Passive fatigue during conditional automated driving can compromise driver readiness and safety. This paper presents findings from a test-track study with 40 participants in a real-world automated driving scenario. In this scenario, a Large Language Model (LLM) based conversational agent (CA) was designed to check in with drivers and re-engage them with their surroundings. Drawing on in-car video recordings, sleepiness ratings and interviews, we analysed how drivers interacted with the agent and how these interactions shaped alertness. Results show the CA is helpful for supporting vigilance during passive fatigue. Thematic analysis of acceptability further revealed three user preference profiles that implicate future intention to use CAs. Positioning empirically observed profiles within existing CA archetype frameworks highlights the need for adaptive design sensitive to diverse user groups. This work underscores the potential of CAs as proactive Human–Machine Interface (HMI) interventions, demonstrating how natural language can support context-aware interaction during automated driving.

When Automated Vehicles (AVs) navigate dynamic, interactive driving scenarios, they must consider the spatio-temporal layout of surrounding traffic, including social interactions among agents, to accurately predict their trajectories. Existing approaches often fail to handle complex inter-agent interactions with the necessary adaptive attention to dynamic contexts. This paper introduces a multi-agent trajectory prediction algorithm that leverages attentive spatio-temporal modelling to capture interactions among agents. Our approach integrates weighted Distance Graph Attention Networks (wDGAT) with dynamic attention assignment and Multi-Head Attention (MHA)-based Transformers to learn multi-headed social interaction patterns, preserving this critical information throughout the learning pipeline. This enables efficient aggregation of information from any number of neighbouring agents, allowing for robust processing of complex, time-dependent data and consistent retrieval of spatio-temporal knowledge across extended prediction horizons. We validate our model through extensive experiments on the NGSIM (US-101 and I-80) highway datasets. The results demonstrate that our approach consistently achieves the lowest prediction error over a 5-second horizon, producing diverse outcomes for different agents and outperforming state-of-the-art methods. Numerical results demonstrate that, compared with state-of-the-art models, the proposed model reduces the average prediction root-mean-square error over a five-second time horizon by 40% and achieves a median performance gain of 20% on large-scale public datasets. Ablation studies further confirm the effectiveness of our algorithm.

This paper reports results from a high-fidelity driving simulator study (N=215) about a head-up display (HUD) that conveys a conditional automated vehicle’s dynamic “uncertainty” about the current situation while fallback drivers watch entertaining videos. We compared (between-group) three design interventions: display (a bar visualisation of uncertainty close to the video), interruption (interrupting the video during uncertain situations), and combination (a combination of both), against a baseline (video-only). We visualised eye-tracking data to conduct a heatmap analysis of the four groups’ gaze behaviour over time. We found interruptions initiated a phase during which participants interleaved their attention between monitoring and entertainment. This improved monitoring behaviour was more pronounced in combination compared to interruption, suggesting pre-warning interruptions have positive effects. The same addition had negative effects without interruptions (comparing baseline & display). Intermittent interruptions may have safety benefits over placing additional peripheral displays without compromising usability.

Connected and automated vehicles (CAVs) are poised to transform the transport system. However, significant uncertainties remain about their impact, particularly regarding concerns that this advanced technology might exacerbate injustices, such as safety disparities for vulnerable road users (VRUs). Therefore, understanding the potential conflicts of this technology with societal values such as justice and safety is crucial for responsible implementation. To date, no research has focused on what safety and justice in transport mean in the context of CAV deployment and how the potential benefits of CAVs can be harnessed without exacerbating the existing vulnerabilities and injustices VRUs face. This paper addresses this gap by exploring car drivers’ and pedestrians’ perceptions of safety and justice issues that CAVs might exacerbate using an existing theoretical framework. Employing a qualitative approach, the study delves into the nuanced aspects of these concepts. Interviews were conducted with 30 participants (40% pedestrians) in Queensland, Australia, aged between 18 and 79. These interviews were recorded, transcribed, organised, and analysed using reflexive thematic analysis. Three main themes emerged from the participants’ discussions: (1) CAVs as a safety problem for VRUs, (2) CAVs as a justice problem for VRUs, and (3) CAVs as an alignment with societal values problem. Participants emphasised the safety challenges CAVs pose for VRUs, highlighting the need for thorough evaluation and regulatory oversight. Concerns were also raised about CAVs potentially marginalising vulnerable groups within society. Participants advocated for inclusive discussions and a justice-oriented approach to designing a comprehensive transport system to address these concerns.

Automated Vehicles (AVs) have been receiving increasing attention as a potential highly mechanised, intelligent, self-regulating futuristic mode of transport. AVs are predicted to address limitations and human factors associated with traditional modes of transportation. Beyond the typical operations of AVs which can perform rudimentary tasks, the intelligent embedded program fit in to process challenging scenarios and deep multi-dimensional/ agent intents and interaction of the roadway is the grey area yet to be explored to design an exclusive encoding of social functionality and operation in order to address human factors causing road crashes. The aim of this study is to design a data-driven prediction framework for AVs that utilises multiple inputs to prove a multimodal, probabilistic estimate of the future intentions and trajectories of surrounding vehicles in freeway operation. Our proposed framework is a deep multi-agent learning-based system designed to effectively capture social interactions between vehicles without relying on map information. Our approach excels in capturing the high-level behaviours of multiple vehicles and generating a multi-modal trajectory forecast. It employs a multi-headed neural architecture to learn from social interactions between vehicle pairs and generates diverse trajectories proportional to predicted target intents, thus enabling feature fusion. Additionally, a multi-head self-attention mechanism is incorporated for prediction refinement. We achieved a good prediction performance with a lower prediction error in real traffic data at highways. Evaluation of the proposed framework using the NGSIM (US-101 and I-80) and HighD datasets shows satisfactory prediction performance for long-term trajectory prediction of multiple surrounding vehicles. Additionally, the proposed framework has higher prediction accuracy and generalisability than state-of-the-art approaches.

This paper presents an early prediction framework to classify drivers’ intended intersection movements in a connected vehicle environment. Intersections are considered accident blackspots with major traffic violations that cause property damage, injuries and fatalities. An accurate perception of drivers’ intended movements at intersections is required for advanced red-light (ARLW) or turning warnings for vulnerable road users (TWVR). Early prediction of intersection movement and adequate warning assistance will ensure road users’ safety at the intersection. In this study, we adopted recurrent neural networks (RNN): long short-term memory (LSTM) and gated recurrent units (GRU) networks to predict driver intended movements at intersections using the vehicle kinematics extracted from the Cooperative Awareness Messages (CAMs). We used naturalistic driving data of the Ipswich Connected Vehicle Pilot (ICVP) project, Queensland, which was collected from 351 participants who drove their connected vehicles during the pilot period. The pilot study installed roadside equipment at 29 signalised intersections to enable the Cooperative Intelligent Transportation System (C-ITS) use cases. Vehicle speed, speed limit, longitudinal acceleration, lateral acceleration, and yaw rate are used as predictors and monitored in 100-millisecond intervals for 1s to 4s at different warning distances from the stop line. Separate prediction models are trained based on different monitoring windows. Furthermore, drivers’ intended intersection movements are predicted at two individual intersections to evaluate intersection-specific prediction performance and are found with improved prediction accuracy than overall prediction models trained with all 29 intersections data. Overall prediction models are useful for some intersections which lack available data for individual intersection-based prediction.

Human errors contribute to 94%(±2.2%) of road crashes resulting in fatal/non-fatal causalities, vehicle damages and a predicament in the pathway to safer road systems. Automated Vehicles (AVs) have been a potential attempt in lowering the crash rate by replacing human drivers with an advanced computer-aided decision-making approach. However, AVs are yet to progress in handling the unprecedented situations involving interactions with other road users. This raises a need for a sophisticated and robust methodological framework to predict human driver interaction and intention. It is of prime importance to develop a constructive knowledge on the existing literature for a proficient forward leap in the field. To address this, we aim to conduct a comprehensive review on motion prediction methods in automated driving context with a special emphasis on model-based and data-driven approaches. Over a hundred studies related to the motion prediction for AVs have been extensively reviewed. This study recommends that the field requires more intricate classification of motion prediction methods, as the conventional three-level categorisation scheme should be upgraded to a profound and present-day context. Therefore, we attempt to provide a clear categorisation of existing motion prediction solutions by adopting four principal strategies: 1. Prediction methods, 2. Classes, 3. Algorithms and 4. Datasets. An all-inclusive summary of the reviewed studies with their respective pros and cons are also presented. Furthermore, we summarise the standard evaluation metrics applied for road users’ intention estimation and trajectory prediction tasks. It is found that the recent studies are built upon multi-agent learning systems with interaction among multiple road users in the same road environment. These methods can provide reliable prediction performance in highly interactive situations over long periods of time. However, the limitation could be at the cost of higher computational complexity in comparison to conventional methods, which are simpler to design and computationally effective. It is also observed that the conventional methods can only operate over a narrow prediction horizon and seldom consider the interactions among the road users. This review contributes to knowledge in validation, addresses the discrepancies, to explicate the ambiguities and to streamline current research for a futuristic perspective beneficiary in motion prediction field.

Automatically assessing driving behaviour against traffic rules is a challenging task for improving the safety of Automated Vehicles (AVs). There are no AV specific traffic rules against which AV behaviour can be assessed. Moreover current traffic rules can be imprecisely expressed and are sometimes conflicting making it hard to validate AV driving behaviour. Therefore, in this paper, we propose a Defeasible Deontic Logic (DDL) based driving behaviour assessment methodology for AVs. DDL is used to effectively handle rule exceptions and resolve conflicts in rule norms. A data-driven experiment is conducted to prove the effectiveness of the proposed methodology.

A new Ecological and Safe (EcoSafe) driving control algorithm has been recently developed by the authors for controlling the longitudinal motion of the vehicle to minimize fuel consumption while respecting safety constraints. The algorithm uses a Model predictive control framework augmented with enhanced safety constraints based on Intervehicular Time (TIV) and the Time to Collision (TTC). This algorithm requires tuning to adapt to traffic condition. In this paper we propose a tuning method for EcoSafe algorithm which is deduced from driver preference and traffic flow information. In addition, to the best of our knowledge, the benefits of similar EcoSafe algorithms have not been tested with naturalistic data. Hence, we assessed the benefits of EcoSafe algorithm in terms of eco-driving and safety by using 1,100 km of naturalistic driving data. We use velocity profile extracted from the Australian Naturalistic Driving Study (ANDS) as the leading vehicle driving behaviour. The results show that our proposed strategy has a 14% reduction in fuel consumption on average while maintaining high safety levels without increasing travel time significantly.

Driver training is one of the interventions aimed at mitigating the number of crashes that involve novice drivers. Our failure to understand what is really important for learners, in terms of risky driving, is one of the many drawbacks restraining us from building better training programs. Currently, there is a need to develop and evaluate advanced driving assistance systems that could comprehensively assess driving competencies. The aim of this paper is to present a novel intelligent driver training system that analyzes crash risks for a given driving situation, providing avenues for the improvement and personalization of driver training programs. The analysis takes into account numerous variables synchronously acquired from the driver, the vehicle, and the environment. The system then segments out the maneuvers within a drive. This paper further presents the fuzzy set theory to develop the safety inference rules for each maneuver executed during the drive, and presents a framework and its associated prototype that can be used to comprehensively view and assess complex driving maneuvers and then provides a comprehensive analysis of the drive used to give feedback to novice drivers.

Cooperative Systems provide, through the multiplication of information sources over the road, a lot of potential to improve the assessment of the road risk describing a particular driving situation. In this paper, we compare the performance of a cooperative risk assessment approach against a non-cooperative approach; we used an advanced simulation framework, allowing for accurate and detailed, close-to-reality simulations. Risk is estimated, in both cases, with combinations of indicators based on the TTC. For the noncooperative approach, vehicles are equipped only with an AAC-like forward-facing ranging sensor. On the other hand, for the cooperative approach, vehicles share information through 802.11p IVC and create an augmented map representing their environment; risk indicators are then extracted from this map. Our system shows that the cooperative risk assessment provides a systematic increase of forward warning to most of the vehicles involved in a freeway emergency braking scenario, compared to a non-cooperative system.

This paper reports on the implementation of a non-invasive electroencephalography-based brain-computer interface to control functions of a car in a driving simulator. The system is comprised of a Cleveland Medical Devices BioRadio 150 physiological signal recorder, a MATLAB-based BCI and an OKTAL SCANeR advanced driving experience simulator.

Digital information that is place- and time-specific, is increasingly becoming available on all aspects of the urban landscape. People (cf. the Social Web), places (cf. the Geo Web), and physical objects (cf. ubiquitous computing, the Internet of Things) are increasingly infused with sensors, actuators, and tagged with a wealth of digital information. Urban informatics research explores these emerging digital layers of the city at the intersection of people, place and technology. However, little is known about the challenges and new opportunities that these digital layers may offer to road users driving through today's mega cities. We argue that this aspect is worth exploring in particular with regards to Auto-UI's overarching goal of making cars both safer and more enjoyable. This paper presents the findings of a pilot study, which included 14 urban informatics research experts participating in a guided ideation (idea creation) workshop within a simulated environment. They were immersed into different driving scenarios to imagine novel urban informatics type of applications specific to the driving context.

IEEE 802.11p is the new standard for inter-vehicular communications (IVC) using the 5.9 GHz frequency band; it is planned to be widely deployed to enable cooperative systems. 802.11p uses and performance have been studied theoretically and in simulations over the past years. Unfortunately, many of these results have not been confirmed by on-tracks experimentation. In this paper, we describe field trials of 802.11p technology with our test vehicles. Metrics such as maximum range, latency and frame loss are examined.

Inter-Vehicular Communications (IVC) are considered a promising technological approach for enhancing transportation safety and improving highway efficiency. Previous theoretical work has demonstrated the benefits of IVC in vehicles strings. Simulations of partially IVC-equipped vehicles strings showed that only a small equipment ratio is sufficient to drastically reduce the number of head on collisions. However, these results are based on the assumptions that IVC exhibit lossless and instantaneous messages transmission. This paper presents the research design of an empirical measurement of a vehicles string, with the goal of highlighting the constraints introduced by the actual characteristics of communication devices. A warning message diffusion system based on IEEE 802.11 wireless technology was developed for an emergency breaking scenario. Preliminary results are presented as well, showing the latencies introduced by using 802.11a and discussing early findings and experimental limitations.

Pedestrians' use of Motion Pictures Expert Group audio layer 3 players or mobile phones can pose the risk of being hit by motor vehicles. We present an approach for detecting a crash risk level using the computing power and the microphone of mobile devices that can be used to alert the user in advance of an approaching vehicle so as to avoid a crash. A single feature extractor classifier is not usually able to deal with the diversity of risky acoustic scenarios. In this paper, we address the problem of detection of vehicles approaching a pedestrian by a novel simple nonresource intensive acoustic method. The method uses a set of existing statistical tools to mine signal features. Audio features are adaptively thresholded for relevance and classified with a three-component heuristic. The resulting acoustic hazard detection system has a very low false-positive detection rate. The results of this study could help mobile device manufacturers to embed the presented features into future potable devices and contribute to road safety.

Summary form only given. Cooperative systems, also known as V2I and V2V-based, are becoming increasingly important in meeting the demand for safer and more advanced driving assistance systems. Research on wireless networks to support inter-vehicular communications has made great advances. Cooperative systems will significantly enhance and increase driver's ability to be aware, anticipate and react to crash risk situations. Provision of such enhanced capabilities must meet the wellestablished features of existing wireless network systems, such as robust scalability and reliability but, more importantly cater for driver's needs, cognitive and physiological limitations. There has been much work in the area of driver's situation awareness in recent years. Situation awareness is the primary basis for subsequent driver's decision-making and performance in the operation of complex driving tasks such as maneuvering or anticipation. However there is still a gap between the two research fields despite their potentials to improve road safety. This talk will highlight interdisciplinary research issues at the frontier of cooperative systems and human cognition. The focus will be on drivers' situational awareness needs/capabilities and how cooperative systems can address them with the view to improve road safety.

Older drivers represent the fastest growing segment of the road user population. Cognitive and physiological capabilities diminishes with ages. The design of future in-vehicle interfaces have to take into account older drivers' needs and capabilities. Older drivers have different capabilities which impact on their driving patterns and subsequently on road crash patterns. New in-vehicle technology could improve safety, comfort and maintain elderly people's mobility for longer. Existing research has focused on the ergonomic and Human Machine Interface (HMI) aspects of in-vehicle technology to assist the elderly. However there is a lack of comprehensive research on identifying the most relevant technology and associated functionalities that could improve older drivers' road safety. To identify future research priorities for older drivers, this paper presents: (i) a review of age related functional impairments, (ii) a brief description of some key characteristics of older driver crashes and (iii) a conceptualisation of the most relevant technology interventions based on traffic psychology theory and crash data.

Nowadays, more and more driving assistances are available to help the driver and to improve the vehicle handling. With increasing sensing capacities, it also becomes possible to have a local view of the vehicle surrounding. Hence, the next steps are to provide the driving assistances a supervisor that schedules all the possible actions and plane trajectories. In this article, we propose a decision method that fits the driver decision and action schemes. The system is at three levels, for action, decision and long range planning. the article is focused on the second layer and the interaction with other layer. The second layer algorithm, based on risk assumption, evaluates, in the vehicle vicinity, the risk related to each detected object. It then computes possible actions for lower level layer. The developed method is constrained by the future implementation on a vehicle : low computation time available and small memory size.

Abstract Applications that exploit contextual information in order to adapt their behaviour to dynamically changing operating environments and user requirements are increasingly being explored as part of the vision of pervasive or ubiquitous computing. Despite recent advances in infrastructure to support these applications through the acquisition, interpretation and dissemination of context data from sensors, they remain prohibitively difficult to develop and have made little penetration beyond the laboratory. This situation persists largely due to a lack of appropriately high‐level abstractions for describing, reasoning about and exploiting context information as a basis for adaptation. In this paper, we present our efforts to address this challenge, focusing on our novel approach involving the use of preference information as a basis for making flexible adaptation decisions. We also discuss our experiences in applying our conceptual and software frameworks for context and preference modelling to a case study involving the development of an adaptive communication application. Copyright © 2006 John Wiley & Sons, Ltd.

Recent advances in mobile devices create a need for computing architectures and applications which are able to react to environmental changes in order to adapt to the changing context of computation. To date insufficient attention has been paid to the issues of defining an open component-based architecture which is able to describe complex computational context and handle different types of adaptation for a variety of new and existing pervasive enterprise applications. In this paper an architecture for pervasive enterprise systems is proposed. The architecture uses a component based modelling paradigm and an event-based mechanism which provides significant flexibility in dynamic system configuration and adaptation. The architecture includes context management which captures descriptions of complex user, device and application context including enterprise roles and role policies, and allows easy extension by new types of context. The architecture provides an open approach to adaptation which allows easy extension with adaptation mechanisms. In addition, the coordination language used to coordinate system events provides the flexibility needed in pervasive computing applications to support dynamic reconfiguration and a variety of communication paradigms.

In this paper we outline a semantic model for open distributed systems which provides a foundation for a corresponding architecture description language. This semantic model is based on reported architecture models, with a number of refinements to support abstraction and composition. The model is specifically designed to describe open distributed systems independently of implementation details such as communication protocols and middleware systems. The modelling concepts in the semantic model are: object (a model of an entity), event (a unit of interaction between an object and its environment), event relationship (a specification of behaviour defining the relationships amongst a set of events), interface (an abstraction of an object's interaction with its environment) and binding (a context for interaction between objects). The binding concept is particularly important because it can describe any kind of interaction in an open distributed system, ranging from remote procedure calls and multicast to more complex, enterprise interactions. Special attention is given to the problem of composition and abstraction of events and behaviour in the model. This is needed to reflect the reuse, evolution and interworking requirements of open distributed systems. Our approach allows for the effective modelling of asynchrony, concurrency and complex flows of information in open distributed systems.

The transaction concept was originally applied to database applications. Serializability theory captured transaction correctness and database objects consistency properties in a single notion. Today, increasingly sophisticated information requires new correctness criteria due to the limitation of classical serialisability theory which allows only a limited cooperation between its components. Several models relaxing the ACID (Atomicity, Consistency, Isolation, Durability) properties in a controlled manner have been developed. These approaches exploit separately the semantics properties of operations (object semantic approach) and application semantics (transaction interleaving approach). The notion of correctness can be refined with the help of the two previous approaches whilst increasing concurrency. In this paper, we will the gap between transaction and object semantic correctness criteria. We define a new class of schedule called Multilevel Relative Serialisability (MLRS) to combine the two approaches. This class of schedule preserve correctness properties defined in terms of object and transaction semantics. We use ACTA formalism to express object consistency, transaction correctness and MLRS. This work merges existing /spl Lt/relaxed/spl Gt/ transaction models into a unified concept. This concept is useful for long-lived, cooperative and hierarchical transaction models.