The deployment of automated vehicles (AVs) on public roads remains limited due to concerns about their interaction with human-driven vehicles (HDVs) in mixed traffic. While previous studies suggest that AVs influence HDV behaviour, the nature of this influence is still not well understood. This study examines how AVs affect HDV car-following behaviour in mixed traffic conditions. Empirical data were collected through a driving simulator experiment in which participants followed a lead vehicle in four scenarios varying in vehicle appearance (AV or HDV) and driving style (AV-like or HDV-like). Car-following behaviour was analysed using the Intelligent Driver Model (IDM) and an extended version (IDM+). The results show that HDVs adapt their behaviour when following AVs, exhibiting smaller jam spacing distances and shorter safe time headways compared to following HDVs. These findings support more accurate assessments of traffic safety and efficiency and contribute to the safe integration of AVs into mixed traffic.

Perceived safety and comfort influence cycling mode choice and behaviour. While roundabouts are associated with a decreased severity of motor vehicle crashes, recent crash data in the Netherlands suggests that this is not the case for bicycle crashes, with 12% of all bicycle crashes between 2014 and 2021 occurring at roundabouts. Previous studies have mainly focused on intersection type and bicycle facilities, and overlooked how different design elements of dedicated bicycle facilities on roundabouts affect cyclists' perceived safety. Furthermore, previous studies did not investigate the relationship between perceived safety and comfort. To address these gaps, this study aims to better understand the factors contributing to cyclists' perceived safety and comfort at roundabouts. A total of 239 complete responses from cyclists to a stated preference survey were collected. A bivariate random effect ordered probit model was used to simultaneously model cyclist's perceived safety and comfort as a function of behavioural factors and infrastructural design elements. The results revealed that roundabouts where cars must yield to cyclists and with fewer vehicular entrance points were perceived by cyclists as safer and more comfortable. Also, cyclists' place of residence (in or outside the Netherlands), their likelihood to commit traffic violations, their recent crash history, and the type of bicycle they use, significantly affect their perceived safety. To improve cyclists' perceived safety and comfort in urban environments, it is recommended to ensure bicycle yielding priority, design dedicated bicycle facilities on roundabouts and maintain uniformity in bicycle infrastructure design.

The Operational Design Domain (ODD) defines the conditions under which automated driving and driver-assistance systems are expected to operate. This study evaluates the ODD of a camera-based Lane Support System (LSS) using direct Mobileye 6.0 lane-detection quality outputs. A large-scale hybrid factorial–observational field design covered 6 different Light × Weather combinations across 9,351 road sections on two-lane rural roads with wide variability in lane-marking retroreflectivity (RL) and road horizontal alignment and cross section characteristics.Statistical and machine-learning classification models were calibrated and compared to analyze the relationships between lane-marking quality and environmental, road, and traffic features. An AutoML-LightGBM pipeline with SMOTE-based class-imbalance treatment achieved the best accuracy of 0.81. SHAP analysis identified low RL, rain, night conditions, narrow lanes, and high curvature as contributors to critical detection conditions. Because standard ML is optimized for prediction rather than causal inference, Double Machine Learning was added to estimate adjusted effects from observational data. Higher RL, higher speed, and wider lanes were associated with better expected Mobileye quality scores, whereas rain, night conditions, and higher curvature were associated with lower detection quality. SHAP dependence and conditional SHAP analyses supported the identification of maintenance-mitigable infrastructure constraints and harder environmental/geometric ODD limits. One practical result is that RL transitions from low-quality detection mainly occur within 120–150 mcd/(m²·lx) across the majority of environmental and physical conditions, although this transition is less evident under sharp curvature or rain.

Autonomous electric vehicles (AEVs) will increasingly characterize future motorized traffic in urban areas. These vehicles are often silent and can be driverless. Therefore, understanding the interactions between these vehicles and vulnerable road users (VRUs) is crucial for traffic safety. This study explores the impact of an additional auditory alert system and external human-machine interfaces (eHMIs) on AEVs on cyclists' interaction behavior, comfort, and safety under varying vehicle–signal conditions and environmental noise levels. This study employed immersive Virtual Reality (VR) technology to simulate cycling across 18 traffic scenarios, using a within-subjects design with 40 participants. These scenarios included two vehicle types: autonomous conventional vehicles (ACVs) and autonomous electric vehicles (AEVs), two levels of ambient noise levels (quiet residential areas; noisier streets), and three levels of eHMI signal trigger distances (15, 20, 25 m). Data regarding cycling behavior (e.g., response distance, speed variation, gaze direction) and cyclists' perceptions (e.g., realism, simulator sickness, presence, trust, and perceived safety) were collected through VR and post-experiment questionnaires, respectively. We found that compared to vehicles equipped only with an eHMI, AEVs with auditory alerts significantly enhanced cyclists' perceptions and responses, with cyclists detecting the vehicles earlier and responding faster. Subjective feedback also indicated a stronger sense of safety and comfort, such as smoother deceleration that indicated natural responses and reduced stress. Participants also reported a more comfortable interaction experience with AEVs with auditory alerts. These findings provide evidence that equipping AEVs with additional auditory alerts improves the safety and comfort of cyclists in their interactions with AEVs in urban environments. Therefore, policymakers are advised to consider this when setting regulations for the deployment of AEVs in urban areas.

As automated vehicles (AVs) become increasingly popular, the question arises as to how cyclists will interact with such vehicles. This study investigated (1) whether cyclists spontaneously notice if a vehicle is driverless, (2) how well they perform a driver-detection task when explicitly instructed, and (3) how they carry out these tasks. Using a Wizard-of-Oz method, 37 participants cycled a designated route and encountered an AV multiple times in two experimental sessions. In Session 1, participants cycled the route uninstructed, while in Session 2, they were instructed to verbally report whether they detected the presence or absence of a driver. Additionally, we recorded participants’ gaze behaviour with eye-tracking and their responses in post-session interviews. The interviews revealed that 30% of the cyclists spontaneously mentioned the absence of a driver (Session 1), and when instructed (Session 2), they detected the absence and presence of the driver with 93% accuracy. The eye-tracking data showed that cyclists looked more frequently and for longer at the vehicle in Session 2 compared to Session 1. Additionally, participants exhibited intermittent sampling of the vehicle, and they looked at the area in front of the vehicle when it was far away and towards the windshield region when it was closer. The post-session interviews also indicated that participants were curious, but felt safe, and reported a need to receive information about the AV's driving state. In conclusion, cyclists can detect the absence of a driver in the AV, and this detection may influence their perception of safety. Further research is needed to explore these findings in real-world traffic conditions.

As automated vehicles (AVs) become more common, it is important to understand how human-driven vehicles (HDVs) would interact with them. This research investigated HDV gap acceptance behavior in mixed traffic with AVs at a priority intersection, focusing on how mixed traffic factors affect this behavior and overall traffic efficiency. Using a driving simulator, four scenarios were tested by varying AV driving style (less defensive, more defensive, and HDV-like) and AV recognizability (distinguishable or not from HDVs). Gap acceptance models were estimated based on the collected trajectory data. These models were then integrated into the SUMO microscopic traffic simulation platform, where a T-intersection network was set up. Simulation runs varied based on AV driving style, recognizability, penetration rate (0-75% in 25% increments), and whether HDV behavioral adaptation was considered. The results indicated increased minor road vehicle delays with higher AV penetration rates. Recognizable less defensive AVs, and more defensive AVs with high penetration rates caused the largest delays for minor road vehicles compared to other conditions. Ignoring behavioral adaptation led to a delay underestimation of up to 75% for minor road vehicles. In conclusion, there is behavioral adaptation in gap acceptance of HDVs in mixed traffic environments. Taking into account the behavioral adaptation is essential for accurately assessing traffic efficiency in mixed traffic conditions, and guiding AV deployment policies.

By improving road safety, traffic efficiency, and overall mobility, automated vehicles (AVs) hold promise for revolutionizing transportation. Despite the steady advancement in high-level AVs in recent years, the transition to full automation entails a period of mixed traffic, where AVs of varying automation levels coexist with human-driven vehicles (HDVs). Making AVs socially compliant and understood by human drivers is expected to improve the safety and efficiency of mixed traffic. Thus, ensuring AVs’ compatibility with HDVs and social acceptance is crucial for their successful and seamless integration into mixed traffic. However, research in this critical area of developing socially compliant AVs (SCAVs) remains sparse. This study carries out the first comprehensive scoping review to assess the current state of the art in developing SCAVs, identifying key concepts, methodological approaches, and research gaps. An informal expert interview was also conducted to discuss the literature review results and identify critical research gaps and expectations toward SCAVs. On the basis of the scoping review and expert interview input, a conceptual framework is proposed for the development of SCAVs. The conceptual framework is evaluated via an online survey targeting researchers, technicians, policymakers, and other relevant professionals worldwide. The survey results provide valuable validation and insights, affirming the importance of the proposed conceptual framework in tackling the challenges of integrating AVs into mixed-traffic environments. Additionally, future research perspectives and suggestions are discussed, contributing to the research and development agenda of SCAVs.

The study provides preliminary results from a case comparison of road safety management in three African countries (Tanzania, Ghana, Zambia) with three EU countries, all with a great track record of excellence in traffic safety and practicing Safe Systems principles (Norway and Netherlands & Sweden), based on document analysis and qualitative interviews. Norway, Sweden, and The Netherlands are early adopters of what has been termed the Safe System Approach (termed "Sustainable safety"in the Netherlands). Norway and Sweden have the highest road safety level in world. The objectives of the study are to: 1) Examine the alignment with Safe System principles in the road safety management systems in each country, and 2) Discuss possible policy implications. The study is based on document analyses and focus group interviews with road safety experts (n=73) in the six countries. The European countries' road safety management systems are mainly in line with the Safe Systems principles for road safety management. In the three African countries, we find an insufficient systematic approach and a lower level of implementation of existing plans, mostly related to insufficient data on accidents, low institutional road safety influence and lacking funding. We discuss possible policy implications for the three African countries.

This paper investigates the development of self-aware mechanisms for automated vehicles, introducing the notion of an automation state estimation system. This system is capable to understand its capabilities in a given context, and can leverage that knowledge to estimate the current and near-future automation performance based on internal metrics, as well as external, static (e.g. lane geometry) and dynamic environmental elements (e.g. traffic and weather information). From an application perspective, we consider automation state estimation in the scope of automation mediation, as part of a broader and holistic mediation system, with the goal to tackle challenging aspects related to transitions of control, mode confusion, and driver engagement. We used real-world data for system design, and implemented the proposed automation estimation system in a prototype vehicle. Based on 70 hours of real-world driving, we also validated the performance of the automation state estimation for automation mediation purposes.

Sharp curves in freeways are known to be unsafe design elements since drivers do not expect them. It is difficult for drivers to estimate the radius of a curve. Therefore, drivers are believed to use other cues to decelerate when approaching a curve. Based on previous successful experiences of driven speeds in curves, drivers are thought to have built expectations of safe speeds given certain cues, minimalising risks. This research employs a Bayesian Belief Network to model driver expectations using measured speeds in 153 curves and data on the characteristics of the curve approaches. This model mimics expectations as the probability of measured speeds given certain cues. Using Bayes theorem, prior beliefs on safe speeds are updated towards a posterior belief when a new cue is observed during curve approach. We refer to this posterior belief as expected safe speed. Drivers are assumed to adjust their operating speed if it does not match their expected safe speed. The model shows that the visible deflection angle has a large influence in setting the expectations of a safe speed for an upcoming curve. In addition, the preceding type of roadway and the number of lanes are both important cues to set a driver's expectations of a safe speed. Speed and warning signs are shown to be interdependent on the road scene and hence have less influence in setting expectations. This research shows that design and safety assessment of freeway curves should be considered aligned with the road scene upstream of the curve.

Dedicated Lanes (DLs) have been proposed as a potential alternative for the deployment of Connected and Automated Vehicles (CAVs) to facilitate platooning and increase motorway capacity. However, the impact of the presence and utilization policy of such a lane on drivers’ preference to use automation and their behaviour has not yet been thoroughly investigated. In this study, a driving simulator experiment is conducted, where participants drive a CAV in the presence of a DL with different utilization policies. Drivers have the possibility to choose between driving in an automated mode or in a manual mode. In automated mode they could adjust the driving speed and time headway and initiate automated lane changes. Two utilization policies were examined: mandatory versus optional use of DLs when driving in an automated mode. The impact of the presence and utilization policy of the DL on drivers’ preference to use automation and their behaviour in car-following and lane changing are investigated. The study found that while the presence of a DL does not increase drivers’ preference for automation use, it encourages drivers to utilize the DL more when the utilization policy is mandatory (i.e., drivers can only use automation mode when driving on this lane). Furthermore, drivers are more conservative in automated mode and when driving in mixed traffic. However, they perform closer car-following and merge into smaller gaps when driving on DLs which on one hand can increase the capacity of the DLs, but on the other hand can increase the risk of collisions. These results are useful for road operators, and in setting-up a more realistically traffic simulation studies.

This study utilized Virtual Reality (VR) experiments to investigate pedestrian-autonomous vehicle interaction in shared spaces. In the VR experiment, pedestrians attempt to cross the road under different conditions, including the presence of another pedestrian, different external Human-Machine-Interfaces, AV driving styles, and road conditions. We employed an innovative VR setup that enabled two pedestrians to interact in real time with physical movements within an immersive VR environment. Overall, we found that the presence of multiple pedestrians significantly influenced pedestrian movement dynamics during road crossing. Additionally, the relative standing position had a significant impact on the distant pedestrians regarding time before crossing and vehicle-gazing behavior. While previous studies predominantly focused on pedestrian-AV interaction with a single pedestrian, this study takes an important step forward in terms of theory, methods, and relevance by considering interactions between multiple pedestrians and AVs. The findings establish a basis for further exploration of pedestrian-AV interaction in shared space.

Automated vehicles (AVs) may require the implementation of an external human-machine interface (eHMI) to communicate their intentions to human-driven vehicles. The optimal placement of the eHMI, either on the AV itself or as part of the road infrastructure, remains undetermined. The current driving simulator study investigated the effect of eHMI positioning on human driving behaviour, during the approach and execution of right turns at T-intersections. Forty-three participants drove under three conditions: absence of eHMI, eHMI on the AV (eHMIv), and eHMI integrated into the infrastructure (eHMIi). Participants encountered AVs that either yielded or did not yield to their vehicles. The results regarding the placement of the eHMI showed that both concepts are advantageous, but for different reasons. eHMIv was appreciated because implicit and explicit communication are congruent, although the AV must first be visually identified to respond to it. eHMIi was appreciated because a familiar cue is always at a known location in the environment; as a result, participants braked earlier for the intersection and came less close to the AV (which can be interpreted as a safety advantage or an efficiency disadvantage). Although there are limitations to the current driving simulator study, this research provides important insights into the fundamental question of how information placement affects drivers’ visual attention demands and driving behaviour, topics that are important in view of the development of future cities.