Cyngn, a developer of AI-powered autonomous driving solutions for industrial applications, today announced that they have made significant progress adapting DriveMod to enable autonomous forklifts.
The autonomous forklift development effort is spearheaded by Cyngn’s previously announced ongoing paid contract with a separate end-user in the building materials industry. That engagement has yielded AI-powered autonomous vehicle capabilities on an electric BYD forklift.
The powerful material handling vehicle capabilities offered by BYD, a renowned global brand, in conjunction with Cyngn's advanced AI-driven autonomous technology, DriveMod, will facilitate the development of a groundbreaking autonomous forklift. This innovative solution effectively tackles labor shortage and consistency challenges faced by organizations, while simultaneously enhancing safety measures. Cyngn is targeting 2024 for the commercial launch of its autonomous forklift and invites prospective customers to join the waitlist for early access.
Key features of the DriveMod-enabled forklift include:
- Flexible Pallet Detection: Uses proprietary AI and computer vision to detect and analyze pallet dimensions in real-time, creating flexibility to work with standard, non-standard, and custom pallet sizes.
- Unparalleled Safety: Applies DriveMod’s commercially released multilayer perception framework that uses deep learning, machine learning, and basic collision avoidance to equip autonomous forklifts with safety redundancies and a 360° field of view while unloaded and loaded.
- Industry-Leading Load Capacity: Load capacity of 10,000 lbs. with the ability to stack multiple units fully autonomously.
According to Allied Research Market, the global forklift market was valued at $51.6 billion in 2021 and is projected to reach $103.9 billion by 2031, a strong indication of the widespread utilization of forklifts in material handling operations worldwide. By leveraging the latest in autonomous industrial vehicle technology, companies using forklifts can eliminate the safety risks and delays associated with manual pallet transport workflows.