Dataset Information

Communicating Intent of Automated Vehicles to Pedestrians.

ABSTRACT: While traffic signals, signs, and road markings provide explicit guidelines for those operating in and around the roadways, some decisions, such as determinations of "who will go first," are made by implicit negotiations between road users. In such situations, pedestrians are today often dependent on cues in drivers' behavior such as eye contact, postures, and gestures. With the introduction of more automated functions and the transfer of control from the driver to the vehicle, pedestrians cannot rely on such non-verbal cues anymore. To study how the interaction between pedestrians and automated vehicles (AVs) might look like in the future, and how this might be affected if AVs were to communicate their intent to pedestrians, we designed an external vehicle interface called automated vehicle interaction principle (AVIP) that communicates vehicles' mode and intent to pedestrians. The interaction was explored in two experiments using a Wizard of Oz approach to simulate automated driving. The first experiment was carried out at a zebra crossing and involved nine pedestrians. While it focused mainly on assessing the usability of the interface, it also revealed initial indications related to pedestrians' emotions and perceived safety when encountering an AV with/without the interface. The second experiment was carried out in a parking lot and involved 24 pedestrians, which enabled a more detailed assessment of pedestrians' perceived safety when encountering an AV, both with and without the interface. For comparison purposes, these pedestrians also encountered a conventional vehicle. After a short training course, the interface was deemed easy for the pedestrians to interpret. The pedestrians stated that they felt significantly less safe when they encountered the AV without the interface, compared to the conventional vehicle and the AV with the interface. This suggests that the interface could contribute to a positive experience and improved perceived safety in pedestrian encounters with AVs - something that might be important for general acceptance of AVs. As such, this topic should be further investigated in future studies involving a larger sample and more dynamic conditions.

SUBMITTER: Habibovic A

PROVIDER: S-EPMC6090516 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Communicating Intent of Automated Vehicles to Pedestrians.

Habibovic Azra A Lundgren Victor Malmsten VM Andersson Jonas J Klingegård Maria M Lagström Tobias T Sirkka Anna A Fagerlönn Johan J Edgren Claes C Fredriksson Rikard R Krupenia Stas S Saluäär Dennis D Larsson Pontus P

Frontiers in psychology 20180807

While traffic signals, signs, and road markings provide explicit guidelines for those operating in and around the roadways, some decisions, such as determinations of "who will go first," are made by implicit negotiations between road users. In such situations, pedestrians are today often dependent on cues in drivers' behavior such as eye contact, postures, and gestures. With the introduction of more automated functions and the transfer of control from the driver to the vehicle, pedestrians canno ...[more]

PMID: 30131737

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Project description:Autonomous Vehicles (AV) technology is emerging. Field tests on public roads have been on going in several states in the US as well as in Europe and Asia. During the US public road tests, crashes with AV involved happened, which becomes a concern to the public. Most previous studies on AV safety relied heavily on assessing drivers' performance and behaviors in a simulation environment and developing automated driving system performance in a closed field environment. However, contributing factors and the mechanism of AV-related crashes have not been comprehensively and quantitatively investigated due to the lack of field AV crash data. By harnessing California's Report of Traffic Collision Involving an Autonomous Vehicle Database, which includes the AV crash data from 2014 to 2018, this paper investigates by far the most current and complete AV crash database in the US using statistical modeling approaches that involve both ordinal logistic regression and CART classification tree. The quantitative analysis based on ordinal logistic regression and CART models has successfully explored the mechanism of AV-related crash, via both perspectives of crash severity and collision types. Particularly, the CART model reveals and visualize the hierarchical structure of the AV crash mechanism with knowledge of how these traffic, roadway, and environmental contributing factors can lead to crashes of various serveries and collision types. Statistical analysis results indicate that crash severity significantly increases if the AV is responsible for the crash. The highway is identified as the location where severe injuries are likely to happen. AV collision types are affected by whether the vehicle is on automated driving mode, whether the crashes involve pedestrians/cyclists, as well as the roadway environment. The method used in this research provides a proven approach to statistically analyze and understand AV safety issues. And this benefit is potential be even enhanced with an increasing sample size of AV-related crashes records in the future. The comprehensive knowledge obtained ultimately facilitates assessing and improving safety performance of automated vehicles.

Dataset Information

Communicating Intent of Automated Vehicles to Pedestrians.

Publications

Communicating Intent of Automated Vehicles to Pedestrians.

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