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In order to facilitate safe interactions between automated vehicles (AVs) and vulnerable road users (VRUs) such as bicyclists, we present a communication application for mobile devices that allows an AV or its passenger and a bicyclist to interact in certain traffic scenarios. At the intersection, the AV or its passenger can change the existing rig...
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... to know how helpful or practical the users rate the application before deploying the design concept in the real world. Also Fig. 8. Clarity of instructions with and without application usage for rightof-way decision at study scenarios important is, as mentioned earlier, the trust in the instructions and the behaviour of other road users. In Fig. 9, these four issues are addressed. The result of the helpfulness (Bicycle: 3.6 points -AV 3.7 points) and practicability (Bicycle: 3.9 points -AV 3.6 points) evaluation tends to be positive. A slightly increased value can be noticed in the practicability for the bicyclist. The question of trust is split into two aspects: trust in the ...
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Citations
... Another field of research, in which similar a co-simulation frameworks are used or could benefit of, is studying interaction of various road user groups, such as pedestrians, cyclists and manually driven or automated vehicles (AVs). For instance, Lindner, Grigoropoulos, Keler, et al. [18] and Lindner, Keler, Grigoropoulos, et al. [19] investigates the interaction of cyclists and an autonomous vehicle using smartphone-based Human-Machine-Interface (HMI) in urban traffic using a coupled bicycle-AV simulator. With a car driving simulator, Denk, Himmels, Andreev, et al. [20] investigate the interaction of human drivers and cyclists in the safety-critical right turn situation. ...
This paper presents "Sumonity," an interface that bridges SUMO (Simulation of Urban MObility) and Unity, combining SUMO's robust traffic modeling capabilities with Unity's advanced graphical and physical engine, enhancing realism in traffic simulations. The study explores Sumonity's development and implementation, showcasing its capabilities. The interface offers a significant improvement in simulation fidelity by adopting a pure pursuit control approach within Unity for simulating each traffic agent. This methodological shift allows for more granular control over individual vehicle behaviors, aligning with autonomous and common vehicle dynamics. The paper also discusses the broader implications of Sumonity for future research in this field.
... Another field of research, in which similar a co-simulation frameworks are used or could benefit of, is studying interaction of various road user groups, such as pedestrians, cyclists and manually driven or automated vehicles (AVs). For instance, Lindner et al. [18], [19] investigates the interaction of cyclists and an autonomous vehicle using smartphone-based Human-Machine-Interface (HMI) in urban traffic using a coupled bicycle-AV simulator. With a car driving simulator, Denk et al. [20] investigate the interaction of human drivers and cyclists in the safety-critical right turn situation. ...
This paper presents "Sumonity," an interface that bridges SUMO (Simulation of Urban MObility) and Unity, combining SUMO's robust traffic modeling capabilities with Unity's advanced graphical and physical engine, enhancing realism in traffic simulations. The study explores Sumonity's development and implementation, showcasing its capabilities. The interface offers a significant improvement in simulation fidelity by adopting a pure pursuit control approach within Unity for simulating each traffic agent. This methodological shift allows for more granular control over individual vehicle behaviors , aligning with autonomous and common vehicle dynamics. The paper also discusses the broader implications of Sumonity for future research in this field.
... Such simulation experimental designs are usually formulated in the form of "human-machine interaction." That is, either the participant interacts as a motorist with a programmed bicycle [20,21] or as a cyclist with a programmed self-driving vehicle [22]. To achieve a "human-human interaction" simulation, coupling of different simulators is required. ...
At intersections, road users need to comprehend the intentions of others while also implicitly expressing their own intentions using dynamic information. Identifying patterns of this implicit communication between human drivers and vulnerable road users (VRUs) at intersections could enhance automated driving functions (ADFs), enabling more human-like communication with VRUs. To this end, we conducted a coupled vehicle–bicycle simulator study to investigate interactions between right-turning motorists and crossing cyclists. This involved 34 participants (17 pairs of motorists and cyclists) encountering each other in a virtual intersection. The analysis focused on identifying interaction patterns between motorists and cyclists, specifically aiming to discern which patterns were more likely to be accepted by both parties. We found that in CM (vehicles overtaking), the post-encroachment time (PET) and the average speed of vehicles were higher than in the other two interaction patterns: C (bicycles always in front) and CMC (bicycles overtake). However, subjective ratings indicated that CM was viewed as more critical and less cooperative. Furthermore, this study unveiled the influence of crossing order and overtaking position on subjective ratings through ordered logistic regressions, suggesting that earlier overtaking could improve cyclists’ acceptance of the interaction. These findings may contribute to the optimization of communication strategies for ADF, thereby ensuring safety in interactions with VRUs.
... As shown in Table 2, only six of the publications involved descriptions of field observations or naturalistic scenarios of cyclists and automated vehicles (Boersma et al., 2018;De Ceunynck et al., 2022;Oskina et al., 2022;Parkin et al., 2022;Pokorny et al., 2021;Stange et al., 2022). Five publications in our study sample were simulator studies (Hou et al., 2020;Kaß et al., 2020;Lindner et al., 2022;Parkin et al., 2022;Stange et al., 2022). Additionally, one of the identified publications involved a scenario filmed in 360 degrees video (Nuñez Velasco et al., 2021), four publications described animated or still photo scenarios edited to include automated vehicles (Bazilinskyy et al., 2022;Hagenzieker et al., 2020;Stange et al., 2022;Vlakveld et al., 2020), and one focus group interview study on teenage cyclists explored potential infrastructure designs and communication interfaces through illustrations (Ngwu et al., 2022). ...
Automated vehicles pose a unique challenge to the safety of vulnerable road users. Research on cyclist-automated vehicle interaction has received relatively little attention compared to pedestrian safety. This exploratory study aims to bridge this gap by identifying cyclist-automated vehicle scenarios and providing recommendations for future research. In this study, we triangulated three sources: a systematic literature review of previous research on cyclists and automated vehicles, group interviews with eight traffic safety and automation experts, and questionnaire data. The resulting scenario collection comprised 20 prototypical scenarios of cyclist-automated vehicle interaction, grouped into four categories based on the road users' direction of movement: crossing, passing, overtaking, and merging scenarios. The survey results indicated that right-turning vehicles, dooring scenarios, and more complex situations have the highest likelihood of accidents. Passing and merging scenarios are particularly relevant for studying automated vehicle communication solutions, since they involve negotiation. Future research should also consider phantom braking and driving styles of vehicles, as well as programming proactive safety behaviours and designing on-vehicle interfaces that accommodate cyclists.
... The literature review indicates that there is a lack of coupled simulators that enable the investigation of HMI concepts between AVs and bicyclists. Also little work exists regarding HMI concepts for bicyclists, as described in [24]. Due to the trends of vehicle automation [25] and the promotion of sustainable modes of transport [26], [27], enabling safe encounter between AVs and bicycles will be a crucial point in future urban traffic. ...
... Nowadays it is practice that aHMIs are integrated in the instrument cluster in the windshield, a monitor on the center console or headup displays [32]. A more detailed description of the communication application can be found in [24]. ...
... The presented electric cargo bicycle simulator enables experiencing traffic scenarios virtually and can be used for investigating communication concepts via the board computer (e.g., collision warnings, see Lindner, et al., 2022), the transportation of children and goods, or simulation parameter validation. The hardware and software components for this setup do not greatly differ from those of bicycle simulators (e.g., visualization and the micro-simulation). ...
The transformation towards sustainable urban traffic strongly includes bicycle traffic. Besides conventional bicycles, (electric) cargo bicycles are discovered to have a great potential both for private and commercial usage in transportation. Despite many similarities to conventional bicycles, cargo bicycles differ especially in weight, size, and load configuration. Driving simulators for many road user groups such as cars and even conventional bicycles are intensively applied in urban planning and transportation research. Cargo bicycle riders are often omitted as traffic participants in these considerations. In this paper, we describe the setup of an electric cargo bicycle simulator with all hard-and software components used. Additionally, we give insights into first results.
... The literature review indicates that there is a lack of coupled simulators that enable the investigation of HMI concepts between AVs and bicyclists. Also little work exists regarding HMI concepts for bicyclists, as described in [24]. Due to the trends of vehicle automation [25] and the promotion of sustainable modes of transport [26], [27], enabling safe encounter between AVs and bicycles will be a crucial point in future urban traffic. ...
... Nowadays it is practice that aHMIs are integrated in the instrument cluster in the windshield, a monitor on the center console or headup displays [32]. A more detailed description of the communication application can be found in [24]. ...
In order to investigate the interaction between automated vehicles (AVs) and bicyclists, we present a coupled driving simulator that enables these two traffic participants to interact in a virtual environment. To avoid potentially dangerous situations in road traffic, human perception can be extended by communication between vehicles and their environment. In order to assist the communication process between traffic participants, mobile devices are applied as human-machine interfaces (HMIs). The simulator links the simulation and visualization software with a web application to control the HMIs. The passenger of the AV can change priority rules at conflict situation in the simulation with that application and therefore influence the vehicles behavior via the communication application. To test the coupled simulator, a proof of concept study with 16 simulation runs and two participants each is conducted. The subjects rated the overall simulation impressions tending positive. Based on the evaluation of the study participants, the simulator setup will be further developed.
Automated vehicles pose a unique challenge to the safety of vulnerable road users. Research on cyclist-automated vehicle interaction has received relatively little attention compared to pedestrian safety. This exploratory study aims to bridge this gap by identifying cyclist-automated vehicle scenarios and providing recommendations for future research. In this study, we triangulated three sources: a systematic literature review of previous research on cyclists and automated vehicles, group interviews with eight traffic safety and automation experts, and questionnaire data. The resulting scenario collection comprised 20 prototypical scenarios of cyclist-automated vehicle interaction, grouped into four categories based on the road users' direction of movement: crossing, passing, overtaking, and merging scenarios. The survey results indicated that right-turning vehicles, dooring scenarios, and more complex situations have the highest likelihood of accidents. Passing and merging scenarios are particularly relevant for studying automated vehicle communication solutions since they involve negotiation. Future research should also consider phantom braking and driving styles of vehicles, as well as programming proactive safety behaviours and designing on-vehicle interfaces that accommodate cyclists.
In future urban mobility, safe and efficient interaction between vulnerable road users and autonomous vehicles (AVs) will play a crucial role. In order to enable communication between human road users and AVs, different human-machine interfaces (HMI) are developed. Usually, these HMIs and onboard communication units are part of AVs, but some concepts exist that give cyclists communication capabilities and possibilities to interact with the human rider. This paper further investigates one of these on-bicycle HMIs that uses a smartphone mounted on the bicycle's handlebar. On the device, an application is running that augments routing apps with information about upcoming traffic scenarios and gives instructions on how to behave in certain situations. When interacting with AVs, knowing whether an HMI system influences the cyclist's behavior is crucial. Therefore, an AV can anticipate the cyclist's movement in the upcoming scenario reliably. In this paper, we focus on the research questions of whether there is a behavioral change, how it looks like, and whether learning effects with the application can be observed. We studied the behavior in a coupled Bicycle-AV-Simulator and focused on speed variations in the analysis, because of driving simulator validity. The results indicate a speed decrease after receiving app information about the upcoming scenario. However, a learning effect can be found. With an increasing number of study scenarios, the speed reduction decreases. Moreover, after receiving instructions on priority decisions, the cyclist reduces the speed if the AV takes priority and maintains or increases speed if the cyclist is prioritized.