AV dataset benchmarks 2026 quality comparison: nuScenes, KITTI, Argoverse

In 2026, the autonomous driving dataset ecosystem continues to evolve, offering new opportunities for dataset comparison and benchmark evaluation to train and evaluate perception, tracking, and motion prediction models. Classic benchmarks such as nuScenes, KITTI, and Argoverse remain the primary references, but each has its own strengths, limitations, and annotation features that affect model accuracy.

In this article, we compare data quality and suitability for different tasks to help developers choose the optimal resource for specific autonomous driving scenarios.

Key Takeaways

• We define the comparison area for perception-to-prediction pipelines across nuScenes, KITTI, and Argoverse.
• Sensor design and annotation policies affect model performance more than dataset size.
• nuScenes emphasizes multimodal fusion.
• Argoverse scales prediction and map connectivity.
• KITTI is compact and has a history.
• Careful benchmark evaluation and dataset comparison help identify the datasets with the most reliable annotation quality metrics and minimize dataset bias.

Strengths, weaknesses, and best use cases

In autonomous transportation, the choice of dataset directly affects the quality of training and validation for computer vision and sensor-fusion models. The most commonly used are nuScenes, KITTI, and Argoverse. Each has its own strengths, limitations, and best use cases.

Sensor suites and coverage: cameras, lidar, radar, and synchronization

In autonomous driving datasets such as nuScenes, KITTI, and Argoverse, sensor suites serve as the basis for high-quality environmental perception.

Typically, cameras, lidar, and radar are used to provide complete multimodal coverage of the scene. The cameras are responsible for texture information, colors, road signs, and markings.

Lidar provides accurate 3D geometry of the space and distances to objects.

Radar adds reliability in difficult weather conditions and allows you to measure the speed of objects.

An important aspect is spatial coverage. Modern datasets use multiple cameras for a 360° view around the vehicle, and the lidar is installed to minimize "blind spots". Radar sensors complement the system in the forward and lateral directions. However, the sensor suite itself is only part of the data quality. Accurate time synchronization across all streams is critical, as even a slight delay between the camera frame and the lidar point cloud can lead to errors in sensor fusion and inaccurate 3D annotations.

In addition to time synchronization, calibration is also essential, both intrinsic and extrinsic, which ensures the correct alignment of coordinates between different sensors. High-quality datasets provide ready-made calibration parameters and metadata, enabling the construction of accurate perception models without additional processing of raw data.

The combination of a rich sensor stack, wide coverage, and proper synchronization determines a dataset's suitability for 3D detection, tracking, and sensor fusion tasks.

Scope and annotation density: classes, attributes, trajectories, and privacy

The scope of autonomous driving datasets such as nuScenes, KITTI, and Argoverse is determined by the quality and density of annotations. These datasets provide detailed object labeling in camera frames and lidar point clouds, including classes for vehicles, pedestrians, and cyclists, as well as additional attributes such as traffic state, direction, object type, and behavioral characteristics. In many cases, object trajectories over time are also provided, enabling the training of motion prediction and forecasting models.

The density of annotations ranges from 2D bounding boxes to 3D boxes with time sequences, semantic labels, and sensor metadata. Some datasets contain hundreds of thousands of frames with complete annotation of all visible objects, which is required for tracking, route planning, and multi-agent modeling tasks.

Privacy is also important, with data processed in a way that hides personal information, such as blurring faces and license plates, making the datasets safe for scientific and commercial applications. Due to their combination of broad applicability, high annotation density, and compliance with privacy regulations, these datasets remain the foundation for the development of modern autonomous driving systems and computer vision research.

HD maps and scene context: raster and vector lane centerlines

HD maps and scene context play a key role in improving perception and traffic planning accuracy. Different formats are used to represent traffic information, including raster and vector lane centerline maps, top-down semantic grids, and centerline graphs.

Each format has its own advantages and trade-offs, especially for BEV (Bird's Eye View) tasks, where the accuracy of object and lane locations is important.

Evaluation metrics that matter: AP, ATE/ASE/AOE, NDS/CDS, and prediction scores

In the field of autonomous driving, perception and motion prediction models are evaluated using various metrics that help compare the quality of detection, tracking, and trajectory prediction. Each metric emphasizes a particular aspect of the model's performance.

1. AP (Average Precision) measures the accuracy of object detection, takes into account true positives and false positives, and is used to evaluate 2D and 3D detection.
2. ATE (Average Translation Error) is the average error in the object's 3D position, which assesses localization accuracy.
3. ASE (Average Scale Error) is the average error in the object's size, indicating how accurately the model estimates its dimensions.
4. AOE (Average Orientation Error) is the average error of the object's orientation, used for the correct location of objects on the map and movement planning.
5. NDS (NuScenes Detection Score) is an integral indicator of detection quality in nuScenes that combines AP with position, scale, and orientation errors, enabling comprehensive comparisons across models.
6. CDS (Center Distance Score) evaluates the accuracy of the object's center location, especially in multimodal detections.
7. Forecasting Scores are metrics for motion forecasting, such as ADE (Average Displacement Error) and FDE (Final Displacement Error), that measure the deviation between predicted and actual trajectories.

Depth of motion prediction: Argoverse's advantage over the smaller-scale nuScenes

In motion forecasting tasks, the depth of prediction, i.e., the time horizon over which the model predicts the future position of road users, is important. Argoverse has an advantage over nuScenes due to its larger dataset, detailed trajectory annotations, and focus on long-term object behavior.

Argoverse includes thousands of pedestrian and vehicle trajectories with accurate spatiotemporal descriptions spanning up to a few seconds. This allows training models to predict not only short-term movements but also complex maneuvers in urban environments.

In nuScenes, there is multimodal sensor data and 3D annotations. Still, the time horizon for prediction is limited to shorter segments and fewer complete trajectories, which makes it less optimal for long-term motion prediction tasks.

Thus, for motion forecasting over a few seconds, Argoverse provides better data quality and greater potential for model training, while nuScenes is better suited for complex 3D perception and sensory fusion than for deep motion forecasting.

Quality pitfalls: geographic leakage, class imbalance, and annotation limitations

When working with datasets, you need to consider potential issues that can affect the performance of perception and motion forecasting models, such as dataset bias, and rely on annotation quality metrics to detect them.

Ensuring quality in the language of vision and space

Ensuring high-quality data requires an approach that combines information in the language of vision and space. Datasets such as nuScenes and Argoverse include multimodal sensor data, including cameras, LiDAR, and RADAR. This allows models to combine textural, geometric, and dynamic information for object recognition, localization, and motion prediction.

Also, specialized datasets such as CARScenes provide greater detail on scene context, including HD maps, trajectories, and semantic annotations. This allows the model to integrate spatial patterns with the visual characteristics of objects, thereby improving the accuracy of perception and tracking even in complex urban environments.

Combining multisensory data from nuScenes/Argoverse with detailed contextual annotations from CARScenes provides a multi-level foundation for training models, with visual and spatial signals reinforcing each other. All this reduces detection errors, improves the quality of traffic prediction, and enhances the model's ability to generalize to new conditions in terms of road users' behavior.

Dataset ecosystem 2025-2026

In 2025–2026, the dataset ecosystem expanded beyond the classic KITTI, nuScenes, and Argoverse resources. These three datasets continue to play a key role in the development of perception and prediction models, with new releases and research complementing their limitations and expanding their capabilities.

One of the leading areas of expansion is specialized datasets for perception and prediction that go beyond simple 3D detection. For example, the ROVR-Open-Dataset provides a large dataset for depth estimation, complementing the capabilities of depth estimation models and enabling generalization in complex environmental conditions.

In addition, datasets focused on interpreted semantics and multi-aspect annotations are emerging. The CAR-Scenes (Semantic VLM Dataset for Safe Autonomous Driving) project has been providing over 350 attributes covering object behavior and high-level scene context for training vision-language models since 2025. This allows not only to recognize objects, but also to assess risks and contextual factors behind the scenes.

Another PAVE, a new dataset collected entirely autonomously on real production cars, contains thousands of hours of naturalistic data and detailed trajectories to evaluate model behavior in real-world conditions. This approach helps overcome the limitations of classic datasets.

We see that the modern dataset ecosystem has become richer. Ongoing dataset comparison and benchmark evaluation ensure that developers can assess annotation quality metrics while accounting for dataset bias when choosing appropriate resources.

FAQ

What are the main differences in sensor sets between nuScenes, KITTI, and Argoverse?

The main difference among sensor sets is that KITTI uses a limited set of cameras and LiDAR for a frontal perspective, nuScenes provides 360° coverage with cameras, LiDAR, and RADAR, and Argoverse focuses on LiDAR with additional HD maps and trajectory data for motion forecasting.

How does the amount of annotation vary in these benchmarks?

KITTI has small, basic 3D/2D labels, nuScenes has multimodal 3D tracks and semantic annotations, and Argoverse and the new releases in 2025-2026 have even more detailed trajectories, HD maps, and object attributes for motion forecasting.

What evaluation metrics are most important when comparing these benchmarks?

Important metrics when comparing autonomous driving benchmarks are AP for detection, ATE/ASE/AOE for 3D localization and orientation accuracy, NDS/CDS for comprehensive perception assessment, and prediction scores (ADE/FDE) for motion forecasting.

What are some common dataset errors that developers should be aware of?

Developers should be aware of geographic leakage, class imbalance, and limited or inaccurate annotations, which can distort model training and evaluation.

Which dataset is best suited for motion forecasting research?

Argoverse is an excellent tool for forecasting with hundreds of thousands of interaction-rich sequences and scenarios, as well as lane-aligned coordinates. nuScenes provides prediction benchmarks at a smaller scale but with multi-sensor context.