How to present collision warnings at intersections?—A comparison of different approaches

Werneke, Julia; Vollrath, Mark

doi:10.1016/j.aap.2012.12.001

Cited by 63 publications

(25 citation statements)

References 30 publications

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“…In terms of perceived usefulness and satisfaction of the HMIs, there was no statistically significant difference between the carpet and arrow conditions, but drivers found both the carpet and the arrow more useful than the sphere. Previous research by Werneke and Vollrath [78] found that augmented feedback in the form of a bird's eye view received more positive ratings from drivers than a late warning in the form of a sphere that highlighted a dangerous vehicle. Schwarz and Fastenmeier [79] found that augmented reality warnings (i.e., scenario-specific icons accompanied by arrows coming from the direction of danger) were rated more highly than unspecific visual or auditory warnings.…”

Section: Subjective Datamentioning

confidence: 90%

Rolling Out the Red (and Green) Carpet: Supporting Driver Decision Making in Automation-to-Manual Transitions

Eriksson

Petermeijer

Zimmermann

et al. 2019

IEEE Trans. Human-Mach. Syst.

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This 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 acquisitionlow), 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.

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Section: Subjective Datamentioning

confidence: 90%

Rolling Out the Red (and Green) Carpet: Supporting Driver Decision Making in Automation-to-Manual Transitions

Eriksson

Petermeijer

Zimmermann

et al. 2019

IEEE Trans. Human-Mach. Syst.

View full text Add to dashboard Cite

show abstract

“…Various simulator based studies have shown that in-vehicle warning systems can have a positive impact on driving behaviour. In one example, early warning signals displayed while approaching an intersection showed a positive effect towards driving safer [63]. Participants adapted their driving behaviour by turning with a lower velocity after waiting longer at the intersection, and so avoided collisions.…”

Section: In-vehicle Warning Systemsmentioning

confidence: 99%

“…In addition, various research studies are geared towards how in-vehicle DSSs can encourage drivers to adapt their driving behaviour when necessary. Latest studies provide promising evidence that these systems can indeed have significant positive effects on driving behaviour and collision avoidance [23,57,63]. These systems can be delivered to vehicles through existing mobile or standalone satellite navigation systems [68].…”

Section: Introductionmentioning

confidence: 99%

Preventing traffic accidents with in-vehicle decision support systems - The impact of accident hotspot warnings on driver behaviour

Ryder

Gahr

Egolf

et al. 2017

Decision Support Systems

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A B S T R A C TDespite continuous investment in road and vehicle safety, as well as improvements in technology standards, the total amount of road traffic accidents has been increasing over the last decades. Consequently, identifying ways of effectively reducing the frequency and severity of traffic accidents is of utmost importance. In light of the depicted challenge, latest studies provide promising evidence that in-vehicle decision support systems (DSSs) can have significant positive effects on driving behaviour and collision avoidance. Going beyond existing research, we developed a comprehensive in-vehicle DSS, which provides accident hotspot warnings to drivers based on location analytics applied to a national historical accident dataset, composed of over 266,000 accidents. As such, we depict the design and field evaluation of an in-vehicle DSS, bridging the gap between real world location analytics and in-vehicle warnings. The system was tested in a country-wide field test of 57 professional drivers, with over 170,000km driven during a four-week period, where vehicle data were gathered via a connected car prototype system. Ultimately, we demonstrate that in-vehicle warnings of accident hotspots have a significant improvement on driver behaviour over time. In addition, we provide first evidence that an individual's personality plays a key role in the effectiveness of in-vehicle DSSs. However, in contrast to existing lab experiments with very promising results, we were unable to find an immediate effect on driver behaviour. Hence, we see a strong need for further field experiments with high resolution car data to confirm that in-vehicle DSSs can deliver in diverse field situations.

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“…Furthermore, with the knowledge that a potentially dangerous location is ahead, a semi-autonomous vehicle might drive in a more cautious mode to reduce risk or hand over control to the driver to transfer insurance liability. In addition, recent research studies of in-vehicle warning systems have shown that drivers themselves can be encouraged to adapt their driving behaviour at potentially dangerous locations (Kazazi et al, 2015;Ryder et al, 2017;Tey et al, 2011;Werneke and Vollrath, 2013). Ultimately, insurance companies can collaborate with road authorities to improve the road infrastructure that contribute to dangerous locations, and manufactures to better understand vehicle capabilities and advance safety focused offerings (Sheehan et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Spatial prediction of traffic accidents with critical driving events – Insights from a nationwide field study

Ryder

Dahlinger

Gahr

et al. 2019

Transportation Research Part A: Policy and Practice

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Despite the fact that semi-autonomous vehicles will become more and more prevalent in the coming decades, recent studies have highlighted that traffic accidents will persist as a core issue for road users, insurers, and policy makers alike. Researchers and industry players see potential in the technology embedded in semi-autonomous vehicles to combat this challenge by reliably predicting locations with a high likelihood of traffic accidents. This technology can be leveraged to detect accidents and 'near miss incidents', such as heavy braking and evasive manoeuvres, otherwise known as Critical Driving Events (CDEs). The locations of CDEs could identify areas of high accident exposure, offering automotive insurers a unique opportunity to reduce traffic accidents through the adoption of active loss prevention business models, such as providing saferouting services and in-vehicle warnings. To date, there is limited empirical evidence on whether the Crash Frequency and Crash Rate of locations can be accurately identified through CDEs. To address this research gap, an 18-week naturalistic driving field study of 72 vehicles was conducted in Switzerland, covering over 690,000 km. Data collected from the CAN Bus of these vehicles indicate that there is a proportional relationship between the CDEs of the fleet, and the Crash Frequency and Crash Rate of a location. Furthermore, a nationwide spatial regression analysis was applied to determine Crash Frequency across the majority of the Swiss road network. We identify the relationship between Crash Frequency, and the CDEs and Trip Frequency of the fleet, along with additional explanatory variables for urban and highway locations. These insights provide first evidence that insurance companies and other industry players with access to a nationwide semi-autonomous fleet can determine existing and emerging locations of high accident probability, enabling more proactive business models and safety focused services.

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How to present collision warnings at intersections?—A comparison of different approaches

Cited by 63 publications

References 30 publications

Rolling Out the Red (and Green) Carpet: Supporting Driver Decision Making in Automation-to-Manual Transitions

Rolling Out the Red (and Green) Carpet: Supporting Driver Decision Making in Automation-to-Manual Transitions

Preventing traffic accidents with in-vehicle decision support systems - The impact of accident hotspot warnings on driver behaviour

Spatial prediction of traffic accidents with critical driving events – Insights from a nationwide field study

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