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Abstract\u2014The physiological and cognitive load for cycling is high. A constant interaction with other road users, the environment, consisting of static and dynamic objects, is required while riding. In order to make cycling a pleasant and safe experience, it is of utmost importance to consider these interactions in providing adequate infrastructure and managing traffic accordingly. However, to do so successfully, the multiple layers of interaction, interdependencies and systemic relations have to be considered. This can hardly be done within a single domain. Thus, we propose an interdisciplinary mixed methods approach that facilitates domain-specific insights as well as an integrated, systemic understanding of cyclists\u2019 interaction with mobility space.<\/p>\n\n\n\n
Abstract\u2014 Implicit communication, such as driving dynamics, has been studied many times as a way to communicate vehicle or driver intention to pedestrians. It was found that “good” human driving behavior is sufficient to ensure a subjectively safe crossing, regardless of the vehicle’s automation status. The present study investigates in a video-based laboratory experiment to what extent such findings can also be transferred to more complex driving maneuvers, such as turning. The results show that even in this case, driving dynamics and distance remain the main determinants of crossing decisions. In ambiguous situations, however, the automation status can also play a role in the safety assessment. In order to be able to make general deductions for vehicle algorithms, more driving maneuvers need to be investigated and analyzed.<\/p>\n\n\n\n
Keywords: automated vehicles, implicit communication, vulnerable road users, turning maneuver
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Abstract\u2014For smooth and efficient vehicle-to-vehicle interactions in mixed traffic, comprising automated vehicles (AVs) and manually driven vehicles, AVs need to be able to anticipate future states of driving scenes and coordinate interactions with surrounding road users. Therefore, one promising approach is to investigate the communication behavior of manual drivers, which could serve as a basis in AVs\u2019 driving functions. Due to the high demand for coordinating and anticipating driving maneuvers in high-speed lane change scenarios and in low-speed shared space interaction scenarios, the behavior of human road users in these scenarios should be further analyzed. Therefore, driving cues that are applied for anticipating lane changes and manual drivers\u2019 gap acceptance for initiating turning actions were investigated in a combined driving simulator study where N = 29 participants contributed to. The results revealed that participants applied driving cues such as speed and position of surrounding vehicles (i.e., implicit driving cues) as a source of information to predict upcoming lane changes of other road users. Moreover, participants anticipated the development of driving scenes by considering the speed of encountering vehicles when selecting gaps for initiating turning maneuvers. This human driver behavior should therefore be considered for integration into AVs, as this enables an early anticipation of future states of driving scenes and adapting driving actions accordingly. Thus, such established interaction capabilities, based on implicit driving cues, can allow for intuitive encounters with surrounding road users in mixed traffic.<\/p>\n\n\n\n
Keywords: automated vehicles, implicit driving cues, lane change, gap acceptance, driving simulator study
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Abstract\u2014 The drivers\u2019 willingness to use vehicle automation depends on their evaluation of its instrumental and non-instrumental properties. Instrumental properties refer to the usability, utility, etc. of the automation. Non-instrumental properties include emotions, visual aesthetics, etc. This paper presents a study in which we investigated whether drivers use of vehicle automation in real traffic changes their evaluation of the automations non-\/instrumental properties. In a field study, thirty-eight participants completed a one-hour drive including rural roads and highways. Their user experience evaluations concerning vehicle automation were assessed before and after the drive. The results revealed that driver ratings of the instrumental qualities of the vehicle were higher after using it in real traffic compared to their expectations. No effects were found for non-instrumental qualities. However, driver ratings of usability, status, and positive emotions constantly predicted their intention to use the automated vehicle. Implications for automation design are discussed.<\/p>\n\n\n\n
Keywords: automated vehicles, user experience, intention to use, pre-post comparison, field test
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Abstract\u2014For smooth and successful interaction between drivers and (partly) automated vehicles (AVs), drivers need to be aware of the current status of the AV, know the limitations of the technology as well as have a correct understanding of possible actions (e.g., hand-over of the driving task to the automated system). Therefore, HMIs are needed that support drivers in the respective tasks and foster awareness of the AVs\u2019 status and possibilities. To reach the full potential of such supporting HMIs, drivers\u2019 acceptance of and, more importantly, trust in the HMI is important. This paper addresses the question how trust in a new developed system mediating between the driver and a (partly) AV (Mediator system) developed over time. The Mediator system was developed in the European project MEDIATOR. It is designed to coordinate between human drivers and autonomous vehicles based on current fitness levels to perform the driving task at hand by also including environmental factors (e.g., Mediator can detect an upcoming traffic jam or an incoming message and proposes to switch to automated driving).<\/p>\n\n\n\n
Keywords: automated vehicles, trust, HMI for (partly) automated driving, driving simulator study
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Abstract\u2014 Humans apply many cognitive processes to coordinate smoothly in complex traffic scenarios. While many engineering tasks from perception to the automation of driving have been successfully studied, the core cognitive task, however, remains to be tackled: How can a formally grounded and cognitively inspired representation of the way humans mentally simulate possibilities in specific traffic situations be performed? Based on insights from cognitive science and formal knowledge representation and reasoning, we outline some constraints and formal foundations for such a framework. Limitations are discussed.<\/p>\n\n\n\n
Keywords: mental simulation, cognitive modeling, virtual bargaining
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Abstract\u2014 Driver uncertainty can lead to dangerous lane change crashes. This paper aims to investigate driver uncertainty during lane change decisions under the influences of distance gap, time-to-collision (TTC) and closing velocity between a subject vehicle and an approaching vehicle. A driving simulator study was conducted with 29 participants, who had to decide if in a given traffic situation triggered by an acoustic signal, a lane change to the left would be safe or not. Subjective uncertainty scores and response actions for lane change decisions were recorded. The repeated measure logistic regression model that predicts lane change decisions shows that distance gap, TTC and closing velocity have significant effects on it. Moreover, when drivers are uncertain, the frequency of response actions for changing the lane is almost equal to the response actions for no lane change decisions.<\/p>\n\n\n\n
Keywords: driver uncertainty, lane change maneuver, decision-making<\/p>\n\n\n\n