A9-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 a9-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 A9-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 17GB of data.

Data set Description
R1 3 different traffic scenarios from the autobahn.
R0 Multiple sets of randomly selected camera and lidar sequences from the autobahn.

Release notes

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.


A scientific paper describing the A9-Dataset has been accepted for publication at the 33rd IEEE Intelligent Vehicles Symposium to be held between the 5th and 9th of June 2022 at Aachen, Germany. A preprint version is available HERE. We request users to cite this paper in your work.

Release R1 - Traffic Scenarios from the Autobahn

R1_S1 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.
R1_S2 This set contains a 30s long multi-sensor sequence recorded during heavy fog conditions.
R1_S3 This set contains a 60s long multi-sensor sequence covering the time period before, during and after the occurrence of a traffic accident.

R1_S1 R1_S2 R1_S3

Release R0 - Random Camera and Lidar Sequences from the Autobahn

R0_S1 This set contains a random selection of around 600 images from four cameras equipped with two types of lenses. They are are mounted on overhead gantry signs along the A9 autobahn. The traffic objects are labelled with 3D bounding boxes and classified into one of 7 categories.
R0_S2 This set contains a 25s sequence of images from a camera on the A9 autobahn and also captures a number of lane change maneuvers. Apart from 3D bounding boxes and object classes, unique track-ids are also assigned to the traffic objects.
R0_S3 and R0_S4 These sets contain two different sequences of lidar point-cloud frames from overhead gantry bridges on the A9 freeway and are accompanied by labels including 3D bounding boxes and object classes.

R0_S1 R0_S2 R0_S3 and R0_S4

Further Articles

November 23,2021

Traffic: Added value by big data

A huge amount of data is needed as to distinguish cars from motorcycles and bicyclists, to recognize maneuvers, to analyze accidents and to simulate traffic.


September 26,2020

Bird’s-eye view: More precision for the digital twin

To find out how accurate the measurements of the digital twin are, fortiss and the DLR dispatched a helicopter with an integrated camera system. This allowed them to determine the exact position of the vehicles on the highway.


July 31,2021

Predicting movements by autonomous agents

To predict movements, many trajectory options must be taken into consideration and prioritized. The FloMo model works with probabilities and has been trained on the basis of three datasets. Its performance is improved by a proposed method of stabilizing training flows.