Karl Wunderlich

James O'Hara, Karl Wunderlich, Gregory Stevens

As autonomous vehicles slowly deploy into urban roads for limited use cases with significant edge case issues, closed facilities like marshaling yards provide a ripe case for combining lower-level vehicle autonomy with fixed infrastructure to create full autonomy without similar edge case concerns. Within a delivery marshaling yard, electric fleet vehicles complete a set of sequential tasks (charging, inspection, cleaning, and loading) before exiting the yard with their new load of deliveries. Hybrid automation of the vehicles and infrastructure can allow these vehicles to reach full autonomy and navigate the facility without the need of a driver, allowing for quicker movement between tasks increasing vehicle throughput. However, isolated autonomous operations based on static rules are prone to gridlock causing facility failures that temporarily shut down operations. Our orchestrated autonomy solution uses decentralized, dynamic priority scoring of vehicles based on the current status of the marshaling yard to optimally assign vehicles to tasks to increase vehicle throughput. Using a simulated facility with three marshaling yard sizes (small, medium, and large) and three demand levels (low, medium, high), we demonstrated that our orchestration solution increases vehicle throughput above static, isolated autonomy for all combinations of yard size and demand, while reducing facility failures at high demand levels.

Ryan Rubel, Andrew Dudash, Mohammad Goli et al.

Learned object detection methods based on fusion of LiDAR and camera data require labeled training samples, but niche applications, such as warehouse robotics or automated infrastructure, require semantic classes not available in large existing datasets. Therefore, to facilitate the rapid creation of multimodal object detection datasets and alleviate the burden of human labeling, we propose a novel automated annotation pipeline. Our method uses an indoor positioning system (IPS) to produce accurate detection labels for both point clouds and images and eliminates manual annotation entirely. In an experiment, the system annotates objects of interest 261.8 times faster than a human baseline and speeds up end-to-end dataset creation by 61.5%.