TUMTraf Dataset: Multi-sensor data sets for mobility research
High resolution data sets
With a constellation of 7 sensor stations equipped with more than 60 state-of-the-art and multi-modal sensors, and with a road network coverage of approximately 3.5 kilometers, the TUMTraf Dataset offers mobility researchers, industry partners, public authorities and policy makers high-resolution curated data sets created by capturing real-world traffic along freeways, country roads and urban traffic intersections. The data sets contain on the one hand labelled, time-synchronized and anonymized multi-modal sensor data covering area-scanning cameras, doppler radars, lidars and event-triggered cameras for a variety of traffic and weather-related scenarios, and on the other hand abstract digital twins of the traffic objects with position and trajectory-related information.
Current Releases and Registration
The TUMTraf Dataset is available for download upon registration. Please REGISTER HERE to receive the download links. The complete set currently includes the following major releases containing more than 27GB of data.
|Spatiotemporal synchronized event-based and RGB camera data set.
|Four road scenarios of a busy intersection under different weather conditions with data from two cameras and two LiDARs.
|3 different traffic scenarios from the autobahn.
|Multiple sets of randomly selected camera and lidar sequences from the autobahn.
January 2024: In the R3 release TUMTraf Event, synchronized image material between an Event-Based and an RGB camera, as well as a combined representation, is published. The data set contains over 21,900 2D bounding boxes in various traffic scenarios during the day and at night. TUMTraf Event enables, among other things, the research into data fusion of both sensor systems using a stationary intelligent infrastructure.
June 2023: In the R2 release, the focus is on traffic at a busy intersection, which we captured using labeled LiDAR point clouds and synchronized camera images. The annotations are available as 3D bounding boxes with track IDs. This release includes 4,800 camera images and 4,800 point clouds, which are annotated with a total of over 57,400 3D bounding boxes. Here, the dataset includes objects from ten classes that perform complex driving maneuvers, such as left and right turns, overtaking maneuvers, or U-turns. In addition to the dataset, we also provide the calibration data of the individual sensors so that data fusion is possible. Furthermore, we would like to refer to our TUMTraf-Devkit, which makes the processing of the data much easier. More details can be found at https://github.com/tum-traffic-dataset/tum-traffic-dataset-dev-kit.
May 2022: The R1 release comes with time-synchronized multi-sensor data recorded for 3 different traffic scenarios from the A9 autobahn. They include labelled ground truth data for a couple of typical extreme weather situations that usually occur on the autobahn during winter. Heavy snow combined with strong gusts of wind and dense fog have always posed challenges for vision-based driving assistance and automation systems. With this release we offer researchers and engineers a new set of extreme weather ground truth data for the development of robust and weather-proof vision based systems. The R1 release now also includes an extended version of the high speed crash incident on the autobahn which was made available as part of the previous release. The time periods before and after the crash can now be investigated in more detail with this extended version.
March 2022: The R0 set contains labelled multi-sensor data with a mix of random and sequential traffic scenarios from the A9 autobahn. The data from this set can be used as ground truth for the development and verification of AI-based detectors, tracking and fusion algorithms, and to understand and analyze the occurrence and the after-effects of a typical high speed crash incident on the autobahn.
The upcoming releases will contain amidst others, digital twins with information about trajectory and position, new traffic scenarios, longer sequences, and new locations including an urban traffic intersection. Keep watching this space for more.
We request users to cite this paper in your work.
Release R3 – Spatiotemporal synchronized event-based and RGB camera data set
|The TUMTraf Event Dataset contains synchronized images from an Event-Based and an RGB camera, as well as a combined representation with 21,900 2D bounding boxes. The dataset considers different traffic scenarios during the day and at night. TUMTraf Event enables, among other things, the research of the data fusion of both sensor systems using a stationary intelligent infrastructure. In this regard, our work allowed us to combine the strengths of both sensor systems while compensating for the weaknesses simultaneously. Therefore, our fusion led to a noticeable reduction in false positive detections of the RGB camera and, thus, increased detection performance. The results are shown in the video below:
Release R2 – Complex traffic scenarios at a busy intersection
|R2_S1 and R2_S2
|These sequences each contain 30 seconds of traffic events at a busy intersection, which were recorded and synchronized with two LiDARs and two cameras. The recordings were made during the day. The objects are labeled with 3D bounding boxes and unique track IDs. This enables continuous object tracking and data fusion.
|In contrast to R2_S1 and R2_S2, this sequence contains contiguous 120 seconds of traffic events at a road intersection, which were also recorded by 2 LiDARs and 2 cameras. The recordings were made during the day. The objects are labeled with 3D bounding boxes and unique track IDs, which allow continuous object tracking and data fusion.
|This sequence also contains a 30-second recording of the traffic event, which was created on a rainy night. Like the other datasets, the objects are also marked with 3D bounding boxes and unique track IDs.
Release R1 - Traffic Scenarios from the Autobahn
|This set contains a 30s long multi-sensor sequence recorded in winter under heavy snow conditions. The data consists of time-synchronized images recorded at 10fps from 4 cameras observing a 400m long test stretch from multiple perspectives. The traffic objects are labelled with 3D bounding boxes and unique ids within each sensor frame to enable subsequent object-tracking and data-fusion.
|This set contains a 30s long multi-sensor sequence recorded during heavy fog conditions.
|This set contains a 60s long multi-sensor sequence covering the time period before, during and after the occurrence of a traffic accident.