with the methodologies used to extract the relevant time-series data. Secondly driver behaviour models are created in order to mathematically characterize human behaviour. The strength of the created models is their ability to represent the full range of driver behaviour in terms of driving style. The aggressiveness parameter of the model can be easily adjusted to represent different percentiles of driver behaviour. This allows for a quick and effective tuning process: by changing a single parameter the driving style of the model can be fully modified. Finally, the driver behaviour models are implemented into a simulation environment. The models are simulated against an existing ACC logic in order to assess the difference in behaviour. The comparison highlighted two conclusions: first, the ACC logic behaves in a very conservative way compared to driver behaviour, especially when starting from standstill. Secondly, the kept by the ACC logic was not consistent throughout the speed range. This variation of the logic's driving style could result even more bothersome to the customer than its general conservative behaviour. The string stability of the driver behaviour models was also assessed. Although the proposed logic proved more stable than the regular ACC logic, it still cannot reach full string stability.
Hopefully, with the method developed in this study, the process of getting accustomed to this new technology will become easier for the customer. Thanks to the driver behaviour models the motion of the vehicle can feel familiar and predictable, with the controller becoming part of the Human Machine Interface (HMI). As the customer gets more familiar with this technology his expectation will also increase and change, especially as the levels of automation start to increase. This will inevitably push automakers to continue to improve the technology to deliver increasingly advanced and safe vehicles.

Automated driving, one of the rapidly growing research topics in the field of smart traffic, has proved to be a recognized solution for potentially improving road throughput and reducing vehicles' energy consumption by grouping individual vehicles into platoons controlled by one leading vehicle.

The advances in distributed inter-vehicle communication networks have stimulated a fruitful line of research in Cooperative Adaptive Cruise Control (CACC). In CACC, individual vehicles, grouped into platoons, must automatically adjust their own speed using on-board sensors and communication with the preceding vehicle so as to maintain a safe inter-vehicle distance. The importance of CACC lies in the fact that it enables small inter-vehicle time gaps, which leads to a major reduction of the aerodynamical drag force applied on vehicles in such a driving pattern. Consequently, vehicle emissions, which play a main role in trucks and heavy automobiles, are expected to be highly reduced.

However, a crucial limitation of the state-of-the-art research in this control scheme is that the string stability of the platoon can be proven only when the vehicles in the platoon have identical driveline dynamics and perfect engine performance (homogeneous platoon), and possibly an ideal communication channel.

Thus, the objective of this MSc thesis is to address the problem of CACC for heterogeneous platoons under realistic inter-vehicle network conditions. In the first part, we propose a novel CACC strategy that overcomes the homogeneity assumption and that is able to adapt its action and achieve string stability for uncertain heterogeneous platoons under ideal inter-vehicle network conditions. In the second part, in order to handle the inevitable communication losses, we formulate an extended average dwell-time framework and design an adaptive switched control strategy which activates an augmented CACC or an augmented Adaptive Cruise Control strategy depending on communication reliability.

Stability of the proposed control strategies is proven analytically and simulations are conducted to validate the theoretical analysis.

In the last years, Floating Car Data (FCD) has risen as a technology that has the potential to replace legacy sensors, reduce associated installation and maintenance costs and offer new possibilities in the field of traffic management. FCD refers to the use of sources such as in-vehicle systems with network connectivity or road users’ mobile phones to collect relevant traffic information (e.g. vehicle speed or intensity). There is extensive literature on the feasibility of using FCD for traffic management purposes and on the added value of fusing legacy sensors data with FCD. These studies have been mostly experimental and a literature review shows that there is no academic work on how to deploy and integrate legacy sensors data and FCD in an actual live setting. This research aims to fill that gap by designing a flexible ICT Architecture that integrates FCD into Rijkswaterstaat’s Traffic Management and Information Systems.

Taking an architecture point of view to address this challenge is not only novel but also appropriate because it provides a comprehensive view of what changes need to take place at different levels of the organization (business, information systems and technology) for this to happen. This research follows a design science research approach, where four main phases can be distinguished: (1) Problem formulation, (2) Building, (3) Reflection and learning and (4) Formalization of learning. The Building phase is further subdivided into the specification of design principles and requirements and the development of the solution ICT Architecture.

The outcomes of this research are relevant not only to Rijkswaterstaat but also to other organizations looking to integrate FCD in their traffic systems, to design science researchers and to ICT architects.