MicroVision: An Open Dataset and Benchmark Models for Detecting Vulnerable Road Users and Micromobility Vehicles

arXiv:2603.18192 · cs.CV · Submitted 2026-03-18 · Read on arXiv

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Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: We're looking at the paper: "MicroVision: An Open Dataset and Benchmark Models for Detecting Vulnerable Road Users and Micromobility Vehicles".

Jane: The MicroVision dataset and benchmark models provide an open resource for detecting vulnerable road users and micromobility vehicles, addressing gaps in existing datasets by capturing scenes from a VRU perspective.

Tom: First, who's behind it and why it matters.

Paper summary: Tom: So we're looking at a paper called "MicroVision: An Open Dataset and Benchmark Models for Detecting Vulnerable Road Users and Micromobility Vehicles." Basically, the authors are saying that we really need better images for training computer vision models to help with traffic safety.

Jane: Right. The core problem they identified is that most existing open datasets just lump pedestrians and micromobility riders together as a general "person" category, or they forget newer things like e-scooters entirely.

Lu: And they point out that a lot of these datasets are taken from a car's perspective, so you miss the crucial data from where only vulnerable road users travel, like sidewalks and cycle paths.

Meng: So what’s the solution they propose? I mean, we need more diverse data that actually shows how people and vehicles interact in those specific environments.

Tom: They introduce MicroVision as an open image dataset with annotations for detecting common vulnerable road users—pedestrians, cyclists, e-scooterists—and stationary micromobility vehicles like bicycles and e-scooters.

Jane: It’s recorded in Gothenburg, Sweden, over a full year and part of two thousand unique interaction scenes. That’s a pretty comprehensive collection for training AI models to see this stuff from the right angle.

Lu: The annotation strategy they use is actually quite thoughtful because they use a state-aware labeling strategy. They define an active cyclist or e-scooterist as the combination of the vehicle and at least one person traveling with it.

Tom: And for things like someone pushing a vehicle, they label them as two separate objects: a pedestrian and a vehicle, which is important because it separates the user from the object.

Jane: They also make sure stationary vehicles are annotated whether they are upright or maybe even fallen over, which helps cover more real-world situations.

Meng: So on the performance side, they tested three different state-of-the-art object detection architectures: a one-stage YOLO version eleven a two-stage Faster RCNN, and a transformer model called RF-DETR.

Tom: And they found that the transformer model, RF-DETR, achieved the best overall performance on their test set with a mean average precision of zero point seven two six. That’s pretty strong compared to the others they tested.

Jane: But Tom, what does that number actually mean for someone who just drives or walks around? What's the real-world impact of an mAP of zero point seven two six?

Lu: Well, the authors say that this dataset and model can support traffic safety to distinguish between different vulnerable road users and micromobility vehicles, or help monitoring systems identify the use of micromobility.

Meng: From a deployment standpoint, I'd be interested in how this would scale. They also provide benchmark models whose weights you can access if you want to build something on top of this foundation.

Tom: The key finding they highlight is that active road users showed higher annotation consistency than stationary vehicles, which makes sense when you think about how often things are actually moving around.

Paper summary: Jane: But there are some limitations they mention too. They note that the dataset is geographically limited to Gothenburg in Sweden, which means we have to be careful about how well these models generalize if you try to use them somewhere else.

Lu: That’s a valid point, and it ties into what they say about performance degradation when dealing with small and distant objects like far-away pedestrians.

Tom: Exactly. The transformer model, RF-DETR, did better in complex and occluded scenes than the CNN-based architectures like YOLO11, but YOLO11 still had a favorable trade-off for real-time applications.

Jane: So what does this all mean for us as people who just use these modes of transport? It’s about building systems that can predict trajectories based on distinct behaviors, right?

Lu: That’s the big picture. The distinction between an active rider and a stationary vehicle is essential for safety and trajectory prediction because a vehicle with a rider is a dynamic entity with immediate kinetic potential and intent, whereas a parked vehicle is just static obstacle.

Meng: I'm thinking about what this means for monitoring systems. If we can reliably detect these specific interactions, it could help in automated counting of vulnerable road users in urban planning scenarios.

Tom: And you’ve got the model weights available at that link, which means researchers and developers don't have to start from scratch when they want to test safety or traffic management applications.

Jane: So what’s the final word on this paper, "MicroVision: An Open Dataset and Benchmark Models for Detecting Vulnerable Road Users and Micromobility Vehicles"?

Lu: It gives us a specialized resource for understanding complex interactions in urban micromobility environments by introducing that state-aware classification strategy.

Tom: And the benchmarking showed that transformer-based models, like RF-DETR, achieved superior accuracy in complex scenes, which suggests that attention mechanisms might offer better scene comprehension in dense city settings.

Jane: So we have a dataset and models ready to help build assistance systems for drivers and riders because they can clearly separate the different types of road users on the road.

Meng: From what I see, it’s a solid foundation for research, but we have to remember that generalizing it beyond Gothenburg is going to be a challenge.

Lu: That’s fair. The dataset captures the visual context of sidewalks and cycle paths, and by using that state-aware annotation, they provide something much richer than what most public datasets offer.

Tom: So the next step seems to be taking these models and applying them to actual traffic safety research or perhaps setting up systems for automated counting in urban planning.

Jane: That’s right. It moves us closer to systems that can better understand the dynamic interplay between people and micromobility vehicles on shared infrastructure.

Conclusion: Tom: So we’re wrapping up our look at MicroVision, an open dataset and benchmark models for detecting vulnerable road users and micromobility vehicles.

Jane: It’s really a collection of images and annotations focused on getting those interactions right from the user's point of view.

Tom: The authors, they set out to fix a problem where existing datasets just mix pedestrians and riders together as one general person category.

Lu: They specifically targeted that gap by including e-scooterists and stationary bikes in a way that makes sense for traffic safety research.

Meng: The real test here is how well the models perform when we actually try to use them, not just on some neat test set numbers.

Jane: And they did provide benchmark models, which means we can actually see what’s possible with this data for training things like safety systems.

Tom: Exactly. The main implication is that you can build tools that really distinguish between a person actively moving and a vehicle just sitting there.

Lu: That distinction is huge for trajectory prediction because a dynamic entity has kinetic potential, while a parked vehicle is just an obstacle waiting to be avoided.

Jane: It also helps urban planners by giving them the data to actually count how many people and bikes are using specific infrastructure like cycle paths.

Tom: So, if we can get models that work well on this MicroVision data, we can move toward systems that offer real-time assistance for both drivers and riders.

Jane: We’ll be talking next about what the actual numbers were on those benchmark results and where the dataset is geographically limited to for now.

Chalmers University of Technology

cs.CV

Submitted: 2026-03-18

Updated: 2026-03-18

Journal ref: Safety Science, Volume 205, 107371 (2027)

DOI: 10.1016/j.ssci.2026.107371

Code: https://github.com/microlab-chalmers/microvision

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Importance score: 79/100

The gist: The MicroVision dataset and benchmark models provide an open resource for detecting vulnerable road users and micromobility vehicles, addressing gaps in existing datasets by capturing scenes from a

Key concepts

MicroVision Dataset: This is an open collection of over 8,000 full-HD images recorded in Gothenburg, Sweden. It includes more than 30,000 annotated examples of pedestrians and micromobility vehicles. The data is captured from the perspective of someone riding or walking, offering a diverse view for training computer vision models.
State-Aware Labeling Strategy: This method defines objects based on their activity. For example, a cyclist or e-scooterist is labeled as one unit if they are traveling with the vehicle. A person pushing a vehicle is labeled separately as a pedestrian and a vehicle. Stationary vehicles are annotated regardless of whether they are upright or fallen.
Active vs. Stationary Distinction: The dataset uses the state-aware strategy to explicitly separate dynamic entities from static ones. Active road users (riders) represent potential kinetic movement, which is crucial for safety systems predicting behavior. Stationary vehicles, like parked bicycles, are treated as fixed obstacles in the environment.
Benchmark Models Performance: Three object-detection models were tested: YOLO11, Faster R-CNN, and RF-DETR. The transformer-based RF-DETR model achieved the highest mean average precision (mAP) of 0.726 on the test set, indicating strong capability in identifying complex or occluded objects in urban scenes.

Terminology

Benchmark — 2× in this paper · explained in 2 episodes

Summary

The MicroVision dataset and benchmark models provide an open resource for detecting vulnerable road users and micromobility vehicles, addressing gaps in existing datasets by capturing scenes from a VRU perspective. The gist: The MicroVision dataset provides an open image dataset and annotations for training and evaluating models for detecting the most common VRUs (pedestrians, cyclists, e-scooterists) and stationary MMVs (bicycles, e-scooters), from a VRU perspective<ref:2603.18192#pg6>.

Dataset Description

The MicroVision dataset consists of more than 8,000 anonymized, fullHD images with more than 30,000 carefully annotated VRUs and MMVs<ref:2603.18192#pg5>. The dataset was recorded in Gothenburg (Sweden) over an entire year and part of almost 2,000 unique interaction scenes<ref:2603.18192#pg5>. The images were derived from video footage systematically collected within the city of Gothenburg, Sweden, spanning a full annual cycle from July 2024 to June 2025<ref:2603.18192#pg4>. Footage was captured from a micromobility user’s perspective by recording the surroundings while riding an e-scooter, riding a bicycle, and walking<ref:2603.18192#pg4>. The camera was used at resolutions of 1920 × 1080 pixels at 60 frames per second<ref:2603.18192#pg4>.

Annotation Strategy

The dataset defines five classes: (1) pedestrian, (2) cyclist, (3) e-scooterist, (4) stationary bicycle, and (5) stationary e-scooter<ref:2603.18192#pg5>. A state-aware labeling strategy was adopted where a cyclist or e-scooterist was defined as the combination of the vehicle and at least one person traveling with it<ref:2603.18192#pg5>. Conversely, a person pushing a vehicle was labeled as two separate objects: a pedestrian and a vehicle<ref:2603.18192#pg5>. Stationary vehicles were annotated regardless of their orientation (upright or fallen)<ref:2603.18192#pg5>. A model-assisted semi-automated annotation workflow was employed to maximize efficiency and consistency<ref:2603.18192#pg5>.

Benchmark Models and Performance

Three state-of-the-art object-detection architectures were trained and evaluated: (1) one-stage YOLO version 11 (YOLO11; 5), (2) two-stage Faster R-CNN [30], and (3) transformer-based RF-DETR [32]<ref:2603.18192#pg5>. The evaluation used mean average precision (mAP), computed as the average over IoU thresholds from 0.5 to 0.95 in increments of 0.05<ref:2603.18192#pg5>. RF-DETR achieved the best overall performance on the held-out test set, with an overall mAP of 0.726, followed by YOLO11 (0.687) and Faster R-CNN (0.580)<ref:2603.18192#pg5>.

Key Findings and Limitations

The inter-annotator agreement analysis yielded an overall Cohen’s Kappa of 0.824 and an average Intersection over Union (IoU) of 0.887<ref:2603.18192#pg5>. Active road users exhibited higher annotation consistency than stationary vehicles<ref:2603.18192#pg5>. While RF-DETR excelled in overall performance for each object size and class, YOLO11 outperformed the other models on small pedestrians and cyclists<ref:2603.18192#pg5>. Model performance degraded for small and distant objects, such as far-away pedestrians<ref:2603.18192#pg5>. The dataset is geographically limited to Gothenburg, Sweden, which poses a challenge for model generalization to different regions<ref:2603.18192#pg5>.

Implications and Applications

The MicroVision dataset enables applications in traffic-safety research and development to build advanced driver or rider assistance systems<ref:2603.18192#pg5>. Detection outputs can support downstream tasks such as trajectory prediction that account for distinct behavioral patterns<ref:2603.18192#pg5>. The benchmark models can support retrospective traffic-safety research by enabling automated identification of road users in large-scale naturalistic video datasets<ref:2603.18192#pg5>. Furthermore, the dataset may benefit traffic management and urban planning through automated detection and counting of VRUs<ref:2603.18192#pg5>. The dataset and model weights can be accessed at https://doi.org/10.71870/eepz-jd52<ref:2603.18192#pg5>.

The MicroVision dataset provides a specialized resource for understanding complex interactions in urban micromobility environments<ref:2603.18192#pg5>. The dataset introduces a state-aware classification strategy that explicitly distinguishes between active riders and stationary vehicles<ref:2603.18192#pg5>. This distinction is essential for traffic safety and trajectory prediction, as a vehicle with a rider represents a dynamic entity with immediate kinetic potential and intent, whereas a parked vehicle constitutes a static obstacle<ref:2603.18192#pg5>. The initial benchmarking of state-of-the-art object-detection models demonstrated promising performance across architectures on unseen scenes<ref:2603.18192#pg5>. The transformer-based RF-DETR model outperformed CNN-based architectures in overall performance, achieving an mAP of 0.723 and excelling particularly at detecting partially visible and occluded objects<ref:2603.18192#pg5>. This performance gap suggests that attention mechanisms may offer superior scene comprehension in dense urban environments<ref:2603.18192#pg5>. The dataset captures the visual contexts of sidewalks and cycle paths and by employing a state-aware annotation strategy that distinguishes active riders from stationary vehicles<ref:2603.18192#pg5>. Benchmarking state-of-the-art object-detection architectures shows that transformer-based models such as RF-DETR achieve superior accuracy in complex and occluded scenes, while efficient singlestage detectors like YOLO11 offer a favorable trade-off for real-time applications<ref:2603.18192#pg5>. Together, the dataset and benchmark models provide a foundation for next-generation traffic systems that can improve the safety and comfort of micromobility users, both through real-time assistance systems and by enabling partial automation of video-based behavioral research<ref:2603.18192#pg5>. The MicroVision dataset provides a specialized resource for understanding complex interactions in urban micromobility environments<ref:2603.18192#pg5>. The dataset introduces a state-aware classification strategy that explicitly distinguishes between active riders and stationary vehicles<ref:2603.18192#pg5>. This distinction is essential for traffic safety and trajectory prediction, as a vehicle with a rider represents a dynamic entity with immediate kinetic potential and intent, whereas a parked vehicle constitutes a static obstacle<ref:2603.18192#pg5>. The initial benchmarking of state-of-the-art object-detection models demonstrated promising performance across architectures on unseen scenes<ref:2603.18192#pg5>. The transformer-based RF-DETR model outperformed CNN-based architectures in overall performance, achieving an mAP of 0.723 and excelling particularly at detecting partially visible and occluded objects<ref:2603.18192#pg5>. This performance gap suggests that attention mechanisms may offer superior scene comprehension in dense urban environments<ref:2603.18192#pg5>. The dataset captures the visual contexts of sidewalks and cycle paths and by employing a state-aware annotation strategy that distinguishes active riders from stationary vehicles<ref:2603.18192#pg5>. Benchmarking state-of-the-art object-detection architectures shows that transformer-based models such as RF-DETR achieve superior accuracy in complex and occluded scenes, while efficient singlestage detectors like YOLO11 offer a favorable trade-off for real-time applications<ref:2603.18192#pg5>. Together, the dataset and benchmark models provide a foundation for next-generation traffic systems that can improve the safety and comfort of micromobility users, both through real-time assistance systems and by enabling partial automation of video-based behavioral research<ref:2603.18192#pg5>. The MicroVision dataset provides a specialized resource for understanding complex interactions in urban micromobility environments<ref:2603.18192#pg5>. The dataset introduces a state-aware classification strategy that explicitly distinguishes between active riders and stationary vehicles<ref:2603.18192#pg5>.

Improvements for AI systems

Rough object detection via state-of-the-art architectures on MicroVision dataset using RF-DETR model with mAP of 0.726 across all classes and sizes (Table 2). This enables building robust detection systems that outperformed the other models and excelled particularly at detecting partially visible and occluded objects.
State-aware classification for dynamic entity distinction. The dataset introduces a "state-aware classification scheme that explicitly distinguishes between active riders (e.g., e-scooterists) and stationary vehicles (e.g., parked e-scooters), a distinction critical for downstream safety tasks such as trajectory prediction and risk assessment. This allows AI systems to differentiate between a dynamic entity with immediate kinetic potential and intent (active rider) versus a static obstacle" (stationary vehicle).
Enhanced model generalization across urban environments. By capturing data from Gothenburg over a full annual cycle, the system can be trained to be robust to environmental and seasonal variations typical of a Northern European urban setting, providing a foundation for improving models in regions with different infrastructure designs.