Autonomy in traffic (e.g., autonomous vehicles) could potentially benefit mobility, safety, accessibility and sustainability. However, the realisation of these advancements is highly dependent on how effective these autonomous vehicles interact with vulnerable road users such as pedestrians. Before we can understand how pedestrians will interact with autonomous vehicles, it is essential to understand how pedestrians interact among themselves in interactive traffic scenarios. Previous studies have focused on describing these scenarios with probabilistic trajectory prediction methods such as TrajFlow. However, these approaches often fall short in capturing the nuances of mutual interactions. Simple interaction models have been proposed that can describe these interactions, but neglect the influence of another person's intentions. To address this issue, in existing work the Communication-Enabled-Interaction (CEI) framework was proposed that describes interactions by modelling communication and a belief of another person's intentions. The idea of using beliefs in interaction modelling is based on the concept that people have a general but uncertain idea about the plans of other people. These beliefs are one of the fundamental aspects of the CEI framework and must therefore contain valuable information about possible decisions. That is why this study investigates the use of the probabilistic trajectory prediction method TrajFlow for the belief construction of the CEI framework. TrajFlow is trained on the belief-based Forking Paths dataset, integrated into the CEI framework, and tested in four simulated pedestrian interaction scenarios. The analysis shows that the framework is able to simulate plausible interaction behaviour, dealing with conflicting goals and trajectories in multiple simulations. By doing so, this study takes a positive step towards modelling pedestrian interactions and contributes to the broader goal of realising the benefits linked to autonomy in traffic.

As autonomous vehicle (AV) technology progresses, the necessity for a comprehensive understanding of interactions between AVs and human-driven vehicles (HVs) becomes paramount, particularly in critical manoeuvres such as merging. Mastering merging interactions is essential for enhancing road safety. Existing research in this field focuses on how the AV performs the merging manoeuvre but often fails to assess how they influence these interactions. By drawing inspiration from Human-Robot Interaction and Human Aware Navigation, this study aims to bridge this gap by examining how these interactions influence driver workload, measured through fixations duration, perceived safety and drivers’ subjective perception during merging scenarios. We employed a Virtual Reality environment to simulate realistic driving conditions and measure driver responses. We conducted an experiment where participants engaged in merging manoeuvres with each other and, subsequently and without being informed, with the AV described in “Planning for cars that coordinate with people” [1]. This approach allowed for an unbiased assessment of natural driver reactions to AV behaviours. Our findings reveal significant increases in driver workload and decreases in perceived safety during HV-AV interactions, compared to HV-HV interactions. These results suggest that current AV algorithms may not fully account for the complexity of human-AV interactions, highlighting a need for interaction evaluation in the AV development. Participants’ subjective feedback indicates a recognition of and negative reaction to AV driving behaviours, emphasizing the importance of designing AVs that are both efficient and intuitive for human drivers. The study’s implications suggest improving AV controllers’ evaluations by including their interactions with human drivers. By integrating interaction evaluation, AV technologies can achieve smoother and more successful integration into existing road systems, enhancing predictability and driver acceptance. This study marks a step towards understanding the interactions between AVs and HVs, offering insights that could steer future research and development in autonomous driving technologies.

In order to design safe and effective interactions between autonomous vehicles (AVs) and human road users, it is essential to understand the mechanisms underlying human-human merging behavior. Driving simulator experiments can be used to study these mechanisms, but previous research has primarily focused on the behavior of individual drivers rather than the dynamics of interactions. In addition, current experimental scenarios and data analysis tools do not adequately capture interactive humanhuman merging behavior. To address these issues, I propose an experimental framework featuring a simplified highway-merging scenario that can facilitate human factors research on merging interactions. In a case study with fourteen participants, I used the framework in a coupled virtual reality driving simulator to show a relation between participants’ interactive behavior and fixation behavior. This work shows how to better understand human-human merging interactions, which is essential for developing AVs that can safely and successfully interact with other road users.

Fleet Cleaner B.V. is a Dutch company which deploys a wheeled hull-fixed Remotely Operated Vehicle (ROV) to clean biofouling off the Vertical Sides (VSs) and the Flat Bottom (FB) of the hull of seagoing cargo vessels, while berthed at the harbour. To ensure maximal cleaning coverage, accurate localization to keep track of the robot trajectory on the ship hull is essential, but also challenging. Contrary to the VS case, drift-free localization from motion sensors is not applicable on the FB. Instead, the current system relies heavily on operator input, who in turn relies on features (such as the quay wall, a line feature) appearing on imagery from a Forward Looking Sonar (FLS) to determine the robot pose. Human inattentiveness caused by the high workload of cleaning operations makes this method error-prone and sensitive to drift. In the future, Fleet Cleaner aims to develop an autonomous robot to relieve the operators from the task of localization and improve cleaning performance. As no previous work was performed on this matter, this thesis is a contribution to this vision, by developing a sonar-based framework enabling drift-free localization of a ship hull cleaning robot on FBs. Before the conceptual phase, two objectives are formulated to achieve the thesis goal. The first is to provide a sonar-based algorithm measuring the line parameters of the quay wall, as lateral and heading reference. The second objective seeks to fuse the FLS-based measurements with motion sensors to achieve drift-free localization. From there, the operating conditions relevant to the systems are defined, and both functional and performance requirements are formulated, where the latter define metrics that measure the accuracy of the systems w.r.t. manually defined ground truths.
For the first objective, three candidate solutions for tracking the quay wall line parameters are selected. These are first implemented with recorded data in an iterative tuning procedure, then validated where the Total Least-Squares (TLS)-based line tracker with Gaussian filtering arises as a proof of concept. Although the algorithm is fast enough to function for each incoming sonar image, it does not fulfill the requirements for accuracy and robustness, a problem solvable by tuning with better data and the use of filters. For the second objective, also three sensor fusion algorithms are selected. Analysis of their properties with regards to performance revealed the Extended Kalman Filter (EKF) as the most suitable solution, where tuning was performed heuristically. Validation tests again showed partial success, with fast computation times and greatly improved but still insufficient accuracy of the FLS measurements, although localization drift was reduced within the required limits. Mainly, the poor predictions from auxiliary sensors degrade performance, but also the imperfect nature of manually defined ground truths exaggerate the errors. To improve and complete the localization system, it is recommended to implement trajectory optimization for an improved fit of estimated poses with sensory data, and Simultaneous Localization And Mapping (SLAM) based on weld line detection to reduce longitudinal drift.