The recent advancements in vehicle automation -and associated shift from driving to non-driving activities -has increased the importance of in-vehicle driver monitoring that is safe, trustworthy and useable. Physiological measurement of driver monitoring (rather than just eye tracking) is a nascent approach gaining attention in the space of in-vehicle technologies; however, existing contact sensor-based approaches raise concerns regarding system usage, complexity and privacy. This paper presents research which developed a novel, contactless heartrate monitoring system for drivers using Frequency Modulated Continuous Wave (FMCW) short-range radars, which was validated in vehicular environments. A combination of signal processing and neural network methodologies, incorporating Long Short-Term Memory (LSTM), was adopted to mitigate the effects of body motion and other motion artifacts that cause noisy radar data. The neural network was trained on ground truth data collected in parallel using a medical-grade BIOPAC ECG system. Similarly, the results were validated and compared against this ground truth. The experimental results evidence that FMCW radars are a promising methodology for in-vehicular cardio physiological applications, displaying an overall accuracy of 93% in detecting drivers’ heartrate (HR) and inter-beat-interval (IBI). Additionally, there was no significant difference observed in the RMSE results for driving and non-driving conditions, evidencing that the methodology performed efficiently in both the conditions. This paper demonstrates the benefits of FMCW radars for contactless physiological driver monitoring applications within automotive domains, and beyond.

Trust in automation is crucial for the safe and appropriate adoption of automated driving technology. Current research methods to measure trust mainly rely on subjective scales, with several intrinsic limitations. This empirical experiment proposes a novel method to measure trust objectively, using functional near-infrared spectroscopy (fNIRS). Through manipulating participants’ expectations regarding driving automation credibility, we have induced and successfully measured opposing levels of trust in automation. Most notably, our results evidence two separate yet interrelated cortical mechanisms for trust and distrust. Trust is demonstrably linked to decreased monitoring and working memory, whereas distrust is event-related and strongly tied to affective (or emotional) mechanisms. This paper evidence that trust in automation and situation awareness are strongly interrelated during driving automation usage. Our findings are crucial for developing future driver state monitoring technology that mitigates the impact of inappropriate reliance, or over trust, in automated driving systems.

Highly automated driving will likely result in drivers being out-of-the-loop during specific scenarios and engaging in a wide range of non-driving related tasks. Manifesting in lower levels of risk perception to emerging events, and thus affect drivers’ availability to take-over manual control in safety-critical scenarios. In this empirical research, we measured drivers’ (N = 20) risk perception with cardiac and skin conductance indicators through a series of high-fidelity, simulated highly automated driving scenarios. By manipulating the presence of surrounding traffic and changing driving conditions as long-term risk modulators, and including a driving hazard event as a short-term risk modulator, we hypothesised that an increase in risk perception would induce greater physiological arousal. Our results demonstrate that heart rate variability features are superior at capturing arousal variations from these long-term, low to moderate risk scenarios. In contrast, skin conductance responses are more sensitive to rapidly evolving situations associated with moderate to high risk. Based on this research, future driver state monitoring systems should adopt multiple physiological measures to capture changes in the long and short term, modulation of risk perception. This will enable enhanced perception of driver readiness and improved availability to safely deal with take-over events when requested by an automated vehicle.

Highly Automated Driving technology will be facing major challenges before being pervasively integrated across production vehicles. One of them will be monitoring drivers’ state and determining whether they are ready to take over control under certain circumstances. Thus, we have explored their physiological responses and the effects on trust of different scenarios with varying traffic complexity in a driving simulator. Using a mixed repeated measures design, twenty-seven participants were divided in two reliability groups with opposite induced automation reliability expectations -low and high-. We hypothesized that expectations would modulate participants’ trust in automation, and consequently, their physiological responses across different scenarios. That is, increasing traffic complexity would also increase participants’ arousal, and this would be accentuated or mitigated by automation reliability expectations. Although reliability group differences could not be observed, our results show an increase of physiological activation within high complexity driving conditions (i.e., a mentally demanding non-driving related task and urban scenarios). In addition, we observed a modulation of trust in automation according to the group expectations delivered. These findings provide a background methodology from which further research in driver monitoring systems can benefit and be used to train machine learning methods to classify drivers’ state in changing scenarios. This would potentially help mitigate inappropriate take-overs, calibrate trust and increase users’ comfort and safety in future Highly Automated Vehicles.

Partially automated vehicles present a large range of information to the driver in order to keep them in-the-loop and engaged with monitoring the vehicle’s actions. However, existing research shows that this causes cognitive overload and disengagement from the monitoring task. Adaptive Human Machine Interfaces (HMIs) are an emerging technology that might address this problem, by prioritising the information presented. To date, research aiming to define the driver’s glance fixation behaviour in a partially automated vehicle to contribute towards an adaptive interface is scarce. This study used a unique three-day longitudinal driving simulator study design to explore which information drivers in a partially automated vehicle require. Twenty-seven participants experienced nine partially automated driving simulations over three consecutive days. Nine information types, developed from standards, previous studies and industry collaboration, were displayed as discrete icons and presented on a surrogate in-vehicle display. Unique to the literature, this study showed that the recorded eye-tracking data demonstrated that usage of the information types changed with longitudinal driving simulator use. This study provides three key contributions: first, the longitudinal study design suggest that single exposure HMI evaluations may be limited in their assessment. Secondly, this study has methodologically shortlisted a list of nine information types that can be used in future studies to represent future partially automated vehicle interfaces. Finally, this is one of the first studies to characterise glance behaviour for partially automated vehicles. With this knowledge, this study contributes important design recommendations for the development of adaptive interfaces.

The automotive industry is at the forefront of the smart, connected and autonomous vehicle (SCAV) revolution to improve social mobility with safety and road utilization. Also, more and more countries and cities have announced restrictions on future internal combustion vehicle sales or use for cleaner transport. Hence, together with urgent demands for highly energy-efficient transport systems, the need to make them safer, greener, and smarter has been increasing rapidly in recent years. As a result, the development of intelligent transportation system (ITS) underpinned by advanced propulsion and innovative control systems is known as a feasible solution to address all of these challenges.

Recently, there has been a considerable improvement in low-speed autonomous vehicles (LSAVs), which will function key roles in future intelligent transportation systems. To be successfully distributed on a real road, these vehicles must have the ability to drive autonomously along collision-free paths whilst flowing traffic laws. LSAVs use Lidar sensors to avoid obstacles in its path. However, Lidar sensors have unreliability limitation, which consequently any decision made by sensors alone is insufficient and has let to serious accidents. This is because the difficulties to determine in the sensor fusion system, how wrong information can affect the decision made by the vehicle. In this paper, an observer system is present for fault detection of automated sensor fusion system for a LSAV, which functions based on octree fusion. Through this study, an analytical observer processing the information obtained by physical redundancy and an octree fusion process based on a probabilistic model of occupation of the voxels. This method shows that the decision made by the vehicle is more accurate than the existing system especially when a sensor sends incorrect information to the sensor fusion system.

Localization accuracy obtainable from global navigation satellites systems in built up areas like urban canyons and multi-storey car parks is severely impaired due to multipath and non-line-of-sight signal propagation. In this paper, a simple classifier was used in discriminating between multipath and line-of-sight GNSS signals. By using the carrier to noise ratio which characterizes the received signal strength of the GNSS signals, and the rate of change of the epochs of the satellite vehicles in view, a prediction accuracy of 98% was attained from the classifier. Also investigated in this paper is the effect of antenna placement on localization accuracy. Our measurement campaign using a Nissan Leaf hatch back model showed that the centre longitudinal line of the roof generated the least localization errors for an urbanized route.

As part of a large UK-funded autonomous vehicle project (UK Autodrive), we examined pedestrian attitudes and road-crossing intentions using a real autonomous vehicle (AV) in an indoor arena. Two conceptual external human-machine interfaces (HMIs) were presented to display the vehicle's manoeuvring intentions. Participants experienced a simulated road-crossing task to assess their interactions with the AV. Although neither HMI concept was entirely free of criticism, there were objective performance differences for a projection-based HMI concept, as well as critical subjective opinions in pedestrian responses to specific manoeuvring contexts. These provided insight into pedestrians' safety concerns towards a vehicle where bi-directional communication with a driver is no longer possible, with suggestions for future vehicle HMI concepts.

Understanding how best to present information inside a semi-automated vehicle is a prevalent challenge in HMI design. There is an understanding that a driver's trust and previous driving experience can affect the information they require inside a semi-automated vehicle. However, to date little is known about how these predispositions specifically affect the types of information that should be presented and importantly, how this changes with increased exposure to an automated system. In this paper, seventeen participants experienced twenty-six minutes of an automated driving simulation once every day for a week. The information to display was carefully chosen in accordance with the Skills, Rules, Knowledge model. The information was synchronized to the driving simulation and presented on a tablet in the driving simulator. Eye tracking was used to measure the information looked at. The results showed that trust increased significantly with increased exposure, but this had no correlation to any specific piece of information viewed. Drivers who were more prone to making lapses or errors (as measured by the Driver Behavior Questionnaire) tended towards using information that was less cognitively demanding. Finally, a driver's propensity to making lapses was found to be a potential early predictor of trust, but this became less accurate with increased exposure to the semi-automated vehicle.

This paper presents results from a series of focus groups, aimed at enhancing technical engineering system requirements, for a public transport system, encompassing a fleet of platooning low-speed autonomous vehicles (LSAV; aka pods) in urban areas. A critical review of the pods was conducted, as part of a series of technical workshops, to examine the key areas of the system that could affect users and other stakeholders, such as businesses and the public. These initial findings were used to inform a series of focus groups, aimed at identifying the public's views of multiple autonomous vehicles being deployed in a pedestrianised area that can join and form platoons. Analysis of findings from the focus groups suggests that while people view platooning public transport vehicles favourably as a passenger, they have some concerns from a pedestrian perspective. Thematic analysis was applied to these findings and a systematic approach was used to identify where subjective outputs could be formalised to inform requirements. Finally, a step-by-step requirements elicitation process is presented that illustrates the method used to convert qualitative user data to objective engineering requirements.

With increasing automation, occupants of fully autonomous vehicles are likely to be completely disengaged from the driving task. However, even with no driving involved, there are still activities that will require interfaces between the vehicle and passengers. This study evaluated different configurations of screens providing operational-related information to occupants for tracking the progress of journeys. Surveys and interviews were used to measure trust, usability, workload and experience after users were driven by an autonomous low speed pod. Results showed that participants want to monitor the state of the vehicle and see details about the ride, including a map of the route and related information. There was a preference for this information to be displayed via an onboard touchscreen device combined with an overhead letterbox display versus a smartphone-based interface. This paper provides recommendations for the design of devices with the potential to improve the user interaction with future autonomous vehicles.

It is important to equip future vehicles with an onboard system capable of tracking and analyzing driver state in real-time in order to mitigate the risk of human error occurrence in manual or semi-autonomous driving. This study aims to provide some supporting evidence for adoption of consumer grade electronic devices in driver state monitoring. The study adopted repeated measure design and was performed in high-fidelity driving simulator. Total of 39 participants of mixed age and gender have taken part in the user trials. The mobile application was developed to demonstrate how a mobile device can act as a host for a driver state monitoring system, support connectivity, synchronization, and storage of driver state related measures from multiple devices. The results of this study showed that multiple physiological measures, sourced from consumer grade electronic devices, can be used to successfully distinguish task complexities across common driving activities. For instance, galvanic skin response and some heart rate derivatives were found to be correlated to overall subjective workload ratings. Furthermore, emotions were captured and showed to be affected by extreme driving situations.

The purpose of this paper is to bring together multiple literature sources which present innovative methodologies for the assessment of driver state, driving context and performance by means of technology within a vehicle and consumer electronic devices. It also provides an overview of ongoing research and trends in the area of driver state monitoring. As part of this review a model of a hybrid driver state monitoring system is proposed. The model incorporates technology within a vehicle and multiple brought-in devices for enhanced validity and reliability of recorded data. Additionally, the model draws upon requirement of data fusion in order to generate unified driver state indicator(-s) that could be used to modify in-vehicle information and safety systems hence, make them driver state adaptable. Such modification could help to reach optimal driving performance in a particular driving situation. To conclude, we discuss the advantages of integrating hybrid driver state monitoring system into a vehicle and suggest future areas of research.

This paper presents the results from a preliminary study where a wearable consumer electronic device was used to assess driver's state by capturing human physiological response in non-intrusive manner. Majority of state of the art studies have employed medical equipment drivers' state evaluation. Despite the potential gain in road safety this method of measuring physiology is unlikely to be accepted by private vehicle consumers due to its invasiveness, complexity, and high cost. This study was aiming to investigate possibility of employing a consumer grade wearable device to measure physiological parameters related to cognitive workload in real-time while driving i.e., drivers' heart rate. Furthermore, validity of captured heart activity metrics was analyzed to determine if wearable devices could be embedded into driving at its current technological state. The driving context was reproduced in desktop driving simulator, with 14 participants agreeing to take part in the study (μ = 28, σ = 8.5 years). Drivers were exposed to various road types, including pure Motorway, Rural, and Urban scenario modes. An accident was simulated in order to generate sudden cognitive arousal and capture participants' physiological response to the generated distress. It was found that a smartwatch is capable of reliable heart activity tracking in driving context. The results, supporting the relationship between cognitive workload level, generated by various complexity driving tasks, and Heart Rate Variability, were also presented.

Recently there has been a growth in the incorporation of autonomous features within vehicles. From being perceived as a comfort feature, autonomous features in vehicles have now become a safety feature which are foreseen to reduce accidents. This has led to a new trend within the automotive industry of focussing on autonomous features for driver safety, which might ultimately lead to fully autonomous vehicles. Considering the fact that most of the accidents on UK roads occur due to driver error, driver-less vehicles would prove to be a benefit. However with automation, an even greater challenge of system validation in all scenarios needs to be addressed. For this, various methods of validation have been developed by different research organizations and manufacturers, but a standardized process still evades the industry. Some of the existing methods have been discussed in this paper to critically compare their quality of results and ease of execution. Subsequently, a new test platform has been proposed using the 3xD driving simulator which encompasses most requirements of a general testing method. A standardized process which would benefit the industry both in terms of reducing costs of having varied processes, and by increasing customer confidence can be developed using a non-invasive platform like the 3xD driving simulator. The novelty of the 3xD simulator is the ability to drive-in any vehicle (production/prototype) and develop testing methodologies in an immersive wireless environment.

This paper gives an overview of the new Drive-in Driver-in-Loop simulator at WMG, University of Warwick, UK, which has been conceptualized to serve as a standard platform for virtual verification and validation of autonomous features. This front loading approach is key to the development of some of the upcoming technologies which have been lagging behind in terms of mass acceptance due to lack of proper simulation environment for testing. One of the key areas for the simulator is the study of driver acceptance of autonomous features in cars. Additionally, the simulator would help in the development of a validation methodology for autonomous systems keeping in mind the industry safety regulations and standards. The drive-in component on this scale adds to the novelty of the simulator, as it's a first of its kind. This enhances the challenge of making the communication interfaces of the simulator general, in order to communicate with any vehicle driving into the simulator. In order to achieve this, emphasis was laid on software architecture to enable modularity and re-configuration. A brief about various applications of the simulator has been provided in this paper.

A smart driving system (providing both safety and fuel-efficient driving advice in real time in the vehicle) was evaluated in real-world on-road driving trials to see if any measurable beneficial changes in driving performance would be observed. Forty participants drove an instrumented vehicle over a 50-min mixed-route driving scenario. Two conditions were adopted: one is a control with no smart driving feedback offered and the other is with advice being presented to the driver via a smartphone in the vehicle. Key findings from the study showed a 4.1% improvement in fuel efficiency when using the smart driving aid, importantly with no increase in journey time or reduction in average speed. Primarily, these efficiency savings were enabled by limiting the use of lower gears (facilitated by planning ahead to avoid unnecessary stops) and an increase in the use of the fifth gear (as advised by the in-vehicle system). Significant and important changes in driving safety behaviors were also observed, with an increase in mean headway to 2.3 s and an almost threefold reduction in time spent traveling closer than 1.5 s to the vehicle in front. This paper has shown that an in-vehicle smart driving system specifically developed and designed with the drivers' information requirements in mind can lead to significant improvements in driving behaviors in the real world on real roads with real users.