Deceleration parameters as implicit communication signals for pedestrians' crossing decisions and estimations of automated vehicle behaviour.

Society greatly expects the widespread deployment of automated vehicles (AVs). However, the absence of a driver role results in unresolved communication issues between pedestrians and AVs. Research has shown the crucial role of implicit communication signals in this context. Nonetheless, it remains unclear how pedestrians subjectively estimate vehicle behaviour and whether they incorporate these estimations as part of their crossing decisions. For the first time, this study explores the impact of implicit communication signals on pedestrians' subjective estimations of approaching vehicle behaviour across a wide range of experimental traffic scenarios and on their crossing decisions in the same scenarios through a comprehensive analysis. Two simulator tasks, namely a natural road crossing task and a vehicle behaviour estimation task, were designed with controlled time to collision, vehicle speed, and deceleration behaviour. A novel finding is that the correlation between crossing decisions and vehicle behaviour estimations depends on the traffic scenario. Pedestrians' recognition of different deceleration behaviour aligned with their crossing decisions, supporting the notion that they actively estimate vehicle behaviour as part of their decision-making process. However, if the traffic gap was long enough, the effects of vehicle speed were the opposite between crossing decisions and estimations, suggesting that vehicle behaviour estimation may not directly impact crossing decisions when the time gap to the vehicle is large. We also found that pedestrians crossed the street earlier and estimated yielding behaviour more accurately in early-onset braking scenarios than in late-onset braking scenarios. Interestingly, vehicle speed significantly affected pedestrians' estimations, with pedestrians tending to perceive low vehicle speed as yielding behaviour regardless of whether the vehicle yielded. Finally, we demonstrated that visual cue τ̇ is a practical indicator for controlling the vehicle deceleration evidence in the experiment. In conclusion, these findings reveal in detail the role of deceleration parameters as implicit communication signals between pedestrians and AVs, with implications for road crossing safety and the development of AVs.

Drivers' Evaluation of Different Automated Driving Styles: Is It Both Comfortable and Natural?

OBJECTIVE: This study investigated users' subjective evaluation of three highly automated driving styles, in terms of comfort and naturalness, when negotiating a UK road in a high-fidelity, motion-based, driving simulator. BACKGROUND: Comfort and naturalness play an important role in contributing to users' acceptance and trust of automated vehicles (AVs), although not much is understood about the types of driving style which are considered comfortable or natural. METHOD: A driving simulator study, simulating roads with different road geometries and speed limits, was conducted. Twenty-four participants experienced three highly automated driving styles, two of which were recordings from human drivers, and the other was based on a machine learning (ML) algorithm, termed Defensive, Aggressive, and Turner, respectively. Participants evaluated comfort or naturalness of each driving style, for each road segment, and completed a Sensation Seeking questionnaire, which assessed their risk-taking propensity. RESULTS: Participants regarded both human-like driving styles as more comfortable and natural, compared with the less human-like, ML-based, driving controller. Particularly, between the two human-like controllers, the Defensive style was considered more comfortable, especially for the more challenging road environments. Differences in preference for controller by driver trait were also observed, with the Aggressive driving style evaluated as more natural by the high sensation seekers. CONCLUSION: Participants were able to distinguish between human- and machine-like AV controllers. A range of psychological concepts must be considered for the subjective evaluation of controllers. APPLICATION: Insights into how different driver groups evaluate automated vehicle controllers are important in designing more acceptable systems.

Impacts of visual and cognitive distractions and time pressure on pedestrian crossing behaviour: A simulator study.

Distractions have been recognised as one important factor associated with pedestrian injuries, as the increasing use of cell phones and personal devices. However, the situation is less clear regarding the differences in the effects of visual-manual and auditory-cognitive distractions. Here, we investigated distracted pedestrians in a one-lane road with continuous traffic using an immersive CAVE-based simulator. Sixty participants were recruited to complete a crossing task and perform one of two distractions, a visual-manual task and an auditory-cognitive task. Moreover, normal and time pressure crossing conditions were included as a baseline and comparison. For the first time, this study directly compared the impacts of visual-manual, auditory-cognitive distractions, and time pressure on pedestrian crossing behaviour and safety in a controlled environment. The results indicated that although pedestrian safety was compromised under both types of distraction, the effects of the applied distractions were different. When engaged in the visual-manual distraction, participants crossed the road slowly, but there was no significant difference in gap acceptance or initiation time compared to baseline. In contrast, participants walked slowly, crossed earlier, and accepted smaller gaps when performing the auditory-cognitive distraction. This has interesting parallels to existing findings on how these two types of distractions affect driver performance. Moreover, the effects of the visual-manual distraction were found to be dynamic, as these effects were affected by the gap size. Finally, compared to baseline, time pressure resulted in participants accepting smaller time gaps with shorter initiation times and crossing durations, leading to an increase in unsafe decisions and a decrease in near-collisions. These results provide new evidence that two types of distraction and time pressure impair pedestrian safety, but in different ways. Our findings may provide insights for further studies involving pedestrians with different distraction components.

The effect of information from dash-based human-machine interfaces on drivers' gaze patterns and lane-change manoeuvres after conditionally automated driving.

The goal of this paper was to measure the effect of Human-Machine Interface (HMI) information and guidance on drivers' gaze and takeover behaviour during transitions of control from automation. The motivation for this study came from a gap in the literature, where previous research reports improved performance of drivers' takeover based on HMI information, without considering its effect on drivers' visual attention distribution, and how drivers also use the information available in the environment to guide their response. This driving simulator study investigated drivers' lane-changing behaviour after resumption of control from automation. Different levels of information were provided on a dash-based HMI, prior to each lane change, to investigate how drivers distribute their attention between the surrounding environment and the HMI. The difficulty of the lane change was also manipulated by controlling the position of approaching vehicles in drivers' offside lane. Results indicated that drivers' decision-making time was sensitive to the presence of nearby vehicles in the offside lane, but not directly influenced by the information on the HMI. In terms of gaze behaviour, the closer the position of vehicles in the offside lane, the longer drivers looked in that direction. Drivers looked more at the HMI, and less towards the road centre, when the HMI presented information about automation status, and included an advisory message indicating it was safe to change lane. Machine learning techniques showed a strong relationship between drivers' gaze to the information presented on the HMI, and decision-making time (DMT). These results contribute to our understanding of HMI design for automated vehicles, by demonstrating the attentional costs of an overly-informative HMI, and that drivers still rely on environmental information to perform a lane-change, even when the same information can be acquired by the HMI of the vehicle.

Learning to interpret novel eHMI: The effect of vehicle kinematics and eHMI familiarity on pedestrian' crossing behavior.

INTRODUCTION: In current urban traffic, pedestrians attempting to cross the road at un-signalized locations are thought to mostly use implicit communication, such as deceleration cues, to interpret a vehicle's intention to yield. There is less reliance on explicit driver- or vehicle-based messages, such as hand/head movements, or flashing lights/beeping horns. With the impending deployment of Automated Vehicles (AV), especially those at SAE Level 4 and 5, where the driver is no longer in control of the vehicle, there has been a surge in interest in the value of new forms of communication for AVs, for example, via different types of external Human Machine Interfaces (eHMIs). However, there is still much to be understood about how quickly a novel eHMI affects pedestrian crossing decisions, and whether it provides any additional aid, above and beyond implicit/kinematic information from the vehicle. The aim of this between-participant study, funded by the H2020 interACT project, was to investigate how the combination of kinematic information from a vehicle (e.g., Speed and Deceleration), and eHMI designs, play a role in assisting the crossing decision of pedestrians in a cave-based pedestrian simulator. METHOD: Using an existing, well-recognized, message for yielding (Flashing Headlights - FH) as a benchmark, this study also investigated how quickly a novel eHMI (Slow Pulsing Light Band - SPLB) was learned. To investigate the effect of eHMI visibility on crossing decisions, the distance at which each eHMI was perceivable was also measured. RESULTS: Results showed that, compared to SPLB, the FH led to earlier crossings during vehicle deceleration, especially at lower approaching speeds, and smaller time gaps. However, although FH was visible earlier than SPLB, this visibility does not appear to be the only reason for earlier crossings, with message familiarity thought to play a role. Participants were found to learn the meaning conveyed by FH relatively quickly, crossing around 1 second earlier in its presence (compared to the no eHMI condition), across the three blocks of trials. On the other hand, it took participants at least one block of 12 trials for the new SPLB signal to affect crossing, which only accelerated crossing initiations by around 200 ms, compared to the no eHMI condition. The role of comprehension, long-term exposure, and familiarity of novel messages in this context is therefore important, if AVs are to provide safe, trustworthy communication messages, which will enhance traffic flow and efficiency.

The effect of inconsistent steering guidance during transitions from Highly Automated Driving.

This driving simulator study investigated the effect of inconsistent steering guidance during system and user-initiated transitions from Highly Automated Driving (HAD). In particular, the aim of the study was to understand if steering conflicts could be achieved by adopting inconsistent steering guidance and whether these conflicts could be exploited to accelerate drivers' steering engagement within a limited time. Inconsistent steering guidance was generated by switching the guidance on and off at 3 different frequencies (0.1, 0.2 and 0.3 Hz). Results revealed that steering engagement has more to do with the initiation rather than the quality of the steering guidance. In fact, drivers were more engaged with the steering task when they initiated the transition themselves. Compared to system-initiated transitions, in user-initiated ones, drivers exerted stronger steering inputs throughout the transition, which allowed them to maintain larger Time To Lane Crossing (TTLC) values with fewer steering corrections. During system-initiated transitions, drivers started to actively engage with the steering activity only after more than 5 s from the start of the transition but were able to achieve a steering behaviour close to the one shown during user-initiated transitions at 10 s.

Transitions Between Highly Automated and Longitudinally Assisted Driving: The Role of the Initiator in the Fight for Authority.

OBJECTIVE: A driving simulator study explored how drivers behaved depending on their initial role during transitions between highly automated driving (HAD) and longitudinally assisted driving (via adaptive cruise control). BACKGROUND: During HAD, drivers might issue a take-over request (TOR), initiating a transition of control that was not planned. Understanding how drivers behave in this situation and, ultimately, the implications on road safety is of paramount importance. METHOD: Sixteen participants were recruited for this study and performed transitions of control between HAD and longitudinally assisted driving in a driving simulator. While comparing how drivers behaved depending on whether or not they were the initiators, different handover strategies were presented to analyze how drivers adapted to variations in the authority level they were granted at various stages of the transitions. RESULTS: Whenever they initiated the transition, drivers were more engaged with the driving task and less prone to follow the guidance of the proposed strategies. Moreover, initiating a transition and having the highest authority share during the handover made the drivers more engaged with the driving task and attentive toward the road. CONCLUSION: Handover strategies that retained a larger authority share were more effective whenever the automation initiated the transition. Under driver-initiated transitions, reducing drivers' authority was detrimental for both performance and comfort. APPLICATION: As the operational design domain of automated vehicles (Society of Automotive Engineers [SAE] Level 3/4) expands, the drivers might very well fight boredom by taking over spontaneously, introducing safety issues so far not considered but nevertheless very important.

Doctor when can I drive? Braking response after knee arthroplasty: A systematic review & meta-analysis of brake reaction time.

BACKGROUND: There is limited evidence available on return to driving for patients undergoing knee arthroplasty (KA). Primarily, surgeons have used brake reaction time as a surrogate measure of safe return to driving. The purpose of this study was to review existing literature and provide guidance on the recovery of braking performance following knee arthroplasty. METHODS: A literature search was performed for prospective studies on driving after KA. Two reviewers screened citations for inclusion, assessed methodological quality, and extracted data. Values for total brake reaction-time (TBRT), movement-time (MT), reaction-time (RT), and braking-force (BF) were included for meta-analysis. RESULTS: Twelve studies with 368 subjects were identified. TBRT, RT, MT, and BF relative to preoperative baseline were assessed. Meta-analysis of TBRT showed a significant improvement from preoperative baseline at 3 months & 1 year (p = 0.003 & p = 0.0001). MT showed a significant increase at 2 and 4 weeks (p = 0.00001 & p = 0.03) before returning to no being significantly different at 6 weeks and beyond. In contrast RTs were noted to improve significantly 2 weeks (p = 0.006), 4 weeks (p = 0.03), and 1 year (p = 0.0002). CONCLUSION: Meta-analysis showed no significant difference in TBRT up until 3 months. RT increased significantly post-operatively suggesting it is not a reliable indicator. MT in contrast was significantly decreased post KA and may represent a more reliable measure of braking performance post KA. Surgeons should consider these recommendations and other patient factors that determine fitness to drive prior to advising their patients on a safe to return to driving.

Predicting takeover response to silent automated vehicle failures.

Current and foreseeable automated vehicles are not able to respond appropriately in all circumstances and require human monitoring. An experimental examination of steering automation failure shows that response latency, variability and corrective manoeuvring systematically depend on failure severity and the cognitive load of the driver. The results are formalised into a probabilistic predictive model of response latencies that accounts for failure severity, cognitive load and variability within and between drivers. The model predicts high rates of unsafe outcomes in plausible automation failure scenarios. These findings underline that understanding variability in failure responses is crucial for understanding outcomes in automation failures.

Does the use of a head mounted display increase the success of risk awareness and perception training (RAPT) for drivers?

The PC-based driver training programme, Risk Awareness and Perception Training (RAPT) has been successful in improving young drivers' hazard anticipation and mitigation responses in both simulator and on-road studies. The current research aimed to evaluate the success of an adaptation of this training for the UK context, along with investigating the impact of the presentation modality on RAPT effectiveness. Traditionally RAPT has been delivered on a PC monitor, which does not allow the same range of head and eye movements that drivers use when on the road. Thus, it was anticipated that the 360° field-of-view provided by Head Mounted Display (HMD) technology would provide a more ecologically valid experience, facilitating deeper processing and encoding of driving relevant scanning patterns, and an increased capacity to identify potentially hazardous areas of a driving scenario. Using a between-subjects design, three different training modalities were compared - a PC-based version using still images (PC-Stills), a HMD version using still images (HMD-Stills), and a HMD version using videos (HMD-video). All three training groups' performance on the UK Hazard Perception test was compared to that of a control group, who received no training. Results indicated that the adaptation of the training materials for the UK context was successful, with all three training programmes leading to performance improvements in the RAPT tests. Although participants in the HMD-video condition required more attempts to pass the training, this group showed the greatest improvement in hazard perception scores from the pre- to the post-training tests. Results also showed scenario-based differences between the modalities, suggesting that the success of different versions of RAPT may be linked to the type of risky scenario being targeted.

ActEarly: a City Collaboratory approach to early promotion of good health and wellbeing.

Economic, physical, built, cultural, learning, social and service environments have a profound effect on lifelong health. However, policy thinking about health research is dominated by the 'biomedical model' which promotes medicalisation and an emphasis on diagnosis and treatment at the expense of prevention. Prevention research has tended to focus on 'downstream' interventions that rely on individual behaviour change, frequently increasing inequalities. Preventive strategies often focus on isolated leverage points and are scattered across different settings. This paper describes a major new prevention research programme that aims to create City Collaboratory testbeds to support the identification, implementation and evaluation of upstream interventions within a whole system city setting. Prevention of physical and mental ill-health will come from the cumulative effect of multiple system-wide interventions. Rather than scatter these interventions across many settings and evaluate single outcomes, we will test their collective impact across multiple outcomes with the goal of achieving a tipping point for better health. Our focus is on early life (ActEarly) in recognition of childhood and adolescence being such critical periods for influencing lifelong health and wellbeing.

Sustained sensorimotor control as intermittent decisions about prediction errors: computational framework and application to ground vehicle steering.

A conceptual and computational framework is proposed for modelling of human sensorimotor control and is exemplified for the sensorimotor task of steering a car. The framework emphasises control intermittency and extends on existing models by suggesting that the nervous system implements intermittent control using a combination of (1) motor primitives, (2) prediction of sensory outcomes of motor actions, and (3) evidence accumulation of prediction errors. It is shown that approximate but useful sensory predictions in the intermittent control context can be constructed without detailed forward models, as a superposition of simple prediction primitives, resembling neurobiologically observed corollary discharges. The proposed mathematical framework allows straightforward extension to intermittent behaviour from existing one-dimensional continuous models in the linear control and ecological psychology traditions. Empirical data from a driving simulator are used in model-fitting analyses to test some of the framework's main theoretical predictions: it is shown that human steering control, in routine lane-keeping and in a demanding near-limit task, is better described as a sequence of discrete stepwise control adjustments, than as continuous control. Results on the possible roles of sensory prediction in control adjustment amplitudes, and of evidence accumulation mechanisms in control onset timing, show trends that match the theoretical predictions; these warrant further investigation. The results for the accumulation-based model align with other recent literature, in a possibly converging case against the type of threshold mechanisms that are often assumed in existing models of intermittent control.