Human Road Users Research Articles

Do cyclists disregard ‘priority-to-the-right’ more often than motorists?

The integration of autonomous driving into mixed traffic environments poses unique challenges, especially at unsignalized intersections where communication and cooperation with human road users is necessary. This study addresses a critical research gap by investigating the adherence to the “priority-to-the-right” rule at an urban unsignalized intersection, comparing behavioral patterns of cyclists and motorists. Utilizing stationary mounted cameras, a 12-day traffic observation of an urban T-intersection in Braunschweig, Germany, was conducted. The class and the trajectory of road users as well as their scenarios were identified. In 202 cases, cars appearing from the right (ego, with priority) encountered cars or bikes coming from the left (foe, without priority). The study analyzed the impact of variables, ego’s direction, foe’s class and lateral position and their arrival time on their passing order through descriptive statistics and logistic regression. The findings reveal that cyclists disregard the “priority-to-the-right” rule more often than motorists. Additionally, road users who have the priority are more likely to yield when turning right, arriving at the intersection later, and encountering an opposing road user who is close to the center of the road. This study highlights the importance of implicit communication in traffic and provides essential benchmarks for developing more human-like autonomous driving systems, capable of interpreting and responding to nuanced road user interactions at unsignalized intersections.

Characteristics of Rear-End Collisions: A Comparison between Automated Driving System-Involved Crashes and Advanced Driving Assistance System-Involved Crashes

With the increasing number of vehicles equipped with automated driving systems (ADS) being tested on public roads and the expanding market share of vehicles equipped with advanced driving assistance systems (ADAS), the number of ADS- or ADAS-involved crashes increases. Thus, it is necessary to investigate the distribution of ADS- and ADAS-involved crashes and the factors leading to them. The rear-end collision has been found to dominate ADS-involved crashes. However, no research has explored the conditions when ADS-involved rear-end collisions are more likely to happen and no research has investigated ADAS-involved rear-end crashes. Based on 130 ADS-involved crashes and 84 ADAS-involved crashes extracted from a dataset collected by the National Highway Traffic Safety Administration (NHTSA) between July 2021 and May 2022, this study explored the crash patterns, especially rear-end crashes, of ADS- and ADAS-controlled vehicles. Results show that rear-end collisions dominate both ADS- and ADAS-involved crashes, especially ADAS-involved crashes. The type of both ADS-involved and ADAS-involved crashes was affected by the speed of the ego-vehicle relative to the posted speed limit. Further, the type of ADS-involved crash was affected by the pre-crash movement of the crash partner, while the type of ADAS-involved crash was further associated with the road type. The findings can provide insights into the design of ADAS and ADS control algorithms, the external human-machine interface design of the vehicles with ADS or ADAS, and the training program of human road users to improve traffic safety in mixed traffic.

Social Interaction‐Aware Dynamical Models and Decision‐Making for Autonomous Vehicles

Interaction‐aware autonomous driving (IAAD) is a rapidly growing field of research that focuses on the development of autonomous vehicles (AVs) that are capable of interacting safely and efficiently with human road users. This is a challenging task, as it requires the AV to be able to understand and predict the behaviour of human road users. In this literature review, the current state of IAAD research is surveyed. Commencing with an examination of terminology, attention is drawn to challenges and existing models employed for modeling the behaviour of drivers and pedestrians. Next, a comprehensive review is conducted on various techniques proposed for interaction modeling, encompassing cognitive methods, machine‐learning approaches, and game‐theoretic methods. The conclusion is reached through a discussion of potential advantages and risks associated with IAAD, along with the illumination of pivotal research inquiries necessitating future exploration.

Explaining human interactions on the road by large-scale integration of computational psychological theory.

When humans share space in road traffic, as drivers or as vulnerable road users, they draw on their full range of communicative and interactive capabilities. Much remains unknown about these behaviors, but they need to be captured in models if automated vehicles are to coexist successfully with human road users. Empirical studies of human road user behavior implicate a large number of underlying cognitive mechanisms, which taken together are well beyond the scope of existing computational models. Here, we note that for all of these putative mechanisms, computational theories exist in different subdisciplines of psychology, for more constrained tasks. We demonstrate how these separate theories can be generalized from abstract laboratory paradigms and integrated into a computational framework for modeling human road user interaction, combining Bayesian perception, a theory of mind regarding others' intentions, behavioral game theory, long-term valuation of action alternatives, and evidence accumulation decision-making. We show that a model with these assumptions-but not simpler versions of the same model-can account for a number of previously unexplained phenomena in naturalistic driver-pedestrian road-crossing interactions, and successfully predicts interaction outcomes in an unseen data set. Our modeling results contribute to demonstrating the real-world value of the theories from which we draw, and address calls in psychology for cumulative theory-building, presenting human road use as a suitable setting for work of this nature. Our findings also underscore the formidable complexity of human interaction in road traffic, with strong implications for the requirements to set on development and testing of vehicle automation.

Automated shuttles and ‘negotiation in motion’ – A qualitative meta-synthesis of spatial interactions with human road users

Since automated vehicles (AVs) were first introduced in public imagination, the stated goal of developers has been to develop vehicles that would eventually operate in diverse contexts like any other vehicle. To understand what this entails in real-life traffic, data regarding interactions were extracted from three separately run trials of automated shuttles in low-speed contexts with human road users in Denmark (2018–21) using a qualitative meta-synthesis approach. The underlying data consists of field observations, interviews with road users, geolocalized event registrations, video tracking data, and responses to open-ended surveys. The synthesis suggests that 1) dynamic negotiation of space and timing, 2) handling of situational and traffic system ambiguity, and 3) human road user learning, go beyond what should simply be attributed to a transitory immaturity of the technology. Road users expect other road users to engage in a deeply social negotiation of space and timing. When AVs fail to negotiate, traffic flow is interrupted, and road users express confusion and impatience, until they develop strategies to obstruct or move around the shuttles. We discuss implications on planning in low-car environments.

Trajectory-based traffic observation of cooperation at a road narrowing

Abstract Understanding human interactions in today’s transportation system is a prerequisite for developing well-accepted cooperatively interacting autonomous vehicles. This paper is devoted to the two-sided narrow passage scenario and uses trajectory data to investigate drivers’ interaction behavior when encountering each other from opposite directions. Trajectory data of 209 encounters at a road narrowing were analyzed in terms of drivers’ approaching behavior and arrival order. The exploratory analysis has shown that in this specific location an informal traffic rule has developed: It was not the order of arrival but the direction of travel that primarily determined who passed the road narrowing first. This result shows that informal rules can influence drivers’ interaction behavior and should accordingly be considered in the development of autonomous vehicles to ensure safe and efficient encounters with human road users.

Pavement Marking Practices, Standards, Applications, and Retroreflectivity

Pavement markings are important elements of roadway networks and help guide traffic flow in an orderly manner. In recent years, transportation agencies are facing challenges to manage pavement markings because of the advent of new vehicle technologies. Existing pavement marking standards were developed for serving human road users but they may not be effective for both human drivers and machine vision systems. Therefore, the National Committee on Uniform Traffic Control Devices suggested amendments to the current pavement marking standards. Therefore, it is necessary to explore pavement marking practices for a variety of states. With this aim, the Wyoming Technology Transfer Center conducted an online survey as part of a comprehensive research project on developing pavement marking management plans for the Wyoming Department of Transportation. The main objective of the survey is to document the various state Departments of Transportation (DOTs’) pavement marking management plans, how the plans are developed, strategies for pavement marking data collection, and pavement marking retroreflectivity. The survey has 31 questions dealing with pavement marking striping, pavement marking data collection, pavement marking retroreflectivity, and a few miscellaneous questions. There are 29 DOTs who responded to the survey. This paper summarizes the responses from the survey on evaluating pavement marking management practices at a national level.

Is it OK to bully automated cars?

To integrate automated vehicles (AVs) into our transportation network, we should consider how human road users will interact with them. Human aggression toward AVs could be a new risk in mixed traffic and reduce AV adoption. Is it OK to drive aggressively toward AVs? We examined how identical aggressive behavior toward an AV or human driver is appraised differently by observers. In our 2 (scenario type: human driver vs. AV) × 2 (victim identity salience: low vs. high) between-subjects survey, we randomly allocated participants (N = 956) to one of four conditions where they viewed a video clip from an AV or a human driver showing a car suddenly braking continuously ahead of the AV or human driver’s car. The salience of victim identity influenced the observers’ appraisals of aggressive behavior. When asked to judge the front car’s behavior toward this AV or human driver (the victim identity is salient), they reported more acceptability and less risk perception, negative affect, and immoral judgment while judging this behavior toward the AV. When asked to judge the front car’s behavior (the victim identity not highlighted), they reported non-different appraisals. This finding implies that AVs might need to hide their identity to blend in visually and behaviorally as regular cars.

External communication of automated vehicles in mixed traffic: Addressing the right human interaction partner in multi-agent simulation

The urban traffic system is most likely to change in the next years to a mixed traffic with human drivers, vulnerable road users, and automated vehicles. In the past, the development of external communication approaches for automated vehicles focused on scenarios where an automated vehicle communicates with either a pedestrian or a human driver. However, interactions with more than one traffic partner are more realistic. Therefore, a study with 42 participants was conducted with a multi-agent simulation in which an automated vehicle interacted simultaneously with two participants, a pedestrian and a driver of a manual vehicle. In this study, two main scenarios were investigated in order to evaluate the safety and efficiency of the interactions and to determine whether the human road users feel correctly addressed. In one scenario, the pedestrian had to cross the road in front of the automated and the manual vehicle, which were approaching from different sides. In the other, the manual vehicle had to drive through a bottleneck in front of the oncoming automated vehicle, while the pedestrian had to cross the road after both vehicles passed. The communication approach of the automated vehicle consisted of implicit signals using a speed profile and lateral offset within its lane, and explicit signals using an external human–machine interface. The results of the study show that no collisions were observed in terms of safety and no significant negative effects on efficiency were measured. However, in contrast to single agent interactions, a majority of participants felt wrongly addressed in situations where the automated vehicle signals the right-of-way to the other human road user. It can be concluded that the communication approach of the automated vehicle needs to be modified in order to address certain road users more clearly.

Please stop now, automated vehicle! – Passengers aim to avoid risk experiences in interactions with a crossing vulnerable road user at an urban junction

The success of highly automated vehicles (HAVs; SAE Level 4) will depend to a large extent on how well they are accepted by their future passengers. This is especially true for the interaction of these vehicles with other human road users in mixed traffic. In future urban traffic, passengers of HAVs will observe from a passive position how the automated system resolves space-sharing conflicts with crossing vulnerable road users (VRUs; e.g., pedestrians and cyclists) at junctions. For one such crossing-paths conflict, we investigated when passengers would want the HAV to start braking and how much perceived risk passengers accept in the interaction of their vehicle with VRUs. To this end, we conducted 1) an online video study (N = 118), 2) a driving simulator study (N = 28), and 3) a human&vehicle-in-the-loop (Hu&ViL) study at a test site (N = 10). We varied the speed of the HAV (30 km/h vs. 50 km/h), the type (cyclist vs. pedestrian), and crossing direction of the VRU (left vs. right). During the approach to the junction, passengers' task was to trigger the HAV's braking maneuver, in a first trial at the point they considered ideal and in a second trial at the last point they still considered safe enough to decelerate and come to a stop at the stop line. For each braking maneuver, we analyzed the HAV’s distance and time-to-arrival (TTA) to the VRU at braking onset, as well as passengers’ perceived risk in the VRU interaction. The results showed that most passengers preferred harmless interactions with VRUs (at the ideal braking onset time), and accepted unpleasant, but not dangerous interactions at most (at the last acceptable braking onset time). Methodologically, the results were very similar in the three different environments (online, driving simulator, real vehicle). These results clearly show that, in addition to the technical considerations of safe automated driving, passengers’ perception and evaluation of HAV driving behavior should also be taken into account to achieve a satisfying level of acceptance of these vehicles.

Communication via motion – Suitability of automated vehicle movements to negotiate the right of way in road bottleneck scenarios

The introduction of automated vehicles (AVs) into urban areas initially leads to mixed traffic, consisting of AVs, human drivers, and vulnerable road users. Since the AV's passenger is no longer actively involved in the driving task, there may be changes in the interaction between AVs and surrounding human road users. Therefore, it is essential for an AV to behave in a comprehensible manner in order to maintain or even enhance traffic efficiency and traffic safety. This work investigates the interaction of an AV and a simultaneously oncoming human driver at road bottlenecks due to double-parked vehicles on both sides of the road. Based on findings derived from AV-pedestrian interaction, comfort limits in terms of driving dynamics, and traffic observations, we designed nine AV movements to either yield the right of way or to insist on it by varying the AV's speed (maintain speed, one-step deceleration, two-step deceleration) and its lateral offset (no offset, close offset, distant offset). The different vehicle movements were evaluated with 34 participants in a driving simulator study. The results show participants' shorter passing times, fewer crashes, and significantly higher ratings of the AV's communication if the AV movement contained a lateral offset. In addition to the regular encounters, we analyzed the controllability of an automation failure and its aftereffect on participants' trust in AVs. The experience of the automation failure reduced the trust rating significantly. From the results we conclude that the AV should communicate the right of way not only via speed adjustments but also via the performance of a lateral offset to enhance traffic efficiency and safety. Moreover, a change in the AV's maneuver due to an automation failure must be avoided since it is not controllable by the human driver.

International study on the importance of communication between automated vehicles and pedestrians

The market launch of automated vehicles will take place in complex mixed traffic containing pedestrians and other non-automated drivers. Hand gestures, eye contact or similar informal communication strategies to human road users will not be available without a human car driver. Road crossing studies show that people are feeling confused and unsafe without such feedback. Additional external signaling devices have the aim to increase the perception of safety by providing auditory or visual feedback for road users. Due to the international relevance of this topic, we surveyed participants from six countries on the importance of communication. Our results reveal that intention messages are more important than the status signal. The importance of communication is independent of the time of day, traffic density and number of pedestrians. Cross-group analysis indicates a match of 72.97% between the tested nationalities on which kind of message types an automated vehicle should be able to communicate.

How Much Space Is Required? Effect of Distance, Content, and Color on External Human–Machine Interface Size

The communication of an automated vehicle (AV) with human road users can be realized by means of an external human–machine interface (eHMI), such as displays mounted on the AV’s surface. For this purpose, the amount of time needed for a human interaction partner to perceive the AV’s message and to act accordingly has to be taken into account. Any message displayed by an AV must satisfy minimum size requirements based on the dynamics of the road traffic and the time required by the human. This paper examines the size requirements of displayed text or symbols for ensuring the legibility of a message. Based on the limitations of available package space in current vehicle models and the ergonomic requirements of the interface design, an eHMI prototype was developed. A study involving 30 participants varied the content type (text and symbols) and content color (white, red, green) in a repeated measures design. We investigated the influence of content type on content size to ensure legibility from a constant distance. We also analyzed the influence of content type and content color on the human detection range. The results show that, at a fixed distance, text has to be larger than symbols in order to maintain legibility. Moreover, symbols can be discerned from a greater distance than text. Color had no content overlapping effect on the human detection range. In order to ensure the maximum possible detection range among human road users, an AV should display symbols rather than text. Additionally, the symbols could be color-coded for better message comprehension without affecting the human detection range.

Road user interactions in a shared space setting: Priority and communication in a UK car park

Appropriate communication between road users can lead to safe and efficient interactions in mixed traffic. Understanding how road users communicate can support the development of effective communication methods for automated vehicles. We carried out observations of 66 pedestrian-driver and 124 driver-driver interactions in a shared space setting. Specific actions and reactions of the road users involved were recorded using a novel observation protocol. Overall, results showed that pedestrians’ failure to look towards a driver created the greatest uncertainty in the interaction, with the driver slowing down, but not completely stopping, in response to pedestrians. Looking towards the driver also influenced which road user took priority in driver-driver interactions. Groups of pedestrians were more likely to be given priority than an individual pedestrian, and the use of vehicle-based signals were also associated with taking priority during an interaction. Our observations show the importance of non-verbal communication during road user interactions, highlighting it as an essential area of research in the development of automated vehicles, to allow their safe, cooperative, interactions with other road users. Observations were made on a limited number of interactions to inform challenges facing future automated vehicles. Further work should therefore be done to corroborate and extend our findings, to examine interactions between human road users and automated vehicles in shared space settings.

Comparison of Methods to Evaluate the Influence of an Automated Vehicle’s Driving Behavior on Pedestrians: Wizard of Oz, Virtual Reality, and Video

Integrating automated vehicles into mixed traffic entails several challenges. Their driving behavior must be designed such that is understandable for all human road users, and that it ensures an efficient and safe traffic system. Previous studies investigated these issues, especially regarding the communication between automated vehicles and pedestrians. These studies used different methods, e.g., videos, virtual reality, or Wizard of Oz vehicles. However, the extent of transferability between these studies is still unknown. Therefore, we replicated the same study design in four different settings: two video, one virtual reality, and one Wizard of Oz setup. In the first video setup, videos from the virtual reality setup were used, while in the second setup, we filmed the Wizard of Oz vehicle. In all studies, participants stood at the roadside in a shared space. An automated vehicle approached from the left, using different driving profiles characterized by changing speed to communicate its intention to let the pedestrians cross the road. Participants were asked to recognize the intention of the automated vehicle and to press a button as soon as they realized this intention. Results revealed differences in the intention recognition time between the four study setups, as well as in the correct intention rate. The results from vehicle–pedestrian interaction studies published in recent years that used different study settings can therefore only be compared to each other to a limited extent.

On the Impact of the Rules on Autonomous Drive Learning

Autonomous vehicles raise many ethical and moral issues that are not easy to deal with and that, if not addressed correctly, might be an obstacle to the advent of such a technological revolution. These issues are critical because autonomous vehicles will interact with human road users in new ways and current traffic rules might not be suitable for the resulting environment. We consider the problem of learning optimal behavior for autonomous vehicles using Reinforcement Learning in a simple road graph environment. In particular, we investigate the impact of traffic rules on the learned behaviors and consider a scenario where drivers are punished when they are not compliant with the rules, i.e., a scenario in which violation of traffic rules cannot be fully prevented. We performed an extensive experimental campaign, in a simulated environment, in which drivers were trained with and without rules, and assessed the learned behaviors in terms of efficiency and safety. The results show that drivers trained with rules enforcement are willing to reduce their efficiency in exchange for being compliant to the rules, thus leading to higher overall safety.

Defining interactions: a conceptual framework for understanding interactive behaviour in human and automated road traffic

Rapid advances in technology for highly automated vehicles (HAVs) have raised concerns about coexistence of HAVs and human road users. Although there is a long tradition of research into human road user interactions, there is a lack of shared models and terminology to support cross-disciplinary research and development towards safe and acceptable interaction-capable HAVs. Here, we review the main themes and findings in previous theoretical and empirical interaction research, and find large variability in perspectives and terminologies. We unify these perspectives in a structured, cross-theoretical conceptual framework, describing what road traffic interactions are, how they arise, and how they get resolved. Two key contributions are: (1) a stringent definition of “interaction”, as “a situation where the behaviour of at least two road users can be interpreted as being influenced by the possibility that they are both intending to occupy the same region of space at the same time in the near future”, and (2) a taxonomy of the types of behaviours that road users exhibit in interactions. We hope that this conceptual framework will be useful in the development of improved empirical methodology, theoretical models, and technical requirements on vehicle automation.

Ready to bully automated vehicles on public roads?

Automated vehicles (AVs), the wide adoption of which is expected to improve traffic safety significantly, are penetrating our roads. The AVs that are testing on public roads have been bullied by human road users. We are not sure whether the bullying incidents are isolated or will be common in the future. In a cross-national survey (N = 998 drivers in China and South Korea), we developed an eleven-item bullying intention questionnaire. We assumed and confirmed that, overall, participants had a greater intention to bully machine drivers than to bully other human drivers. Compared to the Korean participants, the Chinese participants reported a greater intention to drive aggressively. The correlations of their intention to bully AVs with their attitude toward AVs and with risk-benefit perception of AVs were weak. Male participants (vs. female participants) and younger participants (vs. older participants) reported a greater intention to drive aggressively. Drivers' aggressive behaviors toward AVs might be common in the future, which might increase traffic risk and hinder the implementation of this technology.

Co‐existence or displacement: Do street trials of intelligent vehicles test society?

This paper examines recent street tests of autonomous vehicles (AVs) in the UK and makes the case for an experimental approach in the sociology of intelligent technology. In recent years intelligent vehicle testing has moved from the laboratory to the street, raising the question of whether technology trials equally constitute tests of society. To adequately address this question, I argue, we need to move beyond analytic frameworks developed in 1990s Science and Technology Studies, which stipulated "a social deficit" of both intelligent technology and technology testing. This diagnosis no longer provides an effective starting point for sociological analysis, as real-world tests of intelligent technology explicitly seek to bring social phenomena within the remit of technology testing. I propose that we examine instead whether and how the introduction of intelligent vehicles into the street involves the qualification and re-qualification of relations and dynamics between social actors. I develop this proposal through a discussion of a field study of AV street trials in three cities in the UK-London, Milton Keynes, and Coventry. These urban trials were accompanied by the claim that automotive testing on the open road will enable cars to operate in tune with the social environment, and I show how iterations of street testing undo this proposition and compel its reformulation. Current test designs are limited by their narrow conception of sociality in terms of interaction between cars and other road users. They exclude from consideration the relational capacities of vehicles and human road users alike-their ability to co-exist on the open road. I conclude by making the case for methodological innovation in social studies of intelligent technology: by combining social research and design methods, we can re-purpose real-world test environments in order to elucidate social issues and dynamics raised by intelligent vehicles in society by experimental means, and, possibly, test society.

Automated Driving System Collisions: Early Lessons.

This research evaluated Automated Driving Systems (ADSs) involved collisions to identify factors relevant to future ADS research and development. Rapidly developing ADSs promise improved safety, among other benefits. Properly applied collision research can inform ADS development, to minimize future collisions. Errors and failures that result in collisions come from sources including the system, ADS operators, and external factors including other drivers. Partially automated systems incorporate new equipment and procedures creating new sources of human error. Fully autonomous systems represent a new class of drivers that interact in unique ways. ADS collision reports from the California Department of Motor Vehicles and the National Transportation Safety Board were collected. An expert in human factors and collision investigation analyzed and categorized the crashes while extracting common factors. ADS vehicles were never at fault but were often affected from the rear during braking, turning, and gap acceptance maneuvers. Side impacts to ADS vehicles were related to passing vehicles and lane keeping behaviors. Unique incidents also provided additional insights. ADS collision rates cannot yet be determined with confidence. Conflicts that lead to collision-involvement with ADSs may be caused by differences between ADS and human driving behavior. Conservative ADS behavior may violate the expectations of other nearby human road users. The findings from this work help inform the future development of ADS, as well as potentially the testing of ADS and the formation of policy to guide their future deployment.