Contactless Vehicle Ordering Automation System
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Solution Overview
Problem
Conventional drive-through ordering systems face inefficiencies due to limited space, long customer ordering and payment times, unavailability of food items, and increased risk of virus transmission during the COVID-19 pandemic, necessitating a more automated and streamlined solution.
Innovation Solution
A computing system equipped with a camera, processors, and displays at kiosks and electronic devices, utilizing machine learning and AI to recognize users via license plate or face recognition, providing user-tailored menus, and enabling contactless payment and order transmission to food preparation stations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional drive-through ordering systems are used, then staff can assist customers with ordering and payment, but customer ordering time and payment time increase, reducing service speed
Solution Approach 1:
The system enables self-service through automated vehicle identification via license plate recognition, automatic user account retrieval, and electronic display of order options at the vehicle. Customers can place orders and make payments without staff intervention, eliminating waiting time and accelerating service speed.
Solution Approach 2:
The system performs preliminary actions by automatically identifying the vehicle and retrieving the associated user account information before the customer even approaches the ordering point. This pre-loading of customer data eliminates the time required for manual identification and account setup.
2Object-affected harmful factors
If staff are present to assist with ordering and payment, then customer service quality improves, but the risk of virus transmission increases due to increased human interaction
Solution Approach 1:
The system extracts and eliminates the need for staff interaction in the ordering and payment process. Vehicle identification, account retrieval, order display, and payment processing are all automated, removing the source of virus transmission risk while maintaining service quality through technology-driven operations.
3Area of stationary object
If drive-through space is limited, then the restaurant footprint is reduced, but service efficiency decreases due to bottlenecks in the ordering process
Solution Approach 1:
The system transitions the ordering interface from a physical counter-based dimension to a vehicle-based mobile dimension. Orders are displayed and confirmed at the vehicle through electronic displays, allowing multiple vehicles to be served simultaneously without requiring expanded physical drive-through space, thus maintaining service efficiency.
4Extent of automation
If manual ordering processes are used, then flexibility in handling various order scenarios is maintained, but ordering time increases and automation level remains low
Solution Approach 1:
The system replaces mechanical manual processes with automated electronic systems. License plate recognition cameras automatically identify vehicles, databases automatically retrieve account information, electronic displays automatically present order options, and payment systems automatically process transactions. This substitution dramatically reduces ordering time while increasing automation level.
Data Source
AI summary
A computing system, includes: a non-transitory memory; processors coupled to the non-transitory memory and configured to execute instructions to perform operations including: detecting a vehicle at a first location, executing instructions local to the first location by one or more edge processors to determine vehicle specific parameters that identify the vehicle; determining the vehicle is associated with a user account stored in a computerized vehicle management system; in response to the determining that the vehicle is associated with the user account, and transmitting instruction to a first display at the first location to present a user-tailored menu generated by using the edge processors to access a machine learning computer model based on data obtaining from the user account.