Boreas: A Multi-Season Autonomous Driving Dataset

Lidar To date, autonomous vehicle research and development has focused on achieving sufficient reliability in ideal conditions GNSS/IMU Camera such as the sunny climates observed in San Francisco, California or Phoenix, Arizona. Adverse weather conditions such as rain and snow remain outside the operational envelope for many of these systems. Additionally, a majority of self-driving vehicles are currently reliant on highlyaccurate maps for both localization and perception. These maps are costly to maintain and may degrade as a result of seasonal changes. In order for self-driving vehicles to be deployed safely, these short-comings must be addressed. To encourage research in this area, we have created the Boreas dataset, a large multi-modal dataset collected by driving a repeated route over the course of one year.