What would drivers like to know during automated driving? Information needs at different levels of automation (original) (raw)
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Applied Ergonomics
Partially automated vehicles present interface design challenges in ensuring the driver remains alert should the vehicle need to hand back control at short notice, but without exposing the driver to cognitive overload. To date, little is known about driver expectations of partial driving automation and whether this affects the information they require inside the vehicle. Twenty-five participants were presented with five partially automated driving events in a driving simulator. After each event, a semi-structured interview was conducted. The interview data was coded and analysed using grounded theory. From the results, two groupings of driver expectations were identified: High Information Preference (HIP) and Low Information Preference (LIP) drivers; between these two groups the information preferences differed. LIP drivers did not want detailed information about the vehicle presented to them, but the definition of partial automation means that this kind of information is required for safe use. Hence, the results suggest careful thought as to how information is presented to them is required in order for LIP drivers to safely using partial driving automation. Conversely, HIP drivers wanted detailed information about the system's status and driving and were found to be more willing to work with the partial automation and its current limitations. It was evident that the drivers' expectations of the partial automation capability differed, and this affected their information preferences. Hence this study suggests that HMI designers must account for these differing expectations and preferences to create a safe, usable system that works for everyone.
Behavioral Impact of Drivers' Roles in Automated Driving
Proceedings of the 8th International Conference on Automotive User Interfaces and Interactive Vehicular Applications - Automotive'UI 16, 2016
This study explores the effects of minor changes in automation level on drivers' engagement in secondary activities. Three levels of automation were tested: manual, semi-autonomous, and fully-autonomous. Potential distractor items were present and participants were instructed they could use them if they felt it was safe. Hand positions and engagement in secondary activities were manually coded. Participants were significantly less likely to engage in a secondary activity in semi-autonomous than fully-autonomous mode. Likewise, they were significantly less likely to use two hands to interact with a secondary activity in semi-autonomous mode than fully-autonomous mode. Gaze classification for each of the driver roles revealed that increasing levels of automation resulted in an increasing percentage of off-road glance durations. These observations suggest that in the event of automation failures, a driver in semi-autonomous driving may be in a somewhat better position to retake control and avoid collisions than during fully autonomous driving.
Highly Automated Driving, Secondary Task Performance, and Driver State
Human Factors: The Journal of the Human Factors and Ergonomics Society, 2012
This study examined the effect of changes in workload on performance in highly automated and manual driving. Variations in workload were also observed using blink measures. Results showed good driver response to incidents in the highly automated condition and some predictions in workload levels by blink frequency measures.
Engaging With Highly Automated Driving: To Be Or Not To Be In The Loop?
This desktop driving simulator study investigated the effect of engagement in a reading task during vehicle automation on drivers' ability to resume manual control and successfully avoid an impending collision with a stationary vehicle. To avoid collision, drivers were required to regain control of the automated vehicle and change lane. The decision-making element of this lane change was manipulated by asking drivers to move into the lane they saw fit (left or right) or to use the colour of the stationary vehicle as a rule (blue – left, red – right). Drivers' reaction to the stationary vehicle in manual control was compared to two automation conditions: (i) when drivers were engaged and observing the road during automation, and (ii) when they were reading a piece of text on an iPad during automation. Overall, findings suggest that drivers experiencing automation were slower to identify the potential collision scenario, but once identified, the collision was evaded more errat...
A proposed psychological model of driving automation
Theoretical Issues in Ergonomics Science, 2000
This paper considers psychological variables pertinent to driver automation. It is anticipated that driving with automated systems is likely to have a major impact on the drivers and a multiplicity of factors needs to be taken into account. A systems analysis of the driver, vehicle and automation served as the basis for eliciting psychological factors. The main variables to be considered were: feedback, locus of control, mental workload, driver stress, situational awareness and mental representations. It is expected that by anticipating the effects on the driver brought about by vehicle automation could lead to improved design strategies. Based on research evidence in the literature, the psychological factors were assembled into a model for further investigation.
Technical Report IMS / Department of Industrial and Material Science ;, 2020
The automotive industry is rapidly developing driving automation systems (DAS) with the aim of supporting drivers through automation of longitudinal and lateral vehicle control. As vehicle complexity increases, drivers' understanding of their responsibility and their vehicles' capabilities and limitations becomes significantly more important. In order to motivate manufacturers to adopt a human-centric perspective for the development of driving automation systems, the factors influencing the driver's perception during usage of such systems have to be understood. Therefore, the aim of this thesis is to contribute to the understanding of factors influencing user perception and understanding of driving automation systems in order to guide future design decisions from a human-centric perspective. The research for this thesis is organised into three empirical studies, embedding a mixedmethods research design. Study 1 aimed at investigating usage of DAS during different driving situations by facilitating an online survey. Studies 2 and 3 aimed to explore how drivers motivate their usage of driving automation systems, and which factors affect their understanding. Study 2 adopted an Explanatory Sequential Mixed Methods approach, consisting of a Naturalistic Driving Study and in-depth interviews to elicit knowledge about how users understand the DAS, and which factors influence usage. In Study 3 observations and interviews during an on-road driving session with a Wizard-of-Oz vehicle were conducted to gain insights into how users build an understanding of a vehicle with multiple levels of automation. The results show that the users of such systems, independent of the level of automation, talked about the systems by referring to different elements: the Context, the Vehicle, and the Driver. In addition, eleven recurring aspects describing the drivers' understanding of an automated system were discerned. Furthermore, six factors were identified that influence how drivers perceive driving automation during usage. The six factors are Preconceptions, Perceived Usefulness, Previous Experiences, Trust, System Performance, and Driving Behaviour of the Vehicle. Collectively, the identified aspects and factors constitute the building blocks of a process describing how drivers perceive driving automation systems and how this shapes their consequent understanding. The process is presented as a descriptive unified model. The main contribution of this thesis is twofold: unification of aspects found to shape a driver's understanding of a driving automation system, and the presentation of a unified descriptive model of the process showing how this understanding is shaped through what the driver perceives at the moment of use.
The Importance of Interruption Management for Usefulness and Acceptance of Automated Driving
Proceedings of the 9th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, 2017
To increase the safety in use of automated vehicles, Human Factors research has focused primarily on driver performance during takeover situations. However, surveys on public opinion on automated vehicles still report a lack of acceptance of the technology. In this review, we give an overview on how taking the changed role of the driver into account when designing Human-Machine Interfaces (HMI) of automated vehicles could increase the usefulness of the technology, which might in turn result in increased public acceptance. We propose that balancing the driver's need of being informed about the automated vehicle's status, actions and intentions with the desire to engage in non-driving related tasks (NDRTs) is likely to play an important role in this process.
2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops), 2021
Recent developments in advanced driving assistance systems (ADAS) that rely on some level of autonomy have led the automobile industry and research community to investigate the impact they might have on driving performance. However, most of the research performed so far is based on simulated environments. In this study we investigated the behavior of drivers in a vehicle with automated driving system (ADS) capabilities in a real life driving scenario. We analyzed their response to a take over request (TOR) at two different driving speeds while being engaged in non-driving-related tasks (NDRT). Results from the performed experiments showed that driver reaction time to a TOR, gaze behavior and self-reported trust in automation were affected by the type of NDRT being concurrently performed and driver reaction time and gaze behavior additionally depended on the driving or vehicle speed at the time of TOR.
Displaying System Situation Awareness Increases Driver Trust in Automated Driving
IEEE Transactions on Intelligent Vehicles, 2017
Self-driving systems are expected to become increasingly popular in the foreseeable future. However, a driver who is out of the control loop might reduce overall situation awareness by overly trusting automated driving systems. Alternatively, the introduction of automated driving systems could lead to misuse or disuse. For these reasons, an automated driving system should encourage appropriate driver reliance to achieve social acceptance. Imperfect information of the system sensing range might adversely affect trust. This study used a vibrotactile display with an automated driving system to provide situation awareness. The display contributes to driver trust by enabling a driver to predict or perceive actions selected by the system. The display provides spatial information related to traffic objects by haptic stimulus. The driving scenario of passing a motorbike with vehicles approaching from behind was considered. The results of this driving simulator study demonstrated that the spatial information and the behavior of the system affected trust.
The Effects of Continuous Driving-Related Feedback on Drivers’ Response to Automation Failures
Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 2017
During prolonged periods of autonomous driving, drivers tend to shift their attention away from the driving task. As a result, they require more time to regain awareness of the driving situation and to react to it. This study examined the use of informative automation that during Level-3 autonomous driving provided drivers with continuous feedback regarding the vehicle’s actions and surroundings. It was hypothesized that the operation of informative automation will trigger drivers to allocate more attention to the driving task and will improve their reaction times when resuming control of the vehicle. Sixteen participants drove manual and autonomous driving segments in a driving simulator equipped with Level-3 automation. For half of the participants, the informative automation issued alerts and messages while for the other half no messages were issued (control). The number of on-road glances served as a proxy for drivers’ attention. Drivers’ performance on handling an unexpected au...