1,721,065 research outputs found
Take-over time in highly automated vehicles: noncritical transitions to and from manual control
No full textObjective: the aim of this study was to review existing research into driver control transitions and to determine the time it takes drivers to resume control from a highly automated vehicle in non-critical scenarios. Background: contemporary research has moved from an inclusive design approach to only adhering to mean/median values when designing control transitions in automated driving. Research into control transitions in highly automated driving has focused on urgent scenarios where drivers are given a relatively short time span to respond to a request to resume manual control. We found a paucity in research into more frequent scenarios for control transitions, such as planned exits from highway systems.Method: twenty six drivers drove two scenarios with an automated driving feature activated. Drivers were asked to read a newspaper, or to monitor the system, and to relinquish, or resume, control from the automation when prompted by vehicle systems.Results: significantly longer control transition times were found between driving with and without secondary tasks. Control transition times were substantially longer than those reported in the peer-reviewed literature.Conclusion: we found that drivers take longer to resume control when under no time-pressure compared to that reported in the literature. Moreover, we found that drivers occupied by a secondary task exhibit larger variance, and slower responses to requests to resume control. Workload scores implied optimal workload.Application: intra- and inter-individual differences need to be accommodated by vehicle manufacturers and policy makes alike to ensure inclusive design of contemporary systems and safety during control transitions.<br/
Driver behaviour in highly automated driving: an evaluation of the effects of traffic, time pressure, cognitive performance and driver attitudes on decision-making time using a web based testing platform
No full textDriverless cars are a hot topic in today’s industry where several vehicle manufacturers try to create a reliable system for automated driving. The advantages of highly automated vehicles are many, safer roads and a lower environmental impact are some of the arguments for this technology. However, the notion of highly automated cars give rise to a large number of human factor issues regarding the safety and reliability of the automated system as well as concern about the driver’s role in the system.The purpose of this study was to explore the effects of systematic variations in traffic complexity and external time pressure on decision-making time in a simulated situation using a web-based testing platform. A secondary focus was to examine whether measures of cognitive performance and driver attitudes have an effect on decision-making time. The results show that systematic variations in both time pressure and traffic complexity have an effect on decision-making time. This indicates that drivers are able to adapt their decision-making to facilitate the requirements of a certain situation. The results also indicate that intelligence; speed of processing and driver attitudes has an effect on decision-making time
Driving performance after self-regulated control transitions in highly automated vehicles
No full textObjective: This study aims to explore whether driver-paced, non-critical transitions of control may counteract some of the after-effects observed in the contemporary literature, resulting in higher levels of vehicle control. Background: Research into control transitions in highly automated driving has focused on urgent scenarios where drivers are given a relatively short time span to respond to a request to resume manual control, resulting in seemingly scrambled control when manual control is resumed. Method: Twenty-six drivers drove two scenarios with an automated driving feature activated. Drivers were asked to read a newspaper, or to monitor the system, and to relinquish, or resume, control from the automation when prompted by vehicle systems. Driving performance in terms of lane-positioning, and steering behaviour was assessed for 20 seconds post resuming control to capture the resulting level of control.Results: It was found that lane-positioning was virtually unaffected for the duration of the 20-second time span in both automated conditions compared to the manual baseline when drivers resumed manual control, however significant increases in the standard deviation of steering input was found for both automated conditions compared to baseline. No significant differences were found between the two automated conditions.Conclusion: The results indicate that when drivers self-paced the transfer back to manual control they exhibit less of the detrimental effects observed in system-paced conditions.Application: It was shown that self-paced transitions could reduce the risk of accidents near the edge of the Operational Design Domain. Vehicle manufacturers must consider these benefits when designing contemporary systems.<br/
When communication breaks down or what was that? – the importance of communication for successful coordination in complex systems
No full textAutomation is increasingly prominent in today's vehicles. Initial systems will likely have some limitations, such as highway only automation. Thus, the designers such systems rely on the driver to resume control of the vehicle when the limits are reached. Such a system introduce similar problems that have been prominent in aviation, relying on the pilot to safely resume control. To resume control of the vehicle, collaboration and communication is key, as most system failures are associated with breakdowns in communication and “the single biggest problem in communication is the illusion that it has taken place” (- George Bernard Shaw). The paper outlook is from an Automated Driving perspective and AF447 serves as an illustrative example of the application of the explanatory capabilities of the Gricean Maxims in assessing Human-Agent communication in complex systems. Lastly, the paper discusses lessons learnt and potential application of the maxims in designing safe human-agent collaboration
The chatty co-driver: A linguistics approach applying lessons learnt from aviation incidents
No full textDrivers of contemporary vehicles are now able to relinquish control of the driving task to the vehicle, essentially allowing the driver to be completely hands and feet free. However, changes to legislation taking effect in 2016 will require the driver to be able to override the automated driving systems or switch them off completely. Initially this functionality is likely to be limited to certain areas, such as motorways. This creates a situation where the driver is expected to take control of the vehicle after being removed from the driving control-loop for extended periods of time, which places high demand on coordination between driver and automation. Resuming control after being removed from the control-loop have proven difficult in domains where automation is prevalent, such as aviation. Therefore the authors propose the Gricean Maxims of Successful Conversation as a means to identify, and mitigate flaws in Human-Automation-Interaction. As automated driving systems have yet to penetrate the market to a sufficient level to apply the Maxims, the authors applied the Maxims to two accidents in aviation. By applying the Maxims to the case studies from a Human-Automation-Interaction perspective, the authors were able to identify lacking feedback in different components of the pilot interface. By applying this knowledge to the driving domain, the authors argue that the Maxims could be used as a means to bridge the gulf of evaluation, by allowing the automation to act like a chatty co-driver, thereby increasing system transparency and reducing the effects of being out-of-the-loop
Rolling out the red (and green) carpet: Supporting driver decision making in automation-to-manual transitions
Full text linkThis paper assessed four types of human–machine interfaces (HMIs), classified according to the stages of automation proposed by Parasuraman et al. [“A model for types and levels of human interaction with automation,” IEEE Trans. Syst. Man, Cybern. A, Syst. Humans, vol. 30, no. 3, pp. 286–297, May 2000]. We hypothesized that drivers would implement decisions (lane changing or braking) faster and more correctly when receiving support at a higher automation stage during transitions from conditionally automated driving to manual driving. In total, 25 participants with a mean age of 25.7 years (range 19–36 years) drove four trials in a driving simulator, experiencing four HMIs having the following different stages of automation: baseline (information acquisition—low), sphere (information acquisition—high), carpet (information analysis), and arrow (decision selection), presented as visual overlays on the surroundings. The HMIs provided information during two scenarios, namely a lane change and a braking scenario. Results showed that the HMIs did not significantly affect the drivers’ initial reaction to the take-over request. Improvements were found, however, in the decision-making process: When drivers experienced the carpet or arrow interface, an improvement in correct decisions (i.e., to brake or change lane) occurred. It is concluded that visual HMIs can assist drivers in making a correct braking or lane change maneuver in a take-over scenario. Future research could be directed toward misuse, disuse, errors of omission, and errors of commission.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Human-Robot Interactio
A toolbox for automated driving on the STISIM driving simulator
No full textDriving simulators have been used since the beginning of the 1930s to assist researchers in assessing driver behaviour without putting the driver in harm's way. The current manuscript describes the implementation of a toolbox for automated driving research on the widely used STISIM platform. The toolbox presented in this manuscript allows researchers to conduct flexible research into automated driving, enabling independent use of longitudinal control, and a combination of longitudinal and lateral control, and is available as an open source download through GitHub. The toolbox allows the driver to adjust parameters such as set speed (in 5 kph increments) and time-headway (in steps of 1, 1.5, and 2 s) as well as automation mode dynamically, while logging additional variabless that STISIM does not provide out-of-the-box (time-headway, time to collision). Moreover, the toolbox presented in this manuscript has gone through validation trials showing accurate speed, time-headway, and lane tracking, as well as transitions of control between manual and automated driving. • A toolbox was developed for STISIM driving simulators.• The toolbox allows for automated driving.• Functionality includes tracking of speed, headway, and lane.</p
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Transition to manual: comparing simulator with on-road control transitions
No full textBackground: Whilst previous research has explored how driver behaviour in simulators may transfer to the open road, there has been relatively little research showing the same transfer within the field of driving automation. As a consequence, most research into human-automation interaction has primarily been carried out in a research laboratory or on closed-circuit test tracks.Objective: The aim of this study was to assess whether research into non-critical control transactions in highly automated vehicles performed in driving simulators correlate with road driving conditions.Method: Twenty six drivers drove a highway scenario using an automated driving mode in the simulator and twelve drivers drove on a public motorway in a Tesla Model S with the Autopilot activated. Drivers were asked to relinquish, or resume control from the automation when prompted by the vehicle interface in both the simulator and on road condition.Results: Drivers were generally faster to resume control in the on-road driving condition. However, strong positive correlations were found between the simulator and on road driving conditions for drivers transferring control to and from automation. No significant differences were found with regard to workload, perceived usefulness and satisfaction between the simulator and on-road drives.Conclusion: The results indicate high levels of relative validity of driving simulators as a research tool for automated driving research.<br/
- …
