As Advanced Driver Assistance Systems (ADAS) become integrated into vehicles, driver education is important to support the safe and effective use of these technologies. However, structured ADAS educational programs for drivers have not been extensively studied. Moreover, the perspective of driving instructors, key stakeholders in the training process, has been overlooked. To address this gap, this study explores the perspectives of professional driving instructors who have delivered structured ADAS driver training at driving academies across four European countries. Through semi-structured interviews with fourteen instructors, this study examines the impact of the training, training design, implementation challenges, demographic considerations, and institutional roles. Instructors reported that ADAS driver training enhances driver confidence and promotes the appropriate use of the system, particularly by reducing overreliance on automation. They also emphasised the importance of a phased training model, combining theoretical instruction, controlled on-track practice, and on-road driving. In addition, Instructors highlighted the need for tailored approaches for older drivers and for introducing ADAS training after novice drivers have acquired basic driving skills. This study suggests the need for standardised ADAS training and cross-sector collaboration among leasing companies, car dealerships, and regulatory bodies to ensure broad accessibility and effective learning. The findings contribute to developing scalable, inclusive, and safety-oriented frameworks for driver education in emerging vehicle technologies.

The introduction of automated driving systems (ADS) presents significant regulatory and operational challenges to ensure safe and responsible deployment in mixed traffic environments. Despite much academic work and efforts of practitioners, these challenges remain open, requiring a transdisciplinary integration of perspectives. This paper draws on insights from a recent transdisciplinary workshop, highlighting the key issues in ADS deployment, including misalignment between regulations and system capabilities, emerging accident types, and gaps in driver understanding and training. Current regulations struggle to keep pace with the advancing capabilities of ADS, resulting in unclear accountability frameworks and inadequate safety measures. The concept of meaningful human control was used as a basis to identify issues. Workshop participants agreed that meaningful human control has an essential role to play to address the identified issues by ensuring that humans can adequately interact with ADS and that ADS are designed in a manner that ensures safe and responsible deployment with clear fail-safes and redundancy mechanisms. The paper advocates for meaningful human control through continuous driver and vehicle assessment, dynamic safety certifications, and stronger communication between regulators and manufacturers to ensure safe and responsible design, regulation and deployment of automated vehicles. Implementing these actions will strengthen ADS regulation and help navigate the ethical and operational complexities of automated driving systems.

Partially automated driving systems are designed to perform specific driving tasks—such as steering, accelerating, and braking—while still requiring human drivers to monitor the environment and intervene when necessary. This shift of driving responsibilities from human drivers to automated systems raises concerns about accountability, particularly in scenarios involving unexpected events. To address these concerns, the concept of meaningful human control (MHC) has been proposed. MHC emphasises the importance of humans retaining oversight and responsibility for decisions made by automated systems. Despite extensive theoretical discussion of MHC in driving automation, there is limited empirical research on how real-world partially automated systems align with MHC principles. This study offers two main contributions: (1) an empirical evaluation of MHC in partially automated driving, based on 103 semi-structured interviews with users of Tesla's Autopilot and Full Self-Driving (FSD) Beta systems; and (2) a methodological framework for assessing MHC through qualitative interview data. We operationalise the previously proposed tracking and tracing conditions of MHC using a set of evaluation criteria to determine whether these systems support meaningful human control in practice. Our findings indicate that several factors influence the degree to which MHC is achieved. Failures in tracking—where drivers' expectations regarding system safety are not adequately met—arise from technological limitations, susceptibility to environmental conditions (e.g., adverse weather or inadequate infrastructure), and discrepancies between technical performance and user satisfaction. Tracing performance—the ability to clearly assign responsibility—is affected by inconsistent adherence to safety protocols, varying levels of driver confidence, and the specific driving mode in use (e.g., Autopilot versus FSD Beta). These findings contribute to ongoing efforts to design partially automated driving systems that more effectively support meaningful human control and promote more appropriate use of automation.

The demand for air transport has experienced rapid growth, raising significant environmental concerns. Previous studies on airport sustainability have mainly focussed on airside areas; while literature pertaining to landside transport sustainability and emissions reduction approaches is limited. This paper addresses this gap by presenting a comprehensive review and critical assessment of the existing studies aiming to inform policymakers and researchers. Based on a holistic approach and an interdisciplinary perspective, the reviewed literature is classified into four categories: travel behaviour, transport infrastructure, transport policy, and sustainability. Within these categories, key findings are identified, along with a concise overview of the strategies employed by large airports to achieve their net-zero targets. Main solutions include increasing the attractiveness of public transport, providing electric vehicle (EV) infrastructure, and promoting low/zero emission vehicles. Moreover, the exploration of innovative fuel technologies (such as hydrogen), fast-charging EVs, and autonomous shuttle buses also has immense potential.

Accurate and timely alerts for drivers or automated systems to unfolding collisions remains a challenge in road safety, particularly in highly interactive urban traffic. Existing approaches require labour-intensive annotation of sparse risk, struggle to consider varying contextual factors, or are useful only in the scenarios they are designed for. To address these limits, this study introduces the generalised surrogate safety measure (GSSM), a new approach that learns exclusively from naturalistic driving without crash or risk labels. GSSM captures the patterns of normal driving and estimates the extent to which a traffic interaction deviates from the norm towards unsafe extreme. Utilising neural networks, normal interactions are characterised by context-conditioned distributions of multi-directional spacing between road users. In the same interaction context, a spacing closer than normal entails higher risk of potential collision. Then a context-adaptive risk score and its associated probability can be calculated based on the theory of extreme values. Any measurable factors, such as motion kinematics, weather, lighting, can serve as part of the context, allowing for diverse coverage of safety-critical interactions. Multiple public driving datasets are used to train GSSMs, which are tested with 2,591 real-world crashes and near-crashes reconstructed from the SHRP2 NDS. A vanilla GSSM using only instantaneous states achieves AUPRC of 0.9 and secures a median time advance of 2.6 seconds to prevent potential collisions. Additional data and contextual factors provide further performance gains. Across various interaction types such as rear-end, merging, and crossing, the accuracy and timeliness of GSSM consistently outperforms existing baselines. GSSM therefore establishes a scalable, context-aware, and generalisable foundation to proactively quantify collision risk in traffic interactions.

Route guidance in traffic management aims to improve traffic network performance aligned with a system optimum. However, service providers commonly offer user optimal travel advice that can negatively impact centralized route guidance. This paper quantifies and demonstrates the impact of different policy strategies for a centralized route guidance systems where road authorities and service providers work together in a coordinated approach. Cooperation through an intermediary is considered with various policy strategies that consider different approaches and levels of cooperation between road authorities and service providers, which are evaluated using traffic modelling. A use case for the ring network of Milan shows that cooperation between the two parties has the potential to get the best out of the measure by utilizing a system optimum approach, while still allowing service providers to offer individual travel advice. The results of the modelled case study clearly show that the two approaches of far-reaching cooperation and increased compliance have a greater positive effect on traffic network performance in terms of reduced delays, reduced congestion and total time spent. In addition, the future presence of Connected Automated Vehicles (CAV) is also considered in which these vehicle demonstrate full compliance. This shows that with increasing percentage of CAVs that route guidance can have a substantial positive effect compared to low compliance or a smaller penetration rate of automated vehicles.

Conditionally automated driving requires drivers to resume vehicle control within constrained time budgets upon receiving takeover requests. Accurately predicting drivers’ takeover time (ToT) is essential for dynamically adjusting time budgets to individual needs across scenarios. This study addresses enduring challenges in reliability and interpretability of ToT prediction models by optimizing predictor selection. Using a driving simulator experiment, we examine the relationship between ToT, driver characteristics, and perceived Spare Capacity (pSC, a cognitive construct from Task-Capability Interface theory) using Category Boosting models. Results show that (i) incorporating 13 additional driver characteristics does not significantly improve prediction accuracy when pSC is already considered; and (ii) individual characteristics influence how drivers cognitively process takeover scenarios, and their predictive contribution likely overlaps with pSC. These findings suggest that monitoring cognitive states may be more effective for ToT prediction than extensive profiling of driver characteristics. This study provides a critical first step toward predictive frameworks for adaptive takeover strategies and offers guidance for designing personalized human–vehicle interactions.

BACKGROUND: Long-term noise annoyance can be expected to have worse outcomes than short-term annoyance. This study investigates noise annoyance over time, its association with personality traits and potential reciprocal effects between health outcomes and noise annoyance. METHODS: Firstly, we conducted a Longitudinal Latent Class Analysis to identify noise annoyance profiles. We further analysed the effect of Big Five personality traits on the likelihood of belonging to these annoyance profiles. Secondly, we used Cross-lagged Panel Models to analyse whether changes in noise annoyance precede changes in health outcomes or vice versa. For both analyses, we used 8 years of data from the Dutch Longitudinal Internet Studies for the Social Sciences (LISS) panel. Between 2708 and 11,068 subjects were included (this varies between models). RESULTS: We found three profiles of noise annoyance, namely, chronically, occasionally and never annoyed. Among all participants, 12% were chronically annoyed by neighbour noise and 6% by street noise. Extraversion and emotional stability decreased the chance of belonging to the cluster of chronically annoyed, while openness had the opposite effect. Chronic noise annoyance showed a significant effect on self-reported heart complaints and sleeping problems, while the effects of noise annoyance profiles on high blood pressure and heart attacks were insignificant. Some potential indications for a reverse effect from health outcomes on noise annoyance were found. CONCLUSION: Noise annoyance was relatively stable over time possibly because of its correlation with personality traits. Noise had a small negative effect on health outcomes, and some health outcomes affected noise annoyance. Further research should be conducted to collect dedicated panel data.

The integration of Advanced Driver Assistance Systems (ADAS) in vehicles marks an advancement in automotive safety and driving efficiency. However, to obtain the benefits of ADAS, drivers need to understand and utilise the systems properly. This study investigates the strategies employed by U.S. drivers to learn about and operate ADAS, focusing on both the learning channels through which they acquire information and the learning content of that information. Through in-depth interviews with twenty-three drivers, who are experienced with SAE Level 2 partial driving automation, this study explores the learning methods, experiences, and knowledge drivers gain when interacting with ADAS features. The findings show that trial-and-error is the predominant method drivers use to learn about ADAS. In addition, dealerships are critical points for providing initial information, but there is variability in the quality and timing of information provided by sales personnel. In the initial learning phase, it is important to convey critical information, such as the basic functionality, operation, and limitations, while avoiding information overload. In addition, drivers expressed a desire to receive feedback on the status of these features and the reasons for any occurrences while driving. The study highlights the necessity of comprehensive learning strategies incorporating multiple learning channels, including driving schools, dealerships, digital resources, and practical experience through trial-and-error. This research shows the importance of a structured and adaptable approach to ADAS learning, tailored to drivers’ preferences and behaviours, to maximise the safety and utility of driving automation.

The effectiveness of neural network models largely relies on learning meaningful latent patterns from data, where self-supervised learning of informative representations can enhance model performance and generalisability. However, self-supervised representation learning for spatially characterised time series, which are ubiquitous in transportation domain, poses unique challenges due to the necessity of maintaining fine-grained spatio-temporal similarities in the latent space. In this study, we introduce two structure-preserving regularisers for the contrastive learning of spatial time series: one regulariser preserves the topology of similarities between instances, and the other preserves the graph geometry of similarities across spatial and temporal dimensions. To balance the contrastive learning objective and the need for structure preservation, we propose a dynamic weighting mechanism that adaptively manages this trade-off and stabilises training. We validate the proposed method through extensive experiments, including multivariate time series classification to demonstrate its general applicability, as well as macroscopic and microscopic traffic prediction to highlight its particular usefulness in encoding traffic interactions. Across all tasks, our method preserves the similarity structures more effectively and improves state-of-the-art task performances. This method can be integrated with an arbitrary neural network model and is particularly beneficial for time series data with spatial or geographical features. Furthermore, our findings suggest that well-preserved similarity structures in the latent space indicate more informative and useful representations. This provides insights to design more effective neural networks for data-driven transportation research. Our code is made openly accessible with all resulting data at this https URL: https://github.com/yiru-jiao/spclt

Identifying driving heterogeneity plays an important role in improving traffic safety and efficiency. This paper proposes a novel framework to identify driving heterogeneity from the underlying characteristics of driving behaviour. The framework includes three processes: Action phase extraction, Action pattern calibration, and Action pattern classification. The concepts of Action phase and Action patterns are proposed to decipher and interpret driving behaviours. Action phases are extracted by rule-based segmentation methods and Action patterns are calibrated based on an unsupervised learning approach. The extraction and calibration processes provide a rigorous labelling approach for the attention-based LSTM Action pattern classification process. Evaluation of the framework on a large-scale naturalistic driving dataset reveals six distinct Action patterns. The implementation of the attention mechanism to LSTM models significantly enhanced both the accuracy and time efficiency of Action pattern identification. The proposed framework offers benefits in detecting and reducing variability in driving behaviour through ITS applications such as user-based traffic management, personalised Advanced Driver Assistance Systems (ADAS), and advanced autonomous vehicles (AV) design, thereby enhancing road safety and traffic efficiency.

Driving heterogeneity significantly influences traffic performance, contributing to traffic disturbances, increased crash risks, and inefficient fuel use and emissions. With the growing availability of driving behaviour data, Machine Learning (ML) techniques have become widely used for analysing driving behaviour and identifying heterogeneity. This paper presents a systematic review of current ML-based methods for driving heterogeneity identification. The review organises key concepts and categorisations of driving heterogeneity, highlights strengths and drawbacks of various methods, and outlines applications of identification analysis. Based on the literature review, we propose a structured framework that guides the ML-based identification process. The framework starts with an extensive data collection and rigorous pre-processing process, followed by feature selection techniques that identify features most indicative of driving behaviours. Sophisticated models including supervised, unsupervised, semi-supervised, and reinforcement learning techniques are discussed with multi-perspective performance evaluation. This paper provides a comprehensive reference for researchers and practitioners to understand driving heterogeneity, supporting the development of data-driven solutions for improving traffic management and road safety.

Sound source classification is a valuable addition to noise monitoring, providing ‘further insights into local soundscapes. For privacy preservation, this classification often must be conducted on the edge, i.e., in real time on noise sensors. This puts constraints on the size and complexity of the classification models that can be used. Furthermore, there is a trade-off between accuracy and efficiency, which needs to be balanced on battery or solar powered sensors. However, little is known about this trade-off under consideration of constraints imposed by such sensors. In this paper, we explore the scope of sound classification models that can run efficiently on low-cost sound sensors. Specifically, we investigate the Pareto frontiers between model accuracy and computational complexity, providing insights into the trade-off necessary for deploying such models on very constrained hardware. Building on these findings, we train new classification models optimized for edge devices. The models are trained on publicly available audio samples and a new Dutch Urban Sounds dataset specifically collected to enhance the accuracy of sound source classification in urban environments. The models and implementation are open source, enabling researchers and practitioners to adopt, adapt, and build upon our work.

With a plethora of different seemingly diverging expansions for use of Meaningful Human Control (MHC) in practice, this paper proposes an alignment for the operationalisation of MHC for autonomous systems by proposing operational principles for MHC and introducing a generic framework for its application. The increasing integration of autonomous systems in various domains emphasises a critical need to maintain human control to ensure responsible safety, accountability, and ethical operation of these systems. The concept of MHC offers an ideal concept for the design and evaluation of human control over autonomous systems, while considering human and technology capabilities. Through conceptual synthesis of existing literature and investigation across various domains and related concepts, principles for the operationalisation of MHC are set out to provide tangible guidelines for researchers and practitioners aiming to implement MHC in their systems. The proposed framework dissects generic components of systems and their subsystems aligned with different agents, stakeholders and processes at different levels of proximity to an autonomous technology. The framework is domain-agnostic, emphasizing the universal applicability of the MHC principles irrespective of the technological context, paving the way for safer and more responsible autonomous systems.

This study applies an extended version of one of the most popular technology acceptance models, the Unified Theory of Acceptance and Use of Technology (UTAUT2), to predict user acceptance of SAE Level 3 conditional automated driving among more than 9,000 car drivers from nine European and non-European countries. We extend the model by two factors, trust and teaming, that we consider pivotal for user acceptance of conditional automated driving. We also investigate the factors impacting the determinants of acceptance and use of conditional automated driving, addressing a well-known gap in research. In this study we find that 40% of respondents did not intend to buy, and 39% of respondents did not express an intention to use conditional automated driving when available. 71% of respondents indicated a preference to stay engaged in the driving task to respond to requests from the car to resume manual control. The structural equation modeling analysis revealed that performance expectancy is the strongest predictor of driver’s behavioral intentions to use conditional automated driving, followed by trust and social influence. Contrary to common beliefs positioning trust as one of the most influential drivers of user acceptance of AVs, the influence of trust on behavioral intention to use conditional automated driving is small. The availability of facilitating conditions supporting the use conditional automated driving (e.g., knowledge, getting help from friends, family, or car dealers) has a small influence on the acceptance of AVs. We also found significant effects of the factors impacting the determinants of acceptance and use. The effect of performance expectancy on hedonic motivation is positive, suggesting that the perceived usefulness positively enhances the perceived enjoyment. Similarily, the effect of social influence on performance expectancy and trust is positive, suggesting the social network of the individual plays an important role in promoting positive beliefs about the effectiveness of the technology and trust in the technology. Access to participation in the questionnaire was limited to respondents with access to internet, which is why future research should be performed with respondents without internet accessibility to examine differences in attitudes and conditional automated driving acceptance between these internet-affine and less internet-affine groups.

The integration of automated vehicles (AVs) into transportation systems presents an unprecedented opportunity to enhance road safety and efficiency. However, understanding the interactions between AVs and human-driven vehicles (HVs) at intersections remains an open research question. This study aims to bridge this gap by examining behavioral differences and adaptations of AVs and HVs at unsignalized intersections by utilizing two large-scale AV datasets from Waymo and Lyft. By using a systematic methodology, the research identifies and analyzes merging and crossing conflicts by calculating key safety and efficiency metrics, including time to collision, post-encroachment time, maximum required deceleration, time advantage, and speed and acceleration profiles. Through this approach, the study assesses the safety and efficiency implications of these behavioral differences and adaptations for mixed-autonomy traffic. The findings reveal a paradox: while AVs maintain larger safety margins, their conservative behavior can lead to unexpected situations for human drivers, potentially causing unsafe conditions. From a performance point of view, human drivers tend to exhibit more consistent behavior when interacting with AVs versus other HVs, suggesting AVs may contribute to harmonizing traffic flow patterns. Moreover, notable differences were observed between Waymo and Lyft vehicles, which highlights the importance of considering manufacturer-specific AV behaviors in traffic modeling and management strategies for the safe integration of AVs. The processed dataset, as well as the developed algorithms and scripts, are openly published to foster research on AV–HV interactions.

Deep Reinforcement Learning (DRL) has made remarkable progress in autonomous vehicle decision-making and execution control to improve traffic performance. This paper introduces a DRL-based mechanism for cooperative lane changing in mixed traffic (CLCMT) for connected and automated vehicles (CAVs). The uncertainty of human-driven vehicles (HVs) and the microscopic interactions between HVs and CAVs are explicitly modelled, and different leader-follower compositions are considered in CLCMT, which provides a high-fidelity DRL learning environment. A feedback module is established to enable interactions between the decision-making layer and the manoeuvre control layer. Simulation results show that the increase in CAV penetration leads to safer, more comfort, and eco-friendly lane-changing behaviours. A CAV-CAV lane-changing scenario can enhance safety by 24.5%–35.8%, improve comfort by 8%–9%, and reduce fuel consumption and emissions by 5.2%–12.9%. The proposed CLCMT promises advantages in the lateral decision-making and motion control of CAVs.

AI in automotive is sterk in opkomst. Nieuwe voertuigen zijn always connected en worden zelflerende machines. Zo pakt de techniek een steeds grotere rol in de bediening en besturing van onze voertuigen. Wat betekent dat voor de veiligheid? En verandert dat ook de rol van de RDW?

Automated Driving Systems (ADS) are aimed to improve traffic efficiency and safety, however these systems are not yet capable of handling all driving tasks in all types of road conditions. The role of a human driver remains crucial in taking over control, if an ADS fails or reaches its operational limits. Takeover performance of human drivers in authority transitions is typically assessed by means of the takeover time (TOT) taken within an available time budget (TB). This approach assumes a uniform perception and reaction time of human drivers in ADS disengagements, and does not include the time needed to execute the actual driving maneuver required to ensure safety. This paper aims to develop and test a set of new indicators to reflect takeover performance and its safety attributes, namely the ‘time to control’ (TC) and the ‘safe time budget’ (STB), in which the actual task execution (i.e. braking) time is taken into account, in addition to the perception and reaction time. It also proposes new thresholds for identifying critical conflicts in takeover situations and assessing the safety of authority transitions. A traffic simulation experimental setup is used with mixed traffic of conventional vehicles and ACC/CACC platoons in order to test these indicators and thresholds. The results suggest that the time difference between TC and STB is a more sensitive and potentially more realistic safety indicator, as it may capture the variability of driver behavior in takeovers and identify critical conflicts, as well as virtual crashes, that would not have been identified by the previously used indicators (TOT and TB). Takeover performance worsens when the speed difference of the vehicles involved is higher, and the initial speed of the rear vehicle is higher. These findings can be useful towards a more dynamic design of takeover request strategies.

Conditionally automated driving enables drivers to engage in non-driving-related activities, with the responsibility to take over vehicle control upon request. This takeover process increases the risk of collisions, especially when drivers fail to safely complete takeovers within limited time budgets (i.e., the time offered by automation for takeovers). This phenomenon underlines the significance of providing time budgets that sufficiently accommodate drivers' takeover time (i.e., the time required by drivers to resume conscious control of vehicles). Considering that drivers' takeover time varies significantly across scenarios, this study centres on understanding the role of driver perception in takeover time using the Task-Capability Interface (TCI) theory. The TCI theory suggests that drivers adjust their behaviours based on their perceived task demands and driver capabilities. Accordingly, in a driving simulator experiment featuring diverse traffic densities and distractions, we investigated drivers' takeover time while capturing their perceived task demands and capabilities through a takeover-oriented questionnaire based on established instruments. The results show that drivers generally have longer takeover time as their perceived task demand rises, perceived driver capability diminishes, and perceived spare capacity (perceived driver capability minus perceived task demand) decreases. These patterns fluctuate under conditions of low perceived task demand or high perceived driver capability. When both conditions coincide, drivers necessitate a considerably longer time to regain vehicle control. Our findings on takeover time contribute to the development of strategies aimed at predicting drivers' takeover time, optimizing time budgets, fostering human-centred vehicle design, and enhancing the safety of conditionally automated driving.