Kodiak Partners with Kognic To Enhance Reliability and Performance of AI Pipelines for Autonomous Trucking
Two Technology Leaders Boost the Performance of Kodiak’s AI models with High-Quality Data Labeling
December 9, 2024 – Dearborn, MI – Kognic, the industry-leading annotation platform for sensor-fusion data, today announced Kodiak Robotics, a leading autonomous truck developer, has chosen Kognic’s data labeling platform to scale its annotation pipelines reliably and efficiently
“Kognic’s high-quality data labeling capabilities have helped Kodiak further enhance our AI models, which are critical for the safe deployment of our autonomous trucking technology in real-world environments,” said Andreas Wendel, CTO, Kodiak Robotics. “Kognic’s platform has a unique capacity to handle time-series data, which has improved our ability to reliably label multi-sensor data. Additionally, integrating Kodiak’s advanced pre-labeling capabilities with Kognic’s platform has allowed us to automate our AI annotation pipeline, building an AI flywheel and further improving the capability of our AI models.”
Kognic provides the industry’s most productive annotation platform for sensor-fusion data in performance-critical, AI applications such as autonomous driving. Kognic’s annotation platform optimizes datasets by merging sensor data from radar, LiDAR and cameras via intuitive interfaces for visualizing complex objects and sequences. Kognic’s solution has become a core toolset for autonomous vehicle developers, and is currently being used by technology leaders such as Qualcomm, Bosch, Continental and Zenseact, which provide systems that power vehicles for global OEMs such as BMW and Volvo Cars.
“We are honored to have been selected to support Kodiak, the trusted leader in autonomous ground transportation,” said Daniel Langkilde, CEO and Co-Founder at Kognic. “We believe that the key to deploying trusted and high-performing perception systems lies in the ability to more effectively understand and visualize data labels. Our collaboration is focused on optimizing and improving the efficiency of Kodiak’s annotation pipelines, and we look forward to supporting their progress every step of the way.”