One major challenge for automated cars is to not only be safe, but also secure. Indeed, connected vehicles are vulnerable to cyberattacks, which may jeopardize individuals' trust in these vehicles ...and their safety. In a driving simulator experiment, 38 participants were exposed to two screen failures:
(i.e., no turn signals on the in-vehicle screen and instrument cluster) and
(i.e., ransomware attack), both while performing a non-driving related task (NDRT) in a conditionally automated vehicle. Results showed that objective trust decreased after experiencing the failures. Drivers took over control of the vehicle and stopped their NDRT more often after the explicit failure than after the silent failure. Lateral control of the vehicle was compromised when taking over control after both failures compared to automated driving performance. However, longitudinal control proved to be smoother in terms of speed homogeneity compared to automated driving performance. These findings suggest that connectivity failures negatively affect trust in automation and manual driving performance after taking over control. This research posits the question of the importance of connectivity in the realm of trust in automation. Finally, we argue that engagement in a NDRT while riding in automated mode is an indicator of trust in the system and could be used as a surrogate measure for trust.
•Drivers used an automated car and engaged in a word search task.•They experienced silent (no turn signal) and explicit (ransomware) screen failures.•Nearly half of drivers looked at the ransomware ...for more than 12 s while driving.•The ransomware attack was distractive and posed significant risks to road safety.•After the ransomware, one driver who resumed manual control crashed the vehicle.
Connected and automated vehicles are vulnerable to cyber-attacks, which may jeopardise their safe and efficient operation and, as a result, negatively affect drivers’ behaviour. A major concern for such cyber-attacks is visual distraction inside the vehicle, which is one of the main causes of road accidents. In this empirical research using a driving simulator, 38 participants drove in a conditionally automated vehicle and experienced two types of failure: explicit (i.e., ransomware attack appearing on the in-vehicle screen) and silent (i.e., turn signals failed to activate on the in-vehicle screen and instrument cluster), while engaged in a non-driving related task. Drivers’ gaze behaviour, in terms of number and duration of fixation, were collected and analysed. Results showed that the HMI where the ransomware was displayed was the area of interest drivers looked at the most. The majority of drivers failed to notice that the turn signal was faulty. Nearly half of drivers looked at the ransomware for more than 12 s while driving. No effect on the timing of failure on gaze behaviour was observed. This research evidenced that ransomware attacks are distractive and pose significant risks to road safety – with one participant crashing the vehicle after resuming manual control. Data also evidenced that such connected vehicles are unlikely to meet NHTSA’s distraction guidelines for safe use of in-vehicle devices.
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.
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.
Using fNIRS to Verify Trust in Highly Automated Driving Perello-March, Jaume R.; Burns, Christopher G.; Woodman, Roger ...
IEEE transactions on intelligent transportation systems,
01/2023, Letnik:
24, Številka:
1
Journal Article
Recenzirano
Odprti dostop
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.
Real-time monitoring of drivers' functional states will soon become a required safety feature for commercially available vehicles with automated driving capability. Automated driving technology aims ...to mitigate human error from road transport with the progressive automatisation of specific driving tasks. However, while control of the driving task remains shared between humans and automated systems, the inclusion of this new technology is not exempt from other human factors-related challenges. Drivers' functional states are essentially a combination of psychological, emotional, and cognitive states, and they generate a constant activity footprint available for measurement through neural and peripheral physiology, among other measures. These factors can determine drivers' functional states and, thus, drivers' availability to safely perform control transitions between human and vehicle. This doctoral project aims at investigating the potential of electrocardiogram (ECG), electrodermal activity (EDA) and functional near-infrared spectroscopy (fNIRS) as measures for a multimodal driver state monitoring (DSM) system for highly automated driving (i.e., SAE levels 3 and 4). While current DSM systems relying on gaze behaviour measures have proven valid and effective, several limitations and challenges could only be overcome using eye-tracking in tandem with physiological parameters. This thesis investigates whether ECG, EDA and fNIRS would be good candidates for such a purpose. Two driving simulator studies were performed to measure mental workload, trust in automation, stress and perceived risk, all identified as modulators of drivers' functional states and that could eventually determine drivers' availability to take-over manual control. The main findings demonstrate that DSM systems should adopt multiple physiological measures to capture changes in functional states relevant for driver readiness. Future DSM systems will benefit from the knowledge generated by this research by applying machine learning methods to these measures for determining drivers' availability for optimal take-over performance.
Objective
Using brain haemodynamic responses to measure perceived risk from traffic complexity during automated driving.
Background
Although well-established during manual driving, the effects of ...driver risk perception during automated driving remain unknown. The use of fNIRS in this paper for assessing drivers’ states posits it could become a novel method for measuring risk perception.
Methods
Twenty-three volunteers participated in an empirical driving simulator experiment with automated driving capability. Driving conditions involved suburban and urban scenarios with varying levels of traffic complexity, culminating in an unexpected hazardous event. Perceived risk was measured via fNIRS within the prefrontal cortical haemoglobin oxygenation and from self-reports.
Results
Prefrontal cortical haemoglobin oxygenation levels significantly increased, following self-reported perceived risk and traffic complexity, particularly during the hazardous scenario.
Conclusion
This paper has demonstrated that fNIRS is a valuable research tool for measuring variations in perceived risk from traffic complexity during highly automated driving. Even though the responsibility over the driving task is delegated to the automated system and dispositional trust is high, drivers perceive moderate risk when traffic complexity builds up gradually, reflected in a corresponding significant increase in blood oxygenation levels, with both subjective (self-reports) and objective (fNIRS) increasing further during the hazardous scenario.
Application
Little is known regarding the effects of drivers’ risk perception with automated driving. Building upon our experimental findings, future work can use fNIRS to investigate the mental processes for risk assessment and the effects of perceived risk on driving behaviours to promote the safe adoption of automated driving technology.
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.
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