  This paper presents a challenging multi-agent seasonal dataset collected by a
fleet of Ford autonomous vehicles at different days and times during 2017-18.
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. 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). We also provide the output of
state-of-the-art reflectivity-based localization for bench-marking purposes.
The dataset can be freely downloaded at our website.
