In-Vehicle Payment Detection Using Sensors, ML, and Digital Wallets
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional payment systems require user involvement, are influenced by user intent, and face challenges such as high infrastructure costs, inefficiencies in real-time billing, difficulty in detecting traffic violations, and unreliable connectivity for vehicle transactions.
Innovation Solution
An in-vehicle transaction system integrating machine learning, sensor data processing, and blockchain technology to autonomously manage transactions, including toll payments, parking fees, and traffic violations, using in-machine digital wallets with tamper-proof storage and privacy-preserving sensor data authorization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional payment systems are used, then user involvement is required, but user engagement is passive and user intent influences the process
Solution Approach 1:
The system enables vehicles to autonomously detect payment-related events (toll gates, parking, traffic violations) using sensor data and machine learning models, then automatically process payments through integrated digital wallets without requiring driver intervention. The vehicle serves itself by detecting events, calculating fees, and executing transactions autonomously.
Solution Approach 2:
The system pre-configures payment parameters, digital wallets, and machine learning detection models before the vehicle encounters payment events. When events occur, the system has pre-established protocols ready to execute immediate payment processing without requiring real-time user decision-making or manual setup.
2Reliability
If conventional payment infrastructure is deployed, then transactions can be processed, but infrastructure costs are high
Solution Approach 1:
The system uses a multi-functional approach where vehicle sensors serve multiple purposes: detecting toll gates, monitoring parking events, identifying traffic violations, and triggering payments. The same sensor infrastructure handles diverse payment scenarios without requiring separate dedicated systems for each function, reducing overall infrastructure costs.
Solution Approach 2:
Instead of deploying expensive physical infrastructure at every payment point (toll gates, parking lots), the system uses digital copies and representations of these locations through machine learning models that recognize visual features from sensor data. This replaces physical infrastructure with computational equivalents.
3Measurement precision
If real-time billing is implemented, then accurate charging is achieved, but system complexity increases
Solution Approach 1:
The system segments the payment processing function into independent modular components: sensor data collection, machine learning event detection, fee calculation module, and payment execution. Each module operates independently and can be optimized separately, reducing overall system complexity while maintaining billing precision through specialized processing at each stage.
4Measurement precision
If sensor data is processed for payment detection, then accurate event recognition is achieved, but data processing requirements increase
Solution Approach 1:
The machine learning models perform partial analysis by focusing computational resources only on detecting specific payment-related patterns in sensor data rather than processing all possible data types. The system uses selective attention to identify toll gates, parking events, and violations without unnecessarily analyzing every sensor reading in detail, reducing energy consumption while maintaining detection accuracy.
Data Source
AI summary
Aspects of the present disclosure describe a system comprising a processor and memory storing instructions, which when executed by the processor, enable the system to detect a payment-related event using vehicle sensors, such as a camera or GPS unit. The detection can involve a machine learning model trained to recognize objects associated with the payment-related event while minimizing false positives. Upon detecting the event, the system can handle the electronic payment by utilizing an in-vehicle digital wallet to execute the payment and recording the transaction on a blockchain-based ledger.