Lane change is a complex behaviour involving subtle interactions among road users. Providing external human-machine interfaces (eHMIs) may improve the safety of lane-changing events. However, previous studies on eHMI mostly focused on the interaction between autonomous vehicles and pedestrians. As a first attempt, we investigated the yielding behaviour of drivers in lane-changing scenarios when different kinds of eHMIs regarding the intentions of the cutting-in vehicles (i.e., command, polite and explanatory) are provided. In a driving simulation experiment with 32 participants, we found that all three eHMIs increased yielding rates and minimum time to collision (minTTC) compared to the baseline condition without eHMI, with the polite eHMI yielding the best results. Regarding subjective evaluation, polite eHMIs were also perceived as having the highest usability. This study underscores the effectiveness of explicitly expressing lane-changing intentions through eHMIs and demonstrates that the eHMI design can influence driver behaviour, usability perception, and traffic safety.

Learning in domains involving complex motor skills, such as performance driving, often requires feedback that is timely, personalized, and actionable. Yet many drivers rely on video and telemetry data to review their performance without guidance. We explore how conversational AI can support post-drive reflection by integrating LLM-generated coaching into an interactive review interface. In an exploratory within-subjects simulator study (n=16), participants completed laps under two conditions: one with video and data visualizations alone, and another with the same tools augmented with a conversational interface that provided verbal feedback after each lap. Conversational feedback supported short-term improvements in lap time, average speed, and steering control, and was rated as more useful and satisfying—though it also elicited slightly higher nervousness. These results suggest that conversational AI can make post-drive feedback more interpretable and actionable, particularly for drivers reviewing performance data in high-skill contexts like performance driving.

Among all types of on-road crashes, rear-end collisions dominate, which is closely related to drivers’ car-following (CF) behaviours. CF behaviours are believed to be based on the states of the directly leading vehicle, though experienced drivers can predict the upstream traffic flow variation based on subtle cues and act in advance. Thus, providing beyond-line-of-sight (BLOS) information of upstream traffic flow to novice drivers may improve CF performance. This study proposed two visualizations for BLOS information in CF events, i.e., braking states of the indirect leading vehicle (ILV-HMI), and the upstream traffic flow information (UTF-HMI). A driving simulator experiment with 24 novice drivers assessed the effectiveness of the visualizations. Results show that ILV-HMI improved safety margins, reduced cognitive load, and enhanced usability compared to baseline without BLOS information and UTF-HMI. These findings highlight the advantages of V2V-based BLOS information in improving novice drivers’ CF performance and enhancing traffic safety.

The emergence of automated vehicles (AVs) introduces new challenges to human-vehicle interactions, especially in conditional automated driving. This study presents different head-up display designs as the human-machine interface (HMI) to convey uncertainty to AV users. It investigates the impact of such designs on drivers’ physiological responses–via electrodermal activity data–and subjective evaluations of cognitive workload during the automated drive. A between-subjects driving simulator experiment (N=187) was conducted to examine four conditions: baseline (no HMI), a progressive colour-based Guardian Angel display, a text-based interruption, and a combination approach. The results showed significant effects of the presence of the Guardian Angel display interventions on physiological arousal associated with cognitive workload. However, the subjective ratings showed no difference across conditions. These findings indicate that the designed displays can trigger physiological responses without affecting perceived workload. It offers insights into HMI design to balance driver awareness and cognitive demand in automated driving.

Automated vehicles (AVs) reached technological maturity and will soon arrive on streets as traffic participants. Human traffic participants such as drivers, pedestrians, or cyclists will be increasingly confronted with the presence of AVs within their environment, not necessarily knowing or understanding what to expect and how to interact with them. Although AVs are designed to act safely, effective interaction in mixed traffic scenarios will depend on successful communication, interaction, or even negotiation beyond static rules and regulations. Prosocial behavior, such as yielding one’s right of way, will be needed to resolve unclear traffic situations or foster traffic flow. However, what are the characteristics of such prosocial behavior, and how to measure this not only for automated vehicles but for all road users? Here, we describe a new scale to measure perceived social behavior in urban traffic scenarios. Through an online survey on N = 318 individuals and a validation study, we developed the Situational Prosocial and Aggressive Behavior in Traffic Scale and assessed it psychometrically.

Numerous driving simulator systems are available and are continuing to be developed. However, we believe many simulator offerings are built around what is technically possible rather than what is useful to the researchers that might use such systems. This points to a critical need to understand what makes a driving simulator practical and effective for automotive interface design researchers. To remedy this shortcoming, we conducted video interviews with 15 industry and academic researchers engaged in automotive interface design research. We transcribed and performed thematic analysis on the data collected to better understand the different ways that researchers are using driving simulators, and what challenges they still face. We identified needs across three broad dimensions including: (1) Participant Experience, (2) Research Needs, and (3) Operationalization Requirements. By categorizing these needs, we aim to inform the development of future simulation tools that are more accessible to researchers from diverse backgrounds.

Safety in roadway work zones remains a major challenge: accidents often stem from unpredictable driver and worker behavior, underscoring the need to understand their interactions and perceptions. Advances in virtual reality and high-fidelity simulators like CARLA enable studying behaviors in risky scenarios without real-world risk. This paper introduces VR-WISE, a VR-based driving simulation platform that captures rich driver behavior data in work zones through full-scene customization, animated roadway workers, and integrated eye-tracking and biometric sensors. VR-WISE supports rapid generation of diverse work-zone scenarios and detailed multimodal data collection to analyze driver perceptions and responses to dynamic roadside activities. A user study across three scenarios demonstrates the platform’s effectiveness. Driver perception was evaluated using gaze metrics (e.g., duration and fixation ratios) and physiological signals (heart rate and electrodermal activity), providing a comprehensive understanding of attention in complex work-zone environments.

How do drivers perceive risk? Understanding what situations and factors cause drivers to perceive situations as critical can improve our understanding of road user behavior and inform automated driving technology. To investigate the factors that shape drivers’ risk perception, we conducted an eye-tracking study with 27 participants who watched dashcam videos and continuously rated the perceived risk of various driving situations. Using the resulting dataset, we developed a computer vision-based machine learning approach that generates explainable predictions of perceived risk from video and eye-tracking data. Our SHAP analysis reveals that the proximity of objects and number of cars in a scene are the most significant contributors to perceived risk. Most interestingly, while people tend to sample similar objects in critical situations, their risk perception remains highly personal making visual sampling behavior a weak predictor of perceived risk. Overall, our explanations reveal non-linear insights beyond previous work, suggesting that risk perception is not only shaped by visual input, but primarily by cognitive processes which is in line with theoretical models of situation awareness.

Advanced driver assistance systems (ADAS) with increasing automation maturity and availability in urban contexts are entering the market. Meanwhile, the situational context has been identified to play a crucial role in system comprehension and usage, yet its subcomponents and their relation to system comprehension remain an open research question. To gain insights in the role of the situation complexity regarding subjective system comprehension and different methodological aspects, this study applies a mixed quantitative and qualitative approach, focusing on signaled intersections as an exemplary scenario. An on-road study with forty-six participants was conducted, involving six traffic light scenarios (all experienced twice). Results indicate that while comprehension was generally high, the situational context, including environmental and traffic-related factors, affected subjective system understanding. The proposed approach sheds light on the role of mixed methods in ADAS research, which may provide insights for system developers and suggestions for user training content.

Social scientists have argued that autonomous vehicles (AVs) need to act as effective social agents; they have to respond implicitly to other drivers’ behaviors as human drivers would. In this paper, we investigate how contingent driving behavior in AVs influences human drivers’ experiences. We compared three algorithmic driving models: one trained on human driving data that responds to interactions (a familiar contingent behavior) and two artificial models that intend to either always-yield or never-yield regardless of how the interaction unfolds (non-contingent behaviors). Results show a statistically significant relationship between familiar contingent behavior and positive driver experiences, reducing stress while promoting the decisive interactions that mitigate driver hesitance. The direct relationship between familiar contingency and positive experience indicates that AVs should incorporate socially familiar driving patterns through contextually-adaptive algorithms to improve the chances of successful deployment and acceptance in mixed human-AV traffic environments.

As vehicles with several levels of automation become increasingly common, there is an increase in incidents involving the misuse of Driving Automation Systems (DAS). The manner in which drivers interact with DAS indicates that the problem extends beyond UI design. We investigate how drivers’ perceptions and expectations affect the understanding and consequent usage of DAS. The study employed a Wizard-of-Oz approach to simulate a vehicle with a Level 2 and Level 3 DAS on a public highway. Sixteen participants were exposed to the two driving modes and two distinct UIs. Observations, think-aloud protocols, and in-depth interviews documented their interaction with the different DAS. Irrespective of the UI, various errors were detected, including omission, commission, and mode confusion. Deeper investigation into the sources led to the conclusion that drivers’ preconceptions of the DAS were a major contributor, resulting in misuse. This highlights the need to look beyond UI design as a sole solution to address driver-vehicle interaction.

Legislation in the USA will soon require that electric vehicles (EVs) display battery degradation in the instrument cluster as “state of health” (SOH), the percentage of the battery’s original capacity. However, the extent to which consumers understand SOH degradation patterns is not known. In an initial study with vehicle owners, we find preliminary evidence for a ‘SOH illusion,’ wherein people expect linear rates of EV battery degradation over time even though batteries degrade non-linearly. Additionally, a third of participants incorrectly confused SOH with a battery’s remaining usable life, demonstrating some misunderstanding of SOH among vehicle owners. In a follow-up study we find that framing SOH information with different chart types and legends reduces linear degradation assumptions and aligns people’s expectations. We discuss implications for the design of SOH representations in user interfaces that vehicle UI designers could employ to promote better EV battery understanding.

Micromobility vehicles, such as e-scooters, provide ecological and financial advantages over automotive transportation. However, as with car drivers, micromobility users often perform non-driving related tasks (NDRTs), interacting with stereo controls or navigation tasks, which can lead to accidents. It remains unclear what control interfaces are appropriate and safe for micromobility. We evaluated six interface modalities for NDRTs and conducted a within-subjects study with 35 participants (yielding N=210 observations) in an e-scooter simulator to compare modality safety and preferences. Our results align with existing work on gaze and tactility in the automotive NDRTs context. However, unique to e-scooters, interfaces that required users to alter their grip on the handlebars were less preferred as they compromised stability. Social comfort also emerged as a critical factor due to concerns about public visibility.

This work aims to encourage the design of safer, more socially acceptable interfaces for e-scooters and other emerging micromobility vehicles.

Cyclists face significant risks from vehicles that overtake too closely. Through crowdsourcing (N = 200) and driving simulator (N = 20) experiments, this study examines driver behaviour in seven scenarios: laser projection, road sign, road marking, car projection, centre line and side line markings (baseline), cycle lane and no road markings. Crowdsourced participants consistently underestimated overtaking distances, particularly at wider gaps, despite feeling safer with greater distances. The simulation results showed that drivers maintained an average passing distance of 3.4 m when not constrained by traffic, exceeding the 1.5 m law of the European Union. However, interventions varied in effectiveness: while laser projection was preferred, it did not significantly increase passing distance. In contrast, a dedicated cycle lane and a solid centreline led to the greatest improvements. These findings highlight the discrepancies between perceived and actual safety and provide insight for policy interventions to enhance cyclist protection in the EU.

Autonomous mobility systems increasingly operate in environments shared with animals, from urban pets to wildlife. However, their design has largely focused on human interaction, with limited understanding of how non-human species perceive, respond to, or are affected by these systems. Motivated by research in Animal-Computer Interaction (ACI) and more-than-human design, this study investigates animal interactions with autonomous mobility through a multi-method approach combining a scoping review (45 articles), online ethnography (39 YouTube videos and 11 Reddit discussions), and expert interviews (8 participants). Our analysis surfaces five key areas of concern: Physical Impact (e.g., collisions, failures to detect), Behavioural Effects (e.g., avoidance, stress), Accessibility Concerns (particularly for service animals), Ethics and Regulations, and Urban Disturbance. We conclude with design and policy directions aimed at supporting multispecies coexistence in the age of autonomous systems. This work underscores the importance of incorporating non-human perspectives to ensure safer, more inclusive futures for all species.

Pedestrian crossing behaviour varies globally. This study analyses dashcam footage from the CROWD dataset, covering 233 countries and territories, to examine crossing initiation time, crossing speed, and contextual variables, including detected vehicles, traffic mortality, GDP, and Gini coefficient. Qatar had the longest mean crossing initiation time (6.44 s), while China exhibited the fastest crossing speed (1.69 m/s). On average, worldwide, pedestrians exhibited a crossing initiation time of 3.18 s and crossing speed 1.20 m/s. Crossing speed and crossing initiation time are negatively correlated (r = −0.18), indicating slower crossings after longer hesitation. Crossing speed is negatively correlated with Gini coefficient (r = −0.19) and positively correlated with traffic mortality (r = 0.18). Similar crossing times in countries with different infrastructures, such as Bangladesh (3.42 s) and the Netherlands (3.40 s), underscore the complex interaction between infrastructure and behavioural adaptation. These findings emphasise the importance of culturally aware road design and the development of adaptive interfaces for vehicles.

A vehicle’s dash-based Human-Machine Interface (HMI) provides critical information to drivers. However, the location of these displays can take drivers’ visual attention away from the forward view and compromise safety. As vehicle automation becomes increasingly common, its impact on drivers’ visual attention to dash-based HMI remains under-explored. Moreover, drivers tend to engage more frequently in non-driving-related tasks during automation, but how the cognitive load imposed by these tasks affects drivers’ inspection of HMI displays is unclear. This driving simulator study examined how partial automation and cognitive load (imposed by a 2-back task) influence drivers’ visual attention to dash-based HMI containing speed and automation status information (N=41). Results showed that increased levels of automation and cognitive load additively reduced drivers’ visual attention to the dash area. Drivers prioritized inspecting the speedometer over the automation status information across all conditions. Our findings provide important implications for HMI design in automated vehicles.

Future high-level autonomous vehicles (AVs) will enable drivers to engage in non-driving-related tasks (NDRTs) during autopilot. Occasionally, an in-vehicle agent may need to notify drivers of important, yet not urgent, information. Through a four-session interruptibility study on a desktop autonomous driving simulator, we investigated how drivers assess their availability to receive notifications by rating moments as good or bad for interruption. Our results suggest drivers fall into four notification availability groups: always available, prioritizing NDRTs, task-content dependent, and mental-state dependent. Using multimodal behavioral data of the participants and vehicle data from the simulation, we trained a proof-of-concept classification model to determine the appropriate timing to send non-emergency notifications to drivers doing NDRTs. Head pose and gaze direction data from the eye tracker were crucial in the predictions. Based on our quantitative modeling and qualitative observation, we discuss the feasibility of notification timing prediction in the real world and design considerations from individual, task, and context perspectives.

In this simulator study, we adopt a human-centered approach to explore whether and how drivers’ cognitive state and driving environment complexity influence reliance on driving automation features. Besides, we examine whether such reliance affects driving performance. Participants operated a vehicle equipped with adaptive cruise control (ACC) in a simulator across six predefined driving scenarios varying in traffic conditions while either performing a cognitively demanding task (i.e., responding to mental calculations) or not. Throughout the experiment, participants had to respect speed limits and were free to activate or deactivate ACC. In complex driving environments, we found that the overall ACC engagement time was lower compared to less complex driving environments. We observed no significant effect of cognitive load on ACC use. Furthermore, while ACC use had no effect on the number of lane changes, it impacted the speed limits compliance and improved lateral control.

Automated driving systems, particularly at SAE Level 3, present new challenges in managing driver attention to ensure smooth transitions from automated to manual control. This paper reports on a qualitative investigation of vestibular cues—implemented via subtle deceleration events—as a form of a dynamic Human-Machine Interface (dHMI) that subtly “nudges” a user’s attention away from a non-driving related task (NDRT) and towards the driving environment. Conducted as a test-track study (N=25), we explore awareness, acceptance, and design considerations related to these cues. Findings reveal that while participants showed positive attitudes toward vestibular nudges as safety features, they were unable to differentiate nudges from necessary vehicle deceleration during automated driving. The study reveals how drivers interpret the implicit interaction with the dHMI during realistic NDRT and potential limitations. The study highlights the need for multimodality HMI approaches and customisation to optimise user experience in conditional automated vehicles.

This study investigated rear-seat passengers’ motion discomfort when displaying (in)congruent visual motion while playing an immersive Augmented Reality (AR) racing game on a headrest-mounted screen. 29 players participated in two, 30-minute drives involving urban and highway roads. In the Synchronized Game (SG), the gameplay elements and video background were live-streamed from the vehicle’s cameras and sensors, creating a congruent sensory environment. In the Desynchronized Game (DG), the gameplay was pre-recorded, resulting in incongruent visuo-vestibular motion where the visual motion in the game did not always align with that of the vehicle. Motion discomfort was measured at 2-min intervals using the MIsery SCale (MISC) and a thermal camera measuring participants’ forehead temperature. The results showed that the SG condition led to significantly lower motion discomfort compared to the DG condition. These findings suggest that immersive games that incorporate real-time vehicle motion can help to mitigate motion discomfort by providing congruent visual-vestibular input.

Control elements with active haptic feedback have become established in modern automotive user interfaces, but numerous media reports and studies indicate customer dissatisfaction with the current design. To investigate the customer preference that has not been well-characterized so far and to deduce whether differences in preference can be linked to a driving task or customer attributes, a Preliminary-Study with adjective rating, an Expert-Study with pairwise comparison, and a Customer-Study with an additional real driving task were conducted. The results show different customer preference groups, with the vast majority favoring short haptic feedback within 13.6 ms and 20.6 ms length. A driving task does not influence preference, nor is the preference affected by attributes such as gender, age, or thumb size. These findings can be used to optimize active haptic feedback according to customer preferences. As a result, well-designed haptics can increase customer satisfaction and the perceived value of automotive controls.

Haptic technology has diverse applications in automated vehicles (AVs), yet research lacks a holistic view of its role in user experience and its inclusive potential for blind and low-vision (BLV) users. This paper reviews state-of-the-art haptic interfaces in AVs, examining technological foundations, applications, user experience considerations, and adaptability for BLV accessibility. We found that existing haptic interfaces are primarily designed for drivers during automation transitions, emphasizing effectiveness over hedonic experience. There is a knowledge gap in how such interfaces can improve BLV user experience in fully automated vehicles. We propose a shift from haptic-supported automated driving to Haptic-Augmented AV Experiences, advocating for more inclusive and adaptive haptic interactions beyond traditional driver-centric paradigms.

Technology Acceptance Models (TAMs) offer valuable insights into Autonomous Vehicle (AV) acceptance, yet little research translates these factors into design requirements for more trustworthy AVs. Self-Organising Map (SOM) analysis of 283 survey participants’ responses revealed distinct majority acceptor and minority rejector profiles, with notable differences in all acceptance factors, with facilitating condition, technology attitude, perceived safety, and AI robustness serving as largest determinants of AV acceptance; anxiety, whilst significantly different was least effective. Rejectors exhibited ‘autonomy sensitivity’, with increased demands for more customisation, redundancy, and experientiality in FAVs compared to PAVs, while acceptors maintained stable design preferences. These findings inform a Profile–Context Interaction (PCI) framework for dual-adaptive interfaces. The PCI framework recommends four design quadrants, Acceptor–PAV, Acceptor–FAV, Rejector–PAV, and Rejector–FAV to tailor interface features to both user profiles and autonomy levels, thereby bridging a gap between acceptance theory and actionable design.

Trust plays a pivotal role in shaping driver interactions with autonomous vehicles (AVs), particularly in Level 3 systems that require timely human intervention during takeover requests (TORs). While prior studies have examined trust and TOR performance independently, limited work has systematically explored their intersection. This review addresses that gap by investigating how trust is formed, miscalibrated, and recovered during TOR events. Key factors such as TOR timing, modality, environmental complexity, system transparency, and individual differences are analysed in relation to both trust and takeover performance. Current trust models and measurement techniques are critically evaluated, highlighting limitations of static approaches and the emerging value of real-time trust monitoring. Guided by PRISMA 2020, a systematic literature review was conducted to screen and synthesise relevant studies. The review identifies challenges and offers design recommendations for adaptive, trust-sensitive AV systems that foster calibrated trust, ultimately improving safety, driver readiness, and overall user acceptance in automated driving contexts.

Uncertainty is an inherent aspect of autonomous vehicle (AV) decision-making, yet it is rarely communicated to pedestrians, which hinders transparency. This study investigates how AV uncertainty can be conveyed through two approaches: explicit communication (confidence percentage displays) and implicit communication (vehicle motion cues), across different confidence levels (high and low). Through a within-subject VR experiment (N=26), we evaluated these approaches in a crossing scenario, assessing interface qualities (visibility and intuitiveness), how well the information conveyed the vehicle’s level of confidence, and their impact on participants’ perceived safety, trust, and user experience. Our results show that explicit communication is more effective and preferred for conveying uncertainty, enhancing safety, trust, and user experience. Conversely, implicit communication introduces ambiguity, especially when AV confidence is low. This research provides empirical insights into how uncertainty communication shapes pedestrian interpretation of AV behaviour and offer design guidance for external interfaces that integrate uncertainty as a communicative element.

A calibrated trust level is essential for the safe use of automated systems. In automated driving, overtrust can reduce drivers’ monitoring behavior and delay takeover times, which poses significant safety risks. This motivates the need for continuous, objective trust assessment to enable real-time system adaptations. Prior research has identified eye-tracking as a promising approach. Therefore, this study examines the longitudinal evolution of dynamic learned trust as well as its relationship with visual attention. Given that mental models influence both trust and visual attention, their role in this process is also examined over time. In a longitudinal study, twenty-three participants repeatedly operated an automated vehicle in real traffic while their visual attention was recorded via the vehicle’s built-in driver monitoring camera. The study indicates that the mental model is a key factor within the field of trust evolution and visual attention. This work contributes to advancing trust measurement in automated driving.

Adoption of Fully Autonomous Vehicles (FAVs) depends on trust, which is defined as confidence in a vehicle’s dependability, safety, and predictability. In cooperative driving scenarios, trust must exceed ego vehicles to include other autonomous vehicles and their coordination. This is challenged by unexpected multi-agent interactions, diminishing human control, and limited system transparency. We hypothesize that enhancing transparency by providing information about ego vehicle, other cooperative vehicles, and road conditions can foster trust. This is achieved by visualizing vehicle-to-everything (V2X) information via augmented reality (AR) interfaces. To test this in a safe environment, we conducted a within-subjects experiment in a Virtual Reality (VR) driving simulator with AR overlays. Participants experienced three interface concepts: (A) no transparency, (B) system-level transparency (ego vehicle intentions only), and (C) environment-level transparency (cooperation intentions, planned paths, and infrastructure). Results show that environment-level transparency, despite the higher cognitive workload, enhanced trust in both ego and cooperating FAVs.

This paper presents a virtual reality (VR) study that examines how multiple road users jointly interact with an autonomous vehicle (AV) in complex traffic scenarios. Moving beyond dyadic studies (e.g., AV-pedestrian or AV-passenger), our multi-user setup simulates ambiguous all-way stop intersections involving a pedestrian, a human driver in a conventional vehicle, and a passenger in an AV, all interacting simultaneously with the AV. We investigated how users perceive and respond to two distinct types of AV behaviors: an efficient AV that proceeds as soon as it is safe to do so, and a prosocial AV that yields to others before entering the intersection. Sixteen groups of three participants (N=48) took part in the study, with each group interacting with a single AV type across four ambiguous traffic scenarios. Our findings show that even simple AV behavior logics can meaningfully shape crossing negotiation dynamics and highlight how trust and perception can vary across different user roles. We conclude by discussing how our methods and insights can inform the research and design of AV interactions in complex multi-agent traffic environments.

Anthropomorphic design in intelligent vehicle agents (IVAs) is crucial for driving safety and user experience. Cultural background may shape user preferences, as evidenced by Chinese car manufacturers offering more anthropomorphic IVAs (e.g., physical robots, human-like virtual agents) than their Western counterparts. While prior research has examined cross-cultural differences in visual anthropomorphism, behavioral anthropomorphism remains understudied. Here we evaluated the performance of eight IVAs (five from Chinese brands, three from Western brands) in responding to user requests and their social repair behaviors (e.g., apology and promise) following request failures. Overall, Chinese and Western IVAs did not differ in their corrective responses or likelihood of employing repair behaviors. However, Chinese IVAs were more likely to use combined behaviors rather than single ones and to incorporate intimacy expressions in their repair behaviors. Our findings highlight cultural design nuances in behavioral anthropomorphism, with implications for the culturally adaptive design of IVAs.

Road traffic is a social context where interactions between road users influence their emotional experiences and behavior. However, with the advancement of driving automation, traditional forms of human-to-human social interaction in traffic will be challenged. While previous research has explored new forms of communication, such as eHMIs for mixed traffic, less is known about the influential factors in decision-making for such communications and their experiential outcomes. To address this gap, we conducted a scoping review of 114 studies and proposed the Social Traffic Interaction Framework (STIF), which describes social interaction among road users across five phases: situational assessment, decision, action, acknowledgment, and affective outcome. To assess the real-world applicability of the STIF framework, 25 traffic scenarios were collected through interviews and analyzed according to the framework. Our findings provide a theoretical foundation for understanding and designing social interaction in mixed traffic that promotes safer, more empathetic, and emotionally positive experiences.