We present a challenging multi-agent seasonal dataset collected by a fleet of Ford autonomous vehicles at different days and times during 2017-18. The vehicles were manually driven on a route in Michigan that included a mix of driving scenarios including the Detroit Airport, freeways, city-centers, university campus and suburban neighborhood.

We present the seasonal variation in weather, lighting, construction and traffic conditions experienced in dynamic urban environments. This dataset can help design robust algorithms for autonomous vehicles and multi-agent systems. Each log in the dataset is time-stamped and contains raw data from all the sensors, calibration values, pose trajectory, ground truth pose, and 3D maps. All data is available in Rosbag format that can be visualized, modified and applied using the open source Robot Operating System (ROS).

The dataset contains full resolution time stamped data from the following sensors:

• Four HDL-32E Velodyne 3-D Lidars

• 6 Point Grey 1.3 MP Cameras

• 1 Point Grey 5 MP Dash Camera

• Applanix POS-LV IMU

The dataset also includes:

• 3D Ground Reflectivity Maps
• 3D Point Cloud Maps
• 6 DoF Ground-truth Pose
• 3 DoF Localized Pose
• Sensor Transforms and Calibration

Data Collection

The vehicles traversed an average route of 66 km in Michigan that included a mix of driving scenarios such as the Detroit Airport, freeways, city-centers, university campus and suburban neighbourhoods, etc. Each vehicle used in this data collection is a Ford Fusion outfitted with an Applanix POS-LV GNSS system, four HDL-32E Velodyne 3D-lidar scanners, 6 Point Grey 1.3 MP Cameras arranged on the rooftop for 360-degree coverage and 1 Pointgrey 5 MP camera mounted behind the windshield for the forward field of view. We present the seasonal variation in weather, lighting, construction and traffic conditions experienced in dynamic urban environments.