CoopQ: Questionnaire for measuring the subjective evaluation of cooperation in road traffic encounters

In nowadays traffic, most encounters of road users are highly regulated. Nevertheless, traffic situations arise that are not explicitly regulated and therefore require communication and cooperation. This will also be true if self-driving vehicles enter our traffic system. It is therefore of great importance that self-driving vehicles are able to react to and show cooperative behavior. Exactly how humans cooperate in traffic is not fully understood yet. The systematic study of cooperative behavior requires appropriate tools and measures. We contribute to the development of tools by developing a questionnaire that assesses the subjective evaluation of cooperation in a traffic encounter. In this work, we present a first version of this questionnaire, which is divided into two parts: The first part of the questionnaire is intended to measure whether a given encounter between road users could be considered cooperation, and the second part of the questionnaire is intended to evaluate the encounter. Based on a literature survey, 39 items, which cover different aspects of cooperation, like altruism, interference, costs and benefits, were formulated for the first part of the questionnaire, i.e. for assessing the occurrence of cooperation. For the second part, i.e. for the evaluation of a given encounter, 40 pairs of adjectives were created based on typical motives in road traffic, e.g. safety and efficiency. In an online survey, 123 participants then rated seven videos of drivers encountering a narrow passage with varying degrees of interaction. Based on factor analysis and descriptive statistics, ten items and 22 pairs of adjectives were selected for a final version. The final version will be tested in future studies to assess the questionnaire’s reliability

Criteria for the evaluation of interaction behaviour of drivers in a bottleneck scenario

Communication and cooperation between road users are essential for road safety and need mutual understanding to be successful. This also applies to autonomous vehicles, which is why their design requires an in-depth knowledge of human interaction behavior. Acquiring this knowledge in turn requires methods and criteria with which interaction behavior can be studied. By focused interviews we aim at identifying potential criteria for the description and evaluation of cooperation in a bottleneck scenario. Participants were presented with short video sequences of motorists passing through a narrow passage and asked to comment aloud on the videos and to describe and evaluate drivers’ behavior. For qualitative analysis, the interviews were coded, distinguishing in particular between the description and evaluation of behavior. Particularly relevant for the evaluation of the bottleneck scenario seem to be the time delay with which drivers arrive at the narrow passage, as well as the arrival and departure order (who arrives first and who passes the narrow passage first). In addition, the majority of participants addressed the clarity of drivers’ behavior. These aspects are promising criteria for the evaluation of interaction behavior in a bottleneck scenario

Analysis of implicit communication of motorists and cyclists in intersection using video and trajectory data

The interaction of automated vehicles with vulnerable road users is one of the greatest challenges in the development of automated driving functions. In order to improve efficiency and ensure the safety of mixed traffic, automated driving functions need to understand the intention of vulnerable road users, to adapt to their driving behavior, and to show its intention. However, this communication may occur in an implicit way, meaning they may communicate with vulnerable road users by using dynamic information, such as speed, distance, etc. Therefore, investigating patterns of implicit communication of human drivers with vulnerable road users is relevant for developing automated driving functions. The aim of this study is to identify the patterns of implicit communication of human drivers with vulnerable road users. For this purpose, the interaction between right-turning motorists and crossing cyclists was investigated at a traffic light controlled urban intersection. In the scenario, motorists and cyclists had a green signal at the same time, but cyclist had right-of-way. Using the Application Platform for Intelligent Mobility (AIM) Research Intersection, trajectory and video data were recorded at an intersection in Braunschweig, Germany. Data had been recorded for four weeks. Based on the criticality metric post encroachment time (PET) and quality of the recorded trajectory, 206 cases of interaction were selected for further analyses. According to the video annotation, when approaching the intersection, three common communication patterns were identified: (1) no yield, motorists, who should yield to cyclists, crossed the intersection first while forcing right-of-way; (2) active yield, motorists, who were in front of cyclists, gave the right-of-way; (3) passive yield, motorists, who were behind cyclists, had to give the right-of-way. The analysis of the trajectory data revealed different patterns of changes in time advantage in these three categories. Additionally, the communication patterns were evaluated with regard to frequency of occurrence, efficiency, and safety. The findings of this study may provide knowledge for the implementation of a communication strategy for automated driving functions, contributing to traffic efficiency as well as ensuring safety in the interaction with vulnerable road users

Turning Left at Urban Intersections: Turning Patterns and Gap Acceptance

Turning left through oncoming traffic is one of the most safety-critical traffic manoeuvres. This work aims to describe and understand the interaction behaviour between left-turning and oncoming traffic by analysing video and trajectory data collected at the Application Platform for Intelligent Mobility (AIM) research intersection in Braunschweig, Germany. At this intersection, motorists turning left from Rebenring into Brucknerstraße must give way to oncoming traffic as well as crossing cyclists and pedestrians. In the following analyses, video and trajectory data of thirteen days (recorded in the period of 20th of May to 2nd of June 2019) were included. In a first step, potential interactions between left-turning and oncoming traffic were extracted based on post encroachment time (PET), which indicates by how many seconds two intersecting road users miss each other. In order to investigate both critical and less critical interactions between left-turning and oncoming traffic, left-turns with a PET ≤ 2 seconds between the left-turning vehicle and oncoming traffic were randomly selected for further analysis (80 left-turns per day). In two cases a left turn was detected by mistake, reducing the number of cases to n = 1038. The video data of these left-turns were then analysed to determine the turning pattern. Four turning patterns were identified: The left-turning motorist turned a) before all oncoming traffic, b) after all oncoming traffic, c) between oncoming traffic (i.e. both before and after oncoming traffic passed the intersection within a traffic light phase), and d) while the traffic light for oncoming traffic turned red (i.e. while oncoming traffic slowed down or stopped in front of the traffic light). Due to the selection by means of PET, left-turns without oncoming traffic were not included. The analysis revealed that left-turning motorists turned between oncoming traffic (pattern c) in about 50 % of cases, and were the first to turn left within one traffic light phase in 85 % of cases. In a second step, the gap acceptance was determined for a subset of cases (n = 191; all motorists who turned left first within one traffic light phase with pattern c that passed between 6 and 20 o’clock within seven days) by first calculating the gap size of all accepted and rejected gaps. The gap size was defined as the time between the point in time when the rear of the first vehicle crossed the future path of the left-turning vehicle and the point in time when the front of the second vehicle crossed this path. Including 191 accepted and 830 rejected gaps, gap acceptance was then estimated by means of logistic regression. The analysis showed that all gaps larger than 8.03 seconds were accepted and all gaps smaller than 2.74 seconds were rejected. Gap acceptance was 50 % for a gap size of 4.70 seconds. In a next step, cases with gap sizes around 4.70 seconds will be examined in more detail to determine factors influencing the decision to turn (besides gap size). Of special interest is the behaviour of oncoming traffic (e.g. speed and acceleration patterns of oncoming motorists). If, for example, specific driving patterns affect left-turn decisions, these patterns could be implemented in automated vehicles to facilitate safe turning behaviour. In general, these findings might support the selection of suitable infrastructure measures and the development of assistance systems and autonomous driving functions to promote safety

Human Performance in Critical Scenarios as a Benchmark for Highly Automated Vehicles

Before highly automated vehicles (HAVs) become part of everyday traffic, their safety has to be proven. The use of human performance as a benchmark represents a promising approach, but appropriate methods to quantify and compare human and HAV performance are rare. By adapting the method of constant stimuli, a scenario-based approach to quantify the limit of (human) performance is developed. The method is applied to a driving simulator study, in which participants are repeatedly confronted with a cut-in manoeuvre on a highway. By systematically manipulating the criticality of the manoeuvre in terms of time to collision, humans’ collision avoidance performance is measured. The limit of human performance is then identifed by means of logistic regression. The calculated regression curve and its inflection point can be used for direct comparison of human and HAV performance. Accordingly, the presented approach represents one means by which HAVs’ safety performance could be proven

Why so serious? – Comparing two traffic conflict techniques for assessing encounters in shared space

In Germany, approximately 2.7 million crashes occurred in 2019. Especially vulnerable road users (VRU) have a high risk of being seriously injured or killed in traffic. Within the safe system approach, changes to the traffic infrastructure have been implemented to increase VRU safety. The creation of so-called shared spaces, in which all road users are encouraged to negotiate priority, is part of these efforts. Even though the concept has been known and applied for more than 40 years, comparatively little is known about interactions between different road users and methods to quantify interactions in shared spaces. The aim of this study is to investigate similarities and differences in quantifying the level of severity of encounters between pedestrians and motorised vehicles applying the Swedish traffic conflicts technique (STCT) and the pedestrian-vehicle conflicts analysis (PVCA). The STCT integrates the factors conflicting speed (CS) and time-to-accident (TA) to arrive at a severity level. In contrast, with four factors, the PVCA integrates more elements: time-to-collision (TTC, corresponding to TA), severity of evasive action, complexity of evasive action, and distance-to-collision (DTC). Trajectory and video data of a shared space were recorded using the Application Platform for Intelligent Mobile Units (AIM) in Ulm, Germany. 1364 interactions were randomly selected. Due to different exclusion criteria, such as interaction partners not being a car or pedestrian, missing values, and detection errors, 69 encounters were available for analyses. Using the PVCA, nine encounters were classified as critical and 60 as non-critical interactions. In contrast, computing the values based on the STCT, only three of the 69 encounters were categorised as critical. The results of a Spearman rank correlation did not show a significant correlation between the severity categories of the PVCA and severity levels of the STCT (r = 0.03, p = 0.78). An additional analysis of the encounters ranked as critical by the PVCA but as non-critical by the STCT showed that all six encounters had a large temporal distance (> 2 s) combined with very small spatial distance (< 5 m for vehicles and < 2.5 m for pedestrians). While the PVCA and STCT yielded similar results in most encounters, this could not be confirmed for all. Results indicate that spatial distance may contribute to the severity of encounters between pedestrians and vehicles in a shared space

What is strenuous? Driving itself or the driving situation?

To avoid driver overload, assistance systems can be adapted regarding the driver’s current strain. Physiological and performance workload measures require special sensors and are problematic concerning sensitivity and specificity. Within the presented study the driver’s stress level was estimated in real-driving based on an analysis of different driving manoeuvres and environmental factors. The analyses show that different driving manoeuvres result in significantly different subjective strain levels. Furthermore, situational factors like the intention to overtake or road characteristics modify subjective strain systematically. This behaviour oriented approach to estimate strain seems to be an auspicious alternative to current measurements of strain

Dynamische Beanspruchungsmessung - Zusätzliche Beanspruchung durch Online-Rating?

Ziel der Forschungsarbeit ist die Entwicklung einer Methode zur kontinuierlichen Beanspruchungsmessung beim Fahren, um damit dynamisch die Auswirkungen von Belastungsfaktoren untersuchen zu können. N = 20 Probanden nahmen an einer Simulatorstudie teil, bei der sie neben der normalen Fahraufgabe kontinuierlich ihre subjektive Beanspruchung beurteilen sollten. Um mögliche Interferenzen zwischen der Sekundäraufgabe „Beanspruchungsrating“ und der Primäraufgabe „Fahren“ zu analysieren wurden sowohl die Ratingbedingung (während versus nach der Fahrt) als auch die Ratingmethode (Tasten- versus Spracheingabe) variiert. Als Belastungsfaktoren wurden vier Fahrmanöver sowie drei Streckenabschnitte untersucht. Es zeigen sich weder auf Verhaltens- noch auf subjektiver Ebene signifikante Haupteffekte der Ratingbedingung und der Ratingmethode. Dagegen zeigen sich die Einflüsse der Belastungsfaktoren in vergleichbarer Größenordnung bei den verschiedenen Ratingmethoden und Ratingbedingungen. Signifikante Wechselwirkungen weisen darauf hin, dass bei hoher Beanspruchung ein Rating über Tasten während der Fahrt schwierig sein könnte. Allerdings ist dieser Effekt nicht so stark, dass dadurch Effekte nicht mehr zu finden sind. Insgesamt findet sich damit keine große zusätzliche Beanspruchung durch die hier vorgestellte Methode des Beanspruchungsratings während der Fahrt, so dass dies für entsprechende Untersuchungen gut genutzt werden kann

Dynamic access to the actual driver state

As an inadequate level of workload is an important contributing factor to accidents, information management and driver assistance systems (ADAS) are developed with the general aim to optimize driver workload. This is done either by controlling the amount of information presented to the driver or by supporting parts of the driving task by an ADAS. As the workload of the driver changes with different driving tasks in a highly dynamic traffic these systems have to react in a similar dynamic manner. This requires a fast and dynamically changing estimation of the driver’s workload. In recent real driving studies we could show that driver workload changes with different driving manoeuvres and additionally depends on environmental factors. There were also indications that workload varied even within one manoeuvre. To examine workload influenced by different driving manoeuvres, different phases within the manoeuvres and different environmental factors a simulator study was conducted where different traffic densities were varied and driver workload was measured continuously. This is used to analyse the level and time-course of workload depending on several influencing factors. Implications of these results for the adaptation of ADAS to the workload of the driver are discussed