Intentions Of Vehicles Research Articles

Hello, is it me you're Stopping for? The Effect of external Human Machine Interface Familiarity on Pedestrians' Crossing Behaviour in an Ambiguous Situation.

We investigated how different deceleration intentions (i.e. an automated vehicle either decelerated for leading traffic or yielded for pedestrians) and a novel (Slow Pulsing Light Band - SPLB) or familiar (Flashing Headlights - FH) external Human Machine Interface (eHMI) informed pedestrians' crossing behaviour. The introduction of SAE Level 4 Automated Vehicles (AVs) has recently fuelled interest in new forms of explicit communication via eHMIs, to improve the interaction between AVs and surrounding road users. Before implementing these eHMIs, it is necessary to understand how pedestrians use them to inform their crossing decisions. Thirty participants took part in the study using a Head-Mounted Display. The independent variables were deceleration intentions and eHMI design. The percentage of crossings, collision frequency and crossing initiation time across trials were measured. Pedestrians were able to identify the intentions of a decelerating vehicle, using implicit cues, with more crossings made when the approaching vehicles were yielding to them. They were also more likely to cross when a familiar eHMI was presented, compared to a novel one or no eHMI, regardless of the vehicle's intention. Finally, participants learned to take a more cautious approach as trials progressed, and not to base their decisions solely on the eHMI. A familiar eHMI led to early crossings regardless of the vehicle's intention but also led to a higher collision frequency than a novel eHMI. To achieve safe and acceptable interactions with AVs, it is important to provide eHMIs that are congruent with road users' expectations.

Go Ahead, Please!—Evaluation of External Human—Machine Interfaces in a Real-World Crossing Scenario

In the future, automated vehicles (AVs) without a human driver will potentially have to manage communication with vulnerable road users, such as pedestrians, in everyday traffic interaction situations. The aim of this work is to investigate pedestrian reactions to external communication concepts in a controlled, but real-world crossing scenario. The focus is to investigate which properties of external human–machine interfaces (eHMIs) promote the comprehensibility of vehicle intention (yielding for the pedestrian) and therefore lead to faster and, at the same time, safer crossing decisions of pedestrians. For this purpose, three different eHMI concepts (intention-based light-band, perception-based light-band, and the combination of light-band and signal lamp) were examined and compared to a baseline (no eHMI). In a Wizard-of-Oz experiment, participants (n = 30) encountered a test vehicle equipped with the eHMIs in a real-world crossing scenario. The crossing initiation time in seconds and the participant's intention recognition were measured. Furthermore, the influence of the eHMIs on acceptance and perceived safety was evaluated. It was shown that the presence of the intention-based light-band, and the combination of light-band and signal lamp led to an earlier crossing decision compared to baseline with no eHMI. In summary, the results indicate that the intention-based light-band has a positive effect on the comprehensibility of the vehicle's intention. All concepts were evaluated positively regarding acceptance and perceived safety, and did not differ significantly from each other.

How Should Automated Vehicles Communicate Critical Situations?

Passengers of automated vehicles will likely engage in non-driving related activities like reading and, therefore, be disengaged from the driving task. However, especially in critical situations such as unexpected pedestrian crossings, it can be assumed that passengers request information about the vehicle's intention and an explanation. Some concepts were proposed for such communication from the automated vehicle to the passenger. However, results are not comparable due to varying information content and scenarios. We present a comparative study in Virtual Reality (N=20) of four visualization concepts and a baseline with Augmented Reality, a Head-Up Display, or Lightbands. We found that all concepts were rated reasonable and necessary and increased trust, perceived safety, perceived intelligence, and acceptance compared to no visualization. However, when visualizations were compared, there were hardly any significant differences between them.

Situation-Aware Left-Turning Connected and Automated Vehicle Operation at Signalized Intersections

One challenging aspect of the connected and automated vehicle (CAV) operation in mixed traffic is the development of a situation-awareness module for CAVs. While operating on public roads, CAVs need to assess their surroundings, especially the intentions of non-CAVs. Generally, CAVs demonstrate a defensive driving behavior, and CAVs expect other nonautonomous entities on the road will follow the traffic rules or common driving behavior. However, the presence of aggressive human drivers in the surrounding environment, who may not follow traffic rules and behave abruptly, can lead to serious safety consequences. In this article, we have addressed the CAV and non-CAV interaction by evaluating a situation-awareness module for left-turning CAV operations in an urban area. Existing literature does not consider the intent of the following vehicle for a CAV's left-turning movement, and existing CAV controllers do not assess the following non-CAV's intents. Based on our simulation study, the situation-aware CAV controller module reduces up to 47% of the abrupt braking of the following non-CAVs for scenarios with different opposing through movement compared to the base scenario with the autonomous vehicle, without considering the following vehicle's intent. The analysis shows that the average travel time reductions for the opposite through traffic volumes of 600, 800, and 1000 vehicle/hour/lane are about 58%, 52%, and 62%, respectively, considering both scenarios for the aggressive human driver following the CAV if the following vehicle's intent is considered by a CAV in making a left-turn at an intersection.