Park 'N Fly

Off-airport parking providers are leveraging cutting-edge technologies to elevate customer experience and streamline operations. One transformative technology that is making a significant impact is the use of tensor processing units (TPUs) at the edge to power machine learning applications. TPUs, specialized hardware accelerators developed by Google for machine learning workloads, are enabling off-airport parking providers to deploy AI cashiers, AI car abandonment monitors, and improve license plate recognition (LPR) at the edge. Here are three key advantages that off-airport parking companies like Park N Fly can achieve by utilizing TPUs at the edge:

AI cashiers for seamless and efficient customer interactions: TPUs at the edge can power AI-based cashier systems that automate the payment process for off-airport parking customers. AI cashiers can analyze license plate data, calculate parking fees, and process payments in real-time, eliminating the need for manual cashiers or payment kiosks. This will have a positive influence on employee efficiency and create more time for employees to complete other tasks. Additionally, This enables a frictionless and convenient experience for customers, reducing wait times and improving overall satisfaction.

AI car abandonment monitors for proactive customer service: TPUs at the edge can enable AI-powered car abandonment monitors that can detect and notify parking staff when a vehicle is left unattended for an extended period. This helps off-airport parking providers proactively manage abandoned or suspicious vehicles, ensuring the safety and security of the parking facility and its customers. This allows customers to have more confidence in Park N Fly’s use of technology.

 

TPUs at the edge can power AI-based cashier systems that automate the payment process for off-airport parking customers

 

Improved LPR at the edge for enhanced security and efficiency: TPUs at the edge can accelerate License Plate Recognition (LPR) processes, enabling more accurate and efficient identification of vehicles entering and exiting the parking facility. Improved LPR at the edge can detect license plates, match them with customer profiles, and trigger automated actions such as opening gates or sending notifications in real-time.

In conclusion, continuous learning is one of the many aspects of AI that makes the tool so enticing. It learns from our mistakes at an inhuman pace and can make corrections and optimize itself very quickly. We at Park N Fly have embraced this technology. By harnessing the power of TPUs and machine learning at the edge, off-airport parking providers can elevate customer experience, optimize operations, and stay ahead in a competitive market.