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# Trust and Safety in Autonomous Vehicles: Evaluating Contextual Visualizations for Highlighting, Prediction, and Anchoring

Thi Thanh Hoa Tran, Etienne Peillard, James Walsh, Guillaume Moreau, Bruce Thomas

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Thi Thanh Hoa Tran, Etienne Peillard, James Walsh, Guillaume Moreau, Bruce Thomas. Trust and Safety in Autonomous Vehicles: Evaluating Contextual Visualizations for Highlighting, Prediction, and Anchoring. ICAT-EGVE2025, Dec 2025, Karlskrona, Sweden. 10.2312/egve.20251351. hal-05396377

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# Trust and Safety in Autonomous Vehicles: Evaluating Contextual Visualizations for Highlighting, Prediction, and Anchoring

Thi Thanh Hoa Tran<fnref n="1" />,<fnref n="1" />,<fnref n="2" />,<fnref n="3" /> ID, Etienne Peillard<fnref n="1" />,<fnref n="3" /> ID, James Walsh<fnref n="2" />,<fnref n="3" /> ID, Guillaume Moreau<fnref n="1" />,<fnref n="3" /> ID and Bruce Thomas<fnref n="2" />,<fnref n="3" /> ID

## Abstract

For autonomous vehicles (AVs) to be widely accepted, users must not only feel safe but also understand how the vehicle perceives and responds to its environment. Augmented Reality (AR) enables real-time, intuitive communication of such information, helping foster trust and enhance situation awareness (SA). This paper presents the results of three online user studies that investigate the design of different AR visualization strategies in simulated AV environments. Although the studies used pre-recorded videos, they were designed to simulate ecologically realistic driving scenarios. Study 1 evaluates six types of highlight visualizations (bounding box, spotlight, point arrow, zoom, semantic segmentation, and baseline) across five driving scenarios varying in complexity and visibility. The results show that highlight effectiveness is scenario-dependent, with bounding boxes and spotlights being more effective in occluded or ambiguous conditions. Study 2 explores predictive visualizations, comparing single vs. multiple predicted paths and goals to communicate future trajectories. Findings indicate that single-path predictions are most effective for enhancing trust and safety, while multi-goal visualizations are perceived as less clear and less helpful. Study 3 examines the impact of spatial anchoring in AR by comparing screen-fixed and world-fixed presentations of time-to-contact information. Results demonstrate that world-fixed visualizations significantly improve trust, perceived safety, and object detectability compared to screen-fixed displays. Together, these studies provide key insights into when, what, and how AR visualizations should be presented in AVs to effectively support passenger understanding. The findings inform the design of adaptive AR interfaces that tailor visual feedback based on scenario complexity, uncertainty, and environmental context.

## CCS Concepts

* Human-centered computing → Mixed / augmented reality;

## 1 Introduction

A key barrier to the widespread adoption of autonomous vehicles (AVs) is a lack of user trust in automated systems [WFRS18]. Augmented Reality (AR) offers a promising solution by making the AV's perception and decision-making processes more transparent through intuitive visual overlays, such as highlighting detected objects or visualizing predicted paths [MLP*19]. In the automotive context, AR has emerged as a transformative tool to improve safety, enhance user engagement, and foster trust in both manual and automated driving. In particular, AR windshield displays (AR-WSDs) have shown promise in increasing system transparency by presenting relevant information directly within the driver's or passenger's line of sight [GFK14, WvSFR17a]. This reduces visual distraction and supports quicker comprehension of system behavior.

In terms of AR content, Müller et al. [MCDR22] propose a classification based on the type of reference provided by the visual-

ization, specifically whether it highlights something in the physical world or presents additional information. They categorize AR content into: highlight, depict, and combined elements. Highlight elements are used to draw attention to existing objects or areas in the real environment. In contrast, depict elements convey information not observable in the physical environment, such as predicted trajectories, alerts, or warnings. Combined elements integrate, highlight, and depict, offering a richer representation by coupling environmental cues with supplementary system information.

Prior research shows that highlight elements clarify AV perception, boosting trust, and Situation Awareness (SA) [KYJ*23, WvSFR17b], while predictive visualizations (e.g., future paths) can enhance user understanding and confidence [CSS*24]. However, most studies evaluate visualization techniques in isolation or within a limited scenario, leaving open questions about how various techniques perform across different contexts and varying levels of complexity. To address these gaps, this paper investigates the following research questions: (1) *Highlight* - Which types of highlights, and in which situations, should be provided to enhance trust, SA, and

<footnote n="1">¹IMT Atlantique, CNRS, Lab-STICC</footnote>

<footnote n="2">²Australian Research Centre for Interactive and Virtual Environments (IVE)</footnote>

<footnote n="3">³ CROSSING IRL 2010, Adelaide, Australia</footnote>

<footnote>† e-mail: thi-thanh-hoa.tran@imt-atlantique.fr</footnote>
safety in AVs? (2) *Depict prediction* - Which types of heatmap visualizations most effectively communicate predictive trajectories to passengers in AV, and under which situations should they be used? (3) *Anchoring* - How does world-fixed vs. screen-fixed AR presentation affect user perception of time to contact (TTC)? To address these questions, we conducted three online video-based AR studies. This method is widely adopted in AV research [CERR21, CSS*24]. It also ensures participant safety and provides realistic driving scenarios. The findings of these studies will inform future work on combining highlight and depict visualizations to determine which driving situations benefit most from their integration.

This work makes the following contributions: (1) A comparative evaluation of six highlight visualization techniques (bounding box, spotlight, point arrow, semantic segmentation, zoom, and baseline) across diverse AV driving scenarios, providing a systematic analysis of how these visualizations affect trust, perceived safety, and SA in passengers. (2) An investigation into predictive visualizations for communicating path and goal uncertainty, examining how different representations (single versus multiple, goal versus path) influence user confidence in the AV. (3) Comparing world-fixed and screen-fixed displays of time-to-contact to determine their relative impact on increasing trust, perceived safety, and object detectability.

The paper begins with a review of relevant literature, followed by an explanation of the methodology. Next, the studies and their findings are presented, and concludes with the discussion.

## 2 Related work

Design features of automated systems can also influence learned trust, though indirectly, by shaping how users perceive the system's performance [HB15]. In this section, we review the state-of-the-art research on AR visualizations designed to enhance trust in AVs.

### 2.1 Highlight Visualization

One commonly used AR strategy to support transparency is object highlight, which draws attention to elements the vehicle considers relevant or hazardous.

Colley et al. [CKLR21] conducted a pilot study in virtual reality (VR) and found that AR-based highlighting of critical objects was perceived as both necessary and reasonable. Compared to a baseline condition with no highlighting, such visual cues significantly improved users' trust, perceived safety, and acceptance of the AV system. However, the effectiveness of object highlighting can depend on both the visualization method and the context in which it is used. Gao et al. [GWH*22] examined the role of bounding boxes in different traffic densities and found that their impact on SA varied. In high-traffic conditions, highlighting vehicles with bounding boxes reduced SA, while in low-traffic contexts, the effect was neutral. Conversely, bounding boxes highlight pedestrians, improve SA in low-traffic situations, but show no significant effect in high-traffic environments. Chen et al. [CNL*24] extended this work by comparing semantic segmentation and bounding boxes in AR-based driver support systems. Both visualization techniques outperformed a baseline in improving driving performance. However, their effectiveness varied depending on the driving task: semantic segmentation helped prevent rear-end collisions, while bounding boxes produced faster reaction times in pedestrian-related scenarios, regardless of traffic density.

Taken together, these studies emphasize that the impact of highlight visualizations is not uniform. It is shaped by factors such

as traffic density, object type (e.g., vehicle vs. pedestrian), and the chosen visual encoding. While bounding boxes and semantic segmentation have been widely studied, the effectiveness of other highlight techniques—such as spotlight, arrows, and zoom—across different driving contexts remains underexplored. Additionally, there is a lack of validation regarding which highlighting techniques are most effective in different driving situations.

### 2.2 Depicting Visualization

Providing predictive information about the vehicle's environment helps build passenger trust by offering a real-time understanding of how the vehicle perceives its surroundings and makes decisions [PV24]. According to You et al., HMI designs that incorporate predictive information can enhance drivers' understanding of and trust in AV [YZZ*24]. A variety of techniques have been explored to represent predicted trajectories, which can generally be grouped into *discrete visualizations*, such as arrow lines, and *continuous visualizations*, such as heatmaps [HKKS17, ZCCW24]. Zhang et al. [ZCCW24] compared these approaches in a user study and found that heatmaps were more effective and more preferred by users when conveying uncertain future movement. Mangalam et al. [MAGM21] distinguish between two types of predicted future uncertainty that used heatmap to display: *epistemic uncertainty*, which pertains to predicting an agent's goal and addresses the question "Where does the agent want to go?"; and *aleatoric uncertainty*, which relates to the predicted path the agent may take and answers "How will the agent reach the goal?" Visualizing epistemic uncertainty can be particularly effective, as it offers insight into the range of potential future goals an agent may pursue. In contrast, visualizing aleatoric uncertainty helps illustrate the variability in possible trajectories caused by stochastic factors. There is limited understanding of how passengers interpret different types of predictive uncertainty (goals vs. paths), and whether single or multiple predictions enhance or hinder user trust and comprehension.

### 2.3 Anchoring

The spatial anchoring of AR content, whether it is fixed to the physical environment or the display screen, can significantly influence user perception, trust, and SA. World-Fixed AR presents information anchored to the real-world environment. In this approach, visual content remains spatially stable and appears to be attached to physical locations or objects. This method enhances depth perception and spatial understanding by providing natural oculomotor cues, which have been shown to lead to faster and more accurate hazard assessments [LMT*19]. Screen-Fixed AR, in contrast, displays information relative to the device screen. This is a more traditional method. While this format can facilitate quicker access to dynamic content, it may reduce spatial context and has been found to increase the time users spend looking at potentially unsafe areas [BDT*23]. The choice between world-fixed and screen-fixed displays influences how users perceive predictive cues and spatial relationships, particularly in safety-critical environments such as AV. Few studies have compared world-fixed versus screen-fixed displays in the context of predictive AV information.

## 3 Methodology

### 3.1 Materials

To ensure ecological validity, five realistic driving scenarios were selected to reflect common challenges encountered by auto-
mated vehicles. These scenarios varied in road type, traffic density, and lighting conditions. The scenarios were as follows: (1) a crosswalk situation where an animal unexpectedly crosses a national road at night under low traffic density; (2) an occluded vehicle that suddenly appears from behind another object at an intersection during daylight with low traffic density; (3) an oncoming car at an unsigned intersection on a national road during the day with high traffic density; (4) a near-collision event where another vehicle abruptly cuts into the AV's lane on an urban road with high traffic density; and (5) a pedestrian emerging from behind a construction site at night on an urban road with high traffic density.

We used 12-second dashcam videos sourced from public YouTube footage recorded on local roads, providing a realistic and consistent view of the driving environment (see Supplementary materials). Visual overlays were generated through a two-step process: segmentation and video editing. First, object masks were extracted using Meta's Segment Anything Model (SAM) [bMA]. These masks were then imported into DaVinci Resolve by dragging them onto the timeline in the Edit page. Next, a new Fusion clip was created that combined the object mask with the original video. In Fusion mode, visual elements were added, including TTC indicators, predicted trajectories (shown as heatmap lines or goals), and highlights (bounding boxes, spotlights, and arrows). The object masks helped accurately place these visualizations on the moving objects, keeping them properly aligned throughout the video.

## 3.2 Procedure

All studies were conducted online. In study 1, participants viewed 30 videos (around 45 minutes in total); in study 2, 25 videos (around 35 minutes); and in study 3, 15 videos (around 25 minutes). Participants completed the experiments on their personal laptops to maintain consistent viewing conditions and were allowed to take short breaks between sessions to minimize fatigue. Participants were students or staff members from our university. The experiment invitation and participation form were distributed via email. Participants started by reviewing an information sheet and signing a consent form before proceeding to read an overview of the study. Following this, they completed a demographic questionnaire that covered details such as age, gender, driver's license status, prior driving experience, and general attitudes toward automation. Once the initial questionnaire was submitted, participants initiated the experiment by clicking a start button. The five driving scenarios were presented using a Latin square design to ensure counterbalanced exposure across conditions. Additionally, the order of the visualization videos within each scenario was randomized to minimize potential order-related biases. After watching each video, participants completed a set of questionnaire items evaluating trust, perceived safety, SA (specifically situation complexity), and perception, as described in Section 3.3. After each scenario, they ranked the visualizations based on trust, perceived safety, SA, and likability. All studies were approved by the university's ethics committee.

## 3.3 Measures

The studies assessed the effectiveness of the visualizations using the following evaluation metrics. All items across the questionnaires were rated on a 7-point Likert scale.

**Trust:** Trust levels were adopted from the trust item in the Trust in Automation (TiA) questionnaire of Kober [Kör19].

**Perceived safety:** Participants provided ratings of their

perceived safety using 7-point semantic differential scales, ranging from 1 (anxious/agitated/unsafe/timid) to 7 (relaxed/calm/safe/confident), as adapted from Faas et al. [FKB20].

**SART:** SA was measured using the Situation Awareness Rating Technique (SART) [ESHCP98], focusing specifically on the complexity dimension, which asks participants to rate how complex the situation was.

**Perception:** Participants' perception of critical elements was assessed with a subjective question: "How easily can you detect the critical elements?"

## 3.4 Analysis plan

To assess the effects of the experimental conditions on trust, perception, situation complexity, attention, and safety, a series of analyses was conducted. Outlier detection was performed to identify disengaged participants (e.g., those showing straight-lining behavior), but none were found. The Shapiro-Wilk test revealed non-normal data distributions, prompting the use of the Friedman test for comparing conditions across all experiments. Post-hoc comparisons were conducted using the Wilcoxon signed-rank test with Bonferroni correction. Data distributions were visualized using the ggboxplot function [Moo17]. In addition to questionnaire responses after each video, participants ranked the visualizations at the end of each scenario. These rankings were analyzed using the same statistical procedure. Detailed descriptions of the statistical analyses are provided in the supplementary materials.

# 4 User study 1 - Impact of Highlight on Trust in AV

## 4.1 Visualization design

**Baseline:** This condition does not include added visualization.

**Bounding box:** The bounding box is a widely used interface design in machine vision algorithms to indicate detected hazards [KG22]. Flohr et al. demonstrated that transparent system feedback through bounding boxes for pedestrian detection enhances user understandability and perceived usefulness [FVKW23]. In this study, we employed oriented bounding boxes to detect hazardous elements. Unlike standard bounding boxes, they can be rotated to better align with an object's orientation, Figure 1b. This is particularly beneficial for detecting vehicles, which often appear at various angles relative to the camera [BP23].

**Spotlight:** The spotlight technique mimics real-world spotlights, illuminating specific objects to make them stand out from the background. The spotlighting system highlights an object by displaying a bright circular focus area, enabling users to identify and emphasize key aspects intuitively. This approach facilitates cognitive sharing, enhancing understanding and engagement during presentations [AKW15]. Building on this concept, we further enhance the highlighted object or area by making it appear brighter than its surroundings, Figure 1c.

**Point to object:** Arrows are commonly used to direct user attention in graphical interfaces, simplifying interactions and selection tasks [MW87]. Ostkamp et al. showed that an arrow can enhance focus by drawing attention to specific elements, facilitating quicker decision-making in environments with dense information [OBK12]. In this study, arrows indicate objects in uncertain situations, ensuring users can quickly identify them, Figure 1d.

**Semantic Segment:** This technique highlights an entire object by altering its appearance, enabling clearer identification, Fig-
ure 1e. Semantic segmentation allows for identifying critical objects, which is essential for safe navigation [LTP*24].

**Zoom:** Zooming provides a detailed view of objects, improving users' ability to recognize and monitor them accurately [NC07]. This technique ensures that important elements remain clearly visible, even in visually complex environments, Figure 1f. The red circle indicates the area of the object that will be zoomed in.

Previous studies have shown that information processing is affected by variations in graphic shape. Processing complex graphics requires more time and cognitive effort than processing simple shapes [DZZ*22]. Based on the research question 1, we propose this H1: The effectiveness of the visualization techniques for highlighting objects can be ranked across different driving scenarios.

<figure><img src="image_3.png" /><figcaption>Figure 1: Highlight visualization</figcaption></figure>

## 4.2 Participants

A total of 34 participants took part in the study (22 men and 12 women; age 21-57, M = 27.9, SD = 9.09). Twenty-six participants held a valid driver's license; eight did not.

## 4.3 Results

### 4.3.1 Trust

Friedman's test found statistically significant differences for trust in scenario 1 ($X^2(5) = 29.91, p < .001$), scenario 2 ($X^2(5) = 35.09, p < .001$), scenario 3 ($X^2(5) = 29.19, p < .001$), scenario 4 ($X^2(5) = 29.26, p < .001$), and scenario 5 ($X^2(5) = 21.43, p = 0.0007$) (Fig. 1, Appendix).

Pairwise Wilcoxon signed-rank tests revealed significant differences in trust between baseline and visualization conditions across scenarios. In scenario 1, trust was significantly higher with both bounding box and spotlight visualizations ($p = .001$). In scenario 2, the bounding box condition showed a significant increase ($p < .001$). Scenario 3 showed higher trust with semantic segmentation ($p = .002$). In scenario 4, significant differences were found between the baseline and the bounding box, spotlight, and semantic segmentation visualizations ($p < .001$). Finally, in scenario 5, the bounding box again led to significantly higher trust ($p = .001$).

### 4.3.2 Situation complexity

Friedman's test found statistically significant differences in the different conditions for the complexity of situation in scenario 1 ($X^2(5) = 18.64, p = 0.002$), scenario 2 ($X^2(5) = 37.34, p < .001$), scenario 3 ($X^2(5) = 24.59, p = 0.0002$), and scenario 4 ($X^2(5) = 44.12, p < .001$) (Fig. 2, Appendix).

A pairwise Wilcoxon signed-rank test revealed statistically significant differences in situation complexity across multiple scenarios. In scenario 1, the complexity of the situation differed significantly between the baseline condition and the bounding box ($W = 0.11, p = 0.002$). In scenario 2, a significant difference was observed between the baseline and both the bounding box and semantic visualizations ($W = 0.22, p < .001$). In scenario 4, significant differences were found between the baseline and all other conditions except for the zoom visualization ($W = 0.26, p < .001$).

### 4.3.3 Easy to detect object

Friedman's test found statistically significant differences in the different conditions for the attention divided in scenario 1 ($X^2(5) = 41.95, p < .001$), scenario 2 ($X^2(5) = 39.29, p < .001$), scenario 3 ($X^2(5) = 23.12, p = 0.0003$), scenario 4 ($X^2(5) = 52.98, p < .001$), and scenario 5 ($X^2(5) = 42.45, p < .001$) (Fig. 3, Appendix).

A pairwise Wilcoxon signed-rank test revealed statistically significant differences in the easy-to-detect object across multiple scenarios. In scenarios 1 and 4, the easy-to-detect object differed significantly between the baseline condition and all other visualizations ($p < .005$). In scenario 2, significant differences were observed between the baseline and the bounding box, spotlight, zoom, and semantic visualizations ($W = 0.23, p < .005$). In scenario 3, significant differences were observed between the baseline and the spotlight ($W = 0.136, p = .001$). In scenario 5, significant differences were found between the baseline and all other visualizations, except for the semantic condition ($W = 0.25, p < .005$).

### 4.3.4 Perceived Safety

Friedman's test revealed significant differences in perceived safety across conditions in scenario 1 ($X^2(5) = 24.69, p = 0.0002$), scenario 2 ($X^2(5) = 35.62, p < .001$), scenario 3 ($X^2(5) = 21.19, p = 0.0007$) and scenario 4 ($X^2(5) = 31.16, p < .001$) (Fig. 4, Appendix). A pairwise Wilcoxon signed-rank test revealed statistically significant differences in perceived safety across multiple scenarios. In scenario 1, perceived safety differed significantly between the baseline condition and the bounding box ($W = 0.15, p = .003$). In scenario 2, a significant difference was observed between the baseline and both the bounding box and semantic visualizations ($W = 0.21, p < .001$). In scenario 4, significant differences were found between the baseline and the bounding box, spotlight, and point arrow visualizations ($W = 0.183, p < .005$).

### 4.3.5 Ranking

Friedman's test found statistically significant differences in the different conditions for trust, perceived safety, SA, and likability (Table 1). Wilcoxon tests revealed several statistically significant differences between conditions across the four key measures. For trust, both the bounding box ($p < .001$) and spotlight ($p < .005$) visualizations showed significant improvements over the baseline in all scenarios. Additionally, the point arrow visualization demonstrated significant differences in scenarios 1 and 2 ($p < .005$). Regarding perceived safety, the bounding box, point arrow, and spotlight were significantly more effective than the baseline in scenarios 1 and 2 ($p < .001$). In scenario 3, only the bounding box showed a significant difference, while in scenario 4, all visualizations except zoom outperformed the baseline. In scenario 5, the bounding box and spotlight again showed significant improvements. For SA, significant differences were observed for the bounding box, point arrow, and spotlight in scenarios 1 to 3 ($p < .001$). In scenario 4, all visualizations except zoom showed significant improvements, and in scenario 5, all but semantic segmentation differed significantly from the baseline. Finally, in terms of likability, the bounding box and spotlight were rated significantly higher than the baseline in scenarios 1, 2, 4, and 5 ($p < .001$), while in scenario 3, only the bounding box showed a statistically significant improvement.
<table><caption>Table 1: Result of Friedman test for ranking of visualization - User study 1</caption><thead><tr><th>Measure</th><th>Scenario 1</th><th>Scenario 2</th><th>Scenario 3</th><th>Scenario 4</th><th>Scenario 5</th></tr></thead><tbody><tr><td>Trust</td><td>X<sup>2</sup>(5) = 36.71, p &lt; .001</td><td>X<sup>2</sup>(5) = 43.41, p &lt; .001</td><td>X<sup>2</sup>(5) = 43.76, p &lt; .001</td><td>X<sup>2</sup>(5) = 50.81, p &lt; .001</td><td>X<sup>2</sup>(5) = 30.07, p &lt; .001</td></tr><tr><td>Perceived Safety</td><td>X<sup>2</sup>(5) = 45.73, p &lt; .001</td><td>X<sup>2</sup>(5) = 44.82, p &lt; .001</td><td>X<sup>2</sup>(5) = 52.54, p &lt; .001</td><td>X<sup>2</sup>(5) = 51.36, p &lt; .001</td><td>X<sup>2</sup>(5) = 25.68, p &lt; .001</td></tr><tr><td>SA</td><td>X<sup>2</sup>(5) = 35.72, p &lt; .001</td><td>X<sup>2</sup>(5) = 49.09, p &lt; .001</td><td>X<sup>2</sup>(5) = 44.29, p &lt; .001</td><td>X<sup>2</sup>(5) = 55.13, p &lt; .001</td><td>X<sup>2</sup>(5) = 47.39, p &lt; .001</td></tr><tr><td>Likability</td><td>X<sup>2</sup>(5) = 40.89, p &lt; .001</td><td>X<sup>2</sup>(5) = 38.94, p &lt; .001</td><td>X<sup>2</sup>(5) = 54.96, p &lt; .001</td><td>X<sup>2</sup>(5) = 46.57, p &lt; .001</td><td>X<sup>2</sup>(5) = 43.34, p &lt; .001</td></tr></tbody></table>

## 4.4 Discussion

AR highlighting effectiveness varies by context: bounding boxes work well in complex scenes (scenarios 2, 5), semantic segmentation suits simpler ones (scenario 3), and spotlights help in low-light or urgent situations (scenarios 1, 4).

Despite improvements in object detectability, trust ratings varied by scenario and visualization type. For instance, in scenario 1, only bounding boxes and spotlights significantly increased trust compared to the baseline. Similarly, in scenario 3, semantic segmentation led to a significant trust improvement.

In occluded or urgent scenarios, bounding boxes increased trust and safety, regardless of traffic density or lighting. This confirms their value as a reliable general-purpose cue. In contrast, semantic segmentation was most effective in low-complexity scenes like scenario 3. However, in complex situations, the same level of detail may demand effort and slow comprehension.

Spotlight visualizations were particularly effective in scenarios 1 and 4. In scenario 1, where poor lighting and the small size of the animal posed detection challenges, the spotlight helped focus attention on low-contrast objects. This suggests that spotlight cues may be especially valuable when visual salience is reduced by environmental conditions.

In summary, these findings underscore the importance of context design in AR interfaces for AVs. The effectiveness of bounding boxes, segmentation, and spotlight cues depends on scene and environmental conditions, supporting the H1 that highlighting techniques can be meaningfully ranked across scenarios.

# 5 User study 2 - Depict information: Predict Location Visualization

## 5.1 Visualization Design

Different types of predictive visualizations communicate varying levels of uncertainty, intent, and trajectory detail. According to Colley et al. [CSS*24], color is the most effective cue for conveying uncertainty. Building on this, we employ color intensity in our designs: darker colors indicate higher probability, while lighter colors indicate lower probability. As demonstrated by [ZCCW24], heatmaps are more effective than discrete visualizations for representing future movement. [MAGM21] further categorized heatmap visualizations into goal-based and path-based predictions. Building on these, we propose the following visualizations, which vary along two dimensions: single versus multiple, and goal versus path.

**Single Goal Prediction:** This visualization highlights a single predicted goal location (e.g., where a pedestrian is likely to go). A red circle indicates the predicted goal, with darker shades representing higher probabilities (Figure 2b).

**Multiple Goal Prediction:** This visualization shows multiple potential goal locations using a heatmap of red circles of varying sizes (Figure 2c). Larger and darker circles indicate higher likelihoods that the object will end up in that location [MAGM21].

**Single Path Prediction:** A continuous heatmap shows the object's single predicted trajectory. Darker paths indicate higher confidence in that predicted motion (Figure 2d).

**Multiple Path Prediction:** Multiple potential future trajectories are visualized as a heatmap, adapted from Liang et al. [LJM*20]. Each path segment is shaded to indicate the likelihood of the object taking that route (Figure 2e).

Based on RQ2, we propose the following hypothesis:

**H2:** Predictive visualization effectiveness varies by context: single-path aids trust in predictable scenarios, while multi-path or multi-goal improves trust in ambiguous ones.

<figure><img src="image_4.png" /><figcaption>Figure 2: Predictive visualization</figcaption></figure>

## 5.2 Participants

A total of 32 participants took part in the study (24 men, 8 women; age 21-57, M=28.84, SD=8.63). On a 5-point Likert scale, in response to the question “Do you trust the autonomous vehicle?”, participants reported a moderate level of trust (M=3.06).

## 5.3 Results

### 5.3.1 Trust

Friedman's test revealed statistically significant differences in trust ratings across conditions in: scenario 1 ($X^2(4) = 21.24, p < .001$), scenario 2 ($X^2(4) = 34.8, p < .001$), scenario 3 ($X^2(4) = 40.06, p < .001$), scenario 4 ($X^2(4) = 27.47, p < .001$), and scenario 5 ($X^2(4) = 47.83, p < .001$) (Fig. 5, Appendix).

Wilcoxon tests showed significant differences in trust between specific conditions within each scenario. Trust was significantly higher in the single-path condition compared to the baseline across five scenarios ($p < .005$). In scenarios 2 and 3, both the single-path and multiple-path conditions led to significantly higher trust than the baseline ($p < .001$). In scenario 4, significant differences were found between the baseline and both the single-path and single-goal conditions ($p < .005$). Finally, in scenario 5, trust was significantly higher in the single-path, single-goal, and multiple-path conditions compared to the baseline ($p < .005$).

### 5.3.2 Easy to detect object

Friedman's test revealed statistically significant differences in trust ratings across conditions for all five scenarios: scenario 1 ($X^2(4) = 35.99, p < .001$), scenario 2 ($X^2(4) = 38.07, p < .001$), scenario 3 ($X^2(4) = 25.08, p < .001$), scenario 4 ($X^2(4) = 30.52, p < .001$) and scenario 5 ($X^2(4) = 47.7, p < .001$) (Fig. 6, Appendix). Wilcoxon tests revealed significantly more easy-to-detect objects in several conditions compared to baseline. In scenarios 1 and 5, the single-path, multiple-path, single-goal condition outperformed the baseline ($p < .001$). In scenarios 2 and 4, both single- and multiple-paths were significantly better ($p < .001$). In Scenario 3, single-path also outperformed baseline ($p < .001$).
<table><caption>Table 2: Result of Friedman test for ranking of visualization - User study 2</caption><thead><tr><th>Measure</th><th>Scenario 1</th><th>Scenario 2</th><th>Scenario 3</th><th>Scenario 4</th><th>Scenario 5</th></tr></thead><tbody><tr><td>Trust</td><td>X<sup>2</sup>(4) = 54.33, p &lt; .001</td><td>X<sup>2</sup>(4) = 40.67, p &lt; .001</td><td>X<sup>2</sup>(4) = 38.88, p &lt; .001</td><td>X<sup>2</sup>(4) = 43.52, p &lt; .001</td><td>X<sup>2</sup>(4) = 51.17, p &lt; .001</td></tr><tr><td>Perceived Safety</td><td>X<sup>2</sup>(4) = 68.62, p &lt; .001</td><td>X<sup>2</sup>(4) = 47.92, p &lt; .001</td><td>X<sup>2</sup>(4) = 47.83, p &lt; .001</td><td>X<sup>2</sup>(4) = 54.17, p &lt; .001</td><td>X<sup>2</sup>(4) = 57.38, p &lt; .001</td></tr><tr><td>SA</td><td>X<sup>2</sup>(4) = 67.3, p &lt; .001</td><td>X<sup>2</sup>(4) = 60.92, p &lt; .001</td><td>X<sup>2</sup>(4) = 52.3, p &lt; .001</td><td>X<sup>2</sup>(4) = 55.73, p &lt; .001</td><td>X<sup>2</sup>(4) = 53.98, p &lt; .001</td></tr><tr><td>Likability</td><td>X<sup>2</sup>(4) = 54.5, p &lt; .001</td><td>X<sup>2</sup>(4) = 46.12, p &lt; .001</td><td>X<sup>2</sup>(4) = 45.15, p &lt; .001</td><td>X<sup>2</sup>(4) = 42.48, p &lt; .001</td><td>X<sup>2</sup>(4) = 49.42, p &lt; .001</td></tr></tbody></table>

### 5.3.3 Perceived Safety

Friedman's test showed statistically significant differences in perceived safety across conditions in: scenario 1 ($X^2(4) = 54.28, p < .001$), scenario 2 ($X^2(4) = 42.72, p < .001$), scenario 3 ($X^2(4) = 32.45, p < .001$), scenario 4 ($X^2(4) = 28.15, p < .001$) and scenario 5 ($X^2(4) = 50.76, p < .001$) (Fig. 7, Appendix).

Wilcoxon tests indicated significant differences in perceived safety between conditions. In Scenarios 1, 2, and 5, perceived safety was higher in the single-path, single-goal, and multiple-path conditions than in the baseline ($p < .01$). In scenario 3, perceived safety was significantly higher in both the single-path and multiple-path conditions compared to the baseline ($W = 0.25, p < .001$). In scenario 4, the single-path condition differed significantly from the baseline ($W = 0.23, p < .001$).

### 5.3.4 Ranking

Friedman's test found statistically significant differences in the different conditions across four measures (Table 2).

Post-hoc Wilcoxon signed-rank tests revealed several statistically significant differences between conditions across the four key measures. For trust, both the multiple path and single path visualizations showed statistically significant improvements over the baseline in all scenarios ($p < .005$). Additionally, the single goal condition showed significant differences in scenarios 4 and 5 ($p < .005$). Regarding perceived safety, all visualization types differed significantly from the baseline in scenario 1 ($p < .001$). In scenarios 2 and 3, both multiple-path and single-path visualizations again showed significant improvements ($p < .001$). For scenarios 4 and 5, the multiple path, single path, and single goal conditions all resulted in significantly higher perceived safety ratings compared to the baseline ($p < .001$). For SA, all visualization conditions showed statistically significant improvements over the baseline in scenarios 1 and 5 ($p < .005$). In scenarios 2, 3, and 4, the multiple path, single path, and single goal visualizations were all significantly more effective than the baseline ($p < .005$). In terms of likability, both the multiple-path and single-path conditions were rated significantly higher than the baseline. Furthermore, the single-path visualization consistently outperformed both the multiple-goal and single-goal conditions across all four measures ($p < .05$).

## 5.4 Discussion

Across all five scenarios, single-path visualization was the most effective at increasing both trust and perceived safety. Participants also rank the single path as the most effective visualization to increase their trust, SA, safety, and likability. The results show that single-path cues also perform well in unpredictable scenarios, such as an unexpected animal crossing.

In scenarios 2 and 3, both involving intersections, multiple-path visualizations significantly improved both trust and perceived safety. This indicates that in settings where the situation is less predictable, visualizing a range of possible trajectories helps passengers better understand how the AV is reasoning about uncertainty. In contrast, the multiple-goal condition was consistently rated as the least effective across all scenarios. It failed to produce significant improvements in trust, safety, SA, or likability, and did not enhance object detectability compared to the baseline.

In conclusion, these findings provide partial support for H2, suggesting that the effectiveness of predictive visualizations may vary across different driving contexts. Single-path cues work well in both predictable and certain unpredictable environments. Multiple-path visualizations add value in ambiguous scenarios. However, multiple goals may lack the clarity to build user trust.

# 6 User study 3 - Depict information: Perception of TTC and World-Fixed vs. Screen-Fixed AR Presentation

## 6.1 Visualization design

While highlighting and prediction convey what the AV sees and what might happen, passengers also benefit from understanding how soon an object may become relevant. Time to contact (TTC) visualizations, such as countdown indicators, have been shown to help drivers and passengers anticipate imminent hazards [WH20]. TTC is a widely adopted measure for evaluating collision risk, providing an estimate of the time until a potential collision occurs. The presentation of AR cues, whether world-fixed or screen-fixed, affects user perception of approaching dangers. This user study investigates RQ3. We propose the following hypothesis: H3: TTC visualizations will increase user trust and perceived safety compared to no visualization, with world-fixed presentation producing the highest levels of trust and safety.

<figure><img src="image_5.png" /><figcaption>Figure 3: a) Baseline – no visualization; b) Screen-fixed TTC – information is displayed fixed to the screen; c) World-fixed TTC – information is anchored to the external environment.</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Figure 4: Color coding concepts for displaying the TTC</figcaption></figure>

This user study includes 3 conditions (1) Baseline, (2) World-fixed TTC, and (3) Screen-fixed TTC (Figure 3). A color-coded system was used to represent TTC, offering intuitive cues about urgency. The indicator shifted from green (5 s, low urgency) to yellow (4 s), light orange (3 s), orange (2 s), and red (1 s, high urgency). This gradient allowed users to assess risk levels quickly without needing to interpret numerical values (Figure 4).

## 6.2 Participants

A total of 28 participants took part in the study (17 men, 11 women; age 20–66, M=28.6, SD=10.6). On a 5-point Likert scale, in response to the question “Do you trust the autonomous vehicle?”, participants reported a moderate level of trust (M=3.11).

## 6.3 Results

### 6.3.1 Trust

Friedman's test revealed statistically significant differences in trust across conditions in all five scenarios: scenario 1 ($X^2(2) =$
<table><caption>Table 3: Result of Friedman test for ranking of visualization - User study 3</caption><thead><tr><th>Measure</th><th>Scenario 1</th><th>Scenario 2</th><th>Scenario 3</th><th>Scenario 4</th><th>Scenario 5</th></tr></thead><tbody><tr><td>Trust</td><td>X<sup>2</sup>(2) = 33.5, p &lt; .001</td><td>X<sup>2</sup>(2) = 27.21, p &lt; .001</td><td>X<sup>2</sup>(2) = 17.21, p &lt; .001</td><td>X<sup>2</sup>(2) = 21.93, p &lt; .001</td><td>X<sup>2</sup>(2) = 23.14, p &lt; .001</td></tr><tr><td>Perceived Safety</td><td>X<sup>2</sup>(2) = 36.64, p &lt; .001</td><td>X<sup>2</sup>(2) = 29.43, p &lt; .001</td><td>X<sup>2</sup>(2) = 17.64, p &lt; .001</td><td>X<sup>2</sup>(2) = 19.5, p &lt; .001</td><td>X<sup>2</sup>(2) = 24.25, p &lt; .001</td></tr><tr><td>SA</td><td>X<sup>2</sup>(2) = 30.05, p &lt; .001</td><td>X<sup>2</sup>(2) = 31.36, p &lt; .001</td><td>X<sup>2</sup>(2) = 24.25, p &lt; .001</td><td>X<sup>2</sup>(2) = 24.93, p &lt; .001</td><td>X<sup>2</sup>(2) = 26, p &lt; .001</td></tr><tr><td>Likability</td><td>X<sup>2</sup>(2) = 33.5, p &lt; .001</td><td>X<sup>2</sup>(2) = 27.71, p &lt; .001</td><td>X<sup>2</sup>(2) = 14.21, p &lt; .001</td><td>X<sup>2</sup>(2) = 23.36, p &lt; .001</td><td>X<sup>2</sup>(2) = 19.5, p &lt; .001</td></tr></tbody></table>

31.32, p < .001), scenario 2 (X<sup>2</sup>(2) = 25.56, p < .001), scenario 3 (X<sup>2</sup>(2) = 16.69, p < .001), scenario 4 (X<sup>2</sup>(2) = 35.77, p < .001) and scenario 5 (X<sup>2</sup>(2) = 31.54, p < .001) (Fig. 8, Appendix).

Follow-up Wilcoxon signed-rank tests revealed significant differences in trust between conditions Notably, in all scenarios, trust was significantly higher in both the world-fixed and screen-fixed conditions compared to the baseline (p < .05).

### 6.3.2 Easy-to-detect object

Friedman's test revealed statistically significant differences in trust across conditions in: scenario 1 (X<sup>2</sup>(2) = 28.68, p < .001), scenario 2 (X<sup>2</sup>(2) = 31.35, p < .001), scenario 4 (X<sup>2</sup>(2) = 22.52, p < .001) and scenario 5 (X<sup>2</sup>(2) = 29.08, p < .001) (Fig. 9, Appendix). Follow-up Wilcoxon signed-rank tests showed significant differences in the ease of object detection between conditions. In scenarios 1 and 2, both the world-fixed and screen-fixed conditions resulted in significantly higher ease of object detection compared to the baseline (p < .001). In scenarios 4 and 5, only the world-fixed condition was significantly better than the baseline (p < .001). World fixed also has significantly higher than screen fixed in scenarios 1, 2, 4, and 5 (p < .01).

### 6.3.3 Perceived Safety

Friedman's test revealed statistically significant differences in perceived safety across conditions in: scenario 1 (X<sup>2</sup>(2) = 30.54, p < .001), scenario 2 (X<sup>2</sup>(2) = 33.84, p < .001), scenario 3 (X<sup>2</sup>(2) = 7.69, p < .001), scenario 4 (X<sup>2</sup>(2) = 31.74, p < .001) and scenario 5 (X<sup>2</sup>(2) = 27.36, p < .001) (Fig. 10, Appendix).

Follow-up Wilcoxon signed-rank tests revealed significant differences in perceived safety between conditions. Specifically, in scenarios 1, 2, and 4, perceived safety was significantly higher in both the world-fixed and screen-fixed conditions compared to the baseline (p < .05). Additionally, the world-fixed condition resulted in significantly higher perceived safety than the screen-fixed condition in these scenarios (p < .005). In contrast, in scenarios 3 and 5, only the world-fixed condition produced significantly higher perceived safety than the baseline (p < .001).

### 6.3.4 Ranking

Friedman's test found statistically significant differences in the different conditions across four measures (Table 3). Post-hoc Wilcoxon signed-rank tests revealed several statistically significant differences between conditions across the four key measures. For trust, both the world-fixed and screen-fixed conditions showed significant improvements over the baseline in scenarios 1, 2, and 5 (p < .05). In scenarios 3 and 4, only the world-fixed condition differed significantly from the baseline (p < .005). Additionally, the world-fixed visualization significantly outperformed the screen-fixed condition in scenarios 1, 2, 4, and 5 (p < .05). Regarding perceived safety, the world-fixed visualization consistently outperformed both the baseline and screen-fixed conditions across all scenarios (p < .05). For situation awareness, all visualization conditions showed statistically significant differences from the baseline in scenarios 1 and 5 (p < .05). Furthermore, the world-fixed condi-

tion significantly outperformed both the baseline and screen-fixed conditions in scenarios 2, 3, 4, and 5 (p < .05). In terms of likability, the world-fixed visualization was rated significantly higher than both the baseline and screen-fixed conditions in scenarios 1, 2, 4, and 5 (p < .05). In scenario 3, the world-fixed condition showed a significant difference when compared to the baseline (p < .001).

## 6.4 Discussion

Across five varied driving scenarios, both world-fixed and screen-fixed TTC visualizations significantly improved user trust and perceived safety compared to the baseline with no visualization. These findings support H3, which proposed that TTC visualizations enhance user trust and safety, with world-fixed presentation being the most effective.

World-fixed visualizations consistently outperformed screen-fixed ones in trust ratings. This advantage is likely due to the direct spatial mapping of the TTC cue to the external environment. By anchoring the information to the actual object in the world, passengers can more intuitively perceive both where and when a critical event is expected to occur. This spatial grounding enhanced the interpretability of the AV's predictions and promoted greater confidence in its behavior. Furthermore, object detectability was significantly higher in the world-fixed condition than in the screen-fixed condition, reinforcing the benefit of spatially anchored visualizations. This suggests that TTC cues are not only more trustworthy when world-anchored but also more effective in helping users identify critical objects in complex scenes.

When it comes to perceived safety, a similar trend was observed: world-fixed visualizations led to significantly higher safety ratings than screen-fixed. This effect was especially evident in scenarios 3 and 5: the unsigned intersection and pedestrians behind construction, where only the world-fixed condition resulted in a significant increase in perceived safety. World-fixed TTC visualizations helped bridge this perceptual gap by anchoring temporal information to specific objects in the environment. In these cases, only the world-fixed condition yielded significant improvements, highlighting its ability to clarify AV intent in uncertain environments.

Overall, these results emphasize the importance of how temporal information is conveyed. Depicting TTC using AR cues enhances trust and safety, especially when these cues are meaningfully integrated into the environment through world-fixed spatial anchoring.

# 7 Global Discussion

## 7.1 Contextual Effectiveness of Highlight Visualizations

Highlight visualizations (Study 1) are most beneficial when tuned to the driving context. Bounding boxes can increase user trust in scenarios where critical objects were partially or fully occluded or where rapid decision-making was necessary, regardless of traffic density or lighting conditions. This highlights the effectiveness of bounding boxes as a general-purpose visualization technique for AVs. This utility contrasts somewhat with Gao et al.'s [GWH*22] finding that bounding boxes can reduce SA in high-traffic scenarios. One possible explanation is that our study focused on passengers, not drivers, and passengers may interpret bounding boxes as
reassuring cues that the AV is actively monitoring its surroundings, particularly when visibility is compromised.

Scenario 3 was rated as the least complex among all scenarios (mean perceived complexity score = 2.706). In this case, the semantic segmentation visualization led to an increase in trust. This may be explained by the nature of the scenario: participants likely preferred a visualization that could precisely identify and differentiate objects in the environment. Semantic segmentation, by clearly outlining potential hazards, may have provided the clarity participants needed to recognize what the AV was attending to and why. Thereby increasing their confidence in the system. This interpretation is supported by Dai et al. [DZZ*22], who argue that complex visualizations can impose higher cognitive load and are better suited to situations where detailed interpretation is necessary, whereas in simpler contexts, clarity and precision are more beneficial for user understanding. Across all scenarios, the use of highlight visualizations increased users' trust in AV behavior compared to the baseline condition without any visualization. This aligns with prior research showing that visual cues indicating what the AV perceives and attends to can improve transparency and foster greater trust in automation systems [HB15].

Interestingly, the zoom visualization failed to enhance trust or perceived safety across all five driving scenarios, as reflected in both the post-video questionnaires and scenario-level rankings. This finding suggests that while zooming in on a specific object may draw attention to it, the technique might not provide sufficient contextual information for users to understand the object's relevance or the vehicle's intended response. Unlike other highlighting techniques, such as bounding boxes, segmentation, or spotlight, which emphasize critical elements while preserving the surrounding scene, zoom visualizations may narrow the field of view. This can unintentionally occlude other important environmental features, reducing users' ability to form a coherent understanding of the full traffic situation. In highly dynamic driving environments, this loss of context may diminish SA.

## 7.2 Prediction Helps, But Clarity is Critical

Study 2 emphasized the utility of predictive visualizations for conveying the AV's understanding of potential future events. However, the results also revealed a critical nuance: not all predictive formats are equally effective. Single-path visualizations can increase trust and safety. This suggests that a clearly defined projected trajectory communicates the AV's intentions in a way that is both intuitive and reassuring to users. The results also revealed important contextual differences. Multiple-path visualizations proved beneficial in ambiguous environments, such as intersections, where the behavior of other road users is less predictable. By illustrating a range of possible trajectories, this approach conveyed that the AV was considering multiple outcomes. This finding aligns with Mangalam et al.'s [MAGM21] concept of aleatoric uncertainty, which involves variability in future movement paths. In contrast, the multiple-goal visualization did not improve trust or safety. One likely reason is that presenting several potential destinations without connecting them to objects reduced interpretive clarity. As a result, participants may have found the multiple-goal condition difficult to understand, particularly when trying to assess how the AV would respond to uncertain movements from other vehicles.

Furthermore, participants rated the single-path visualization as

more likable than the multiple-path version. A single predicted path may give the impression that the AV has assessed the situation and made a clear choice. Additionally, intersections typically involve numerous visual elements and potential hazards. Multiple overlapping trajectories in a multi-path visualization may increase visual clutter and make it harder for users to focus on other information.

## 7.3 Spatial Anchoring Enhances Comprehension

Study 3 showed the value of world-fixed visualizations over screen-fixed alternatives for conveying TTC information. Anchoring the visualization to the environment resulted in better outcomes across trust, safety, and object detectability. Moreover, world-fixed visualizations contributed to a greater awareness, as they provided spatially grounded cues that helped participants understand what was happening and where. SA scores were highest in the world-fixed condition across scenarios. The ability to see not just when a potential hazard will occur, but also where it is, appears critical to forming accurate and confident mental models of AV behavior.

# 8 Limitations and Future Work

While the three online pilot studies provide important insights into the effectiveness of various AR visualization techniques in AVs, several limitations must be acknowledged. First, all studies were conducted in a controlled online setting using pre-recorded videos, which lacked the full immersion typically found in real-world or simulator-based AV experiences. This may have influenced how participants' scores. Second, each video was relatively short, which may not fully reflect the dynamic nature of longer driving situations. Additionally, the participant sample was drawn from a general online population, which may limit the generalizability of the findings. Finally, participants were passive viewers rather than active occupants of an AV.

These limitations point to several directions for future research. More immersive environments could better replicate AV travel conditions. It would also be useful to examine how trust and safety perceptions evolve with repeated exposure to AR feedback. Additionally, adaptive systems that adjust visualizations based on context or user state may enhance usability and reduce cognitive load. Furthermore, future work could examine the impact of the combination of highlight and depict visualization on trust and SA.

# 9 Conclusion

In conclusion, this paper presented three online studies that evaluated components of AR visualization in the context of AVs: object highlighting, predictive trajectory display, and TTC cues. Results showed that highlighting techniques such as bounding boxes and semantic segmentation improved trust and object detectability, particularly in complex and occluded driving environments. Predictive visualizations, especially single-path and multi-path representations, enhanced the understanding of the AV's situational awareness and intentions. TTC cues, especially when anchored to real-world positions, further strengthened perceived safety by clarifying when and where potential hazards may occur. Collectively, these findings underscore the value of context-sensitive and well-anchored AR interfaces in improving transparency, trust, and user experience. These studies provide a foundation for future work on combined visualization strategies that integrate both highlighting and predictive elements, supporting the development of more intelligent and adaptive AR systems for AVs.
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