Sensor-Based Driver Activity Inference for Delivery Automation
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Solution Overview
Problem
Delivery drivers face burdensome interactions with mobile applications during their daily tasks, such as navigation and package handling, which increases service time and distracts them, especially when carrying multiple packages or handling overweight items.
Innovation Solution
A peripheral device with sensors like GPS, IMU, and cameras, integrated with machine learning routines, automatically infers driver activity and location, providing contextual assistance without manual input, such as navigation and package scanning, through a networked environment that includes a computing environment and client devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If delivery drivers manually interact with mobile application during delivery tasks, then navigation and package scanning can be performed, but service time increases and driver distraction occurs
Solution Approach 1:
The system enables self-service by using sensors to automatically detect driver location and activity state, then autonomously triggering navigation and package scanning operations without requiring driver intervention. The mobile device performs these tasks itself based on sensor data, eliminating the need for manual app interaction.
Solution Approach 2:
The system performs preliminary actions by pre-configuring automatic triggers that activate navigation when location data indicates the driver is at a delivery address, and pre-setting automatic package scanning when the driver picks up a package. These actions are prepared in advance and execute automatically based on detected conditions.
2Ease of operation
If delivery drivers manually interact with mobile application during delivery tasks, then navigation and package scanning can be performed, but driver distraction and safety concerns increase
Solution Approach 1:
The system enables self-service by using sensors to automatically detect driver location and activity state, then autonomously triggering navigation and package scanning operations without requiring driver intervention. The mobile device performs these tasks itself based on sensor data, eliminating the need for manual app interaction.
Solution Approach 2:
The system replaces manual mechanical interaction with the mobile application through sensor-based automatic detection and triggering. Instead of the driver manually operating the device, sensors detect physical conditions (location, movement, package handling) and automatically initiate appropriate actions, substituting manual control with automated sensor-driven control.
3Productivity
If automatic inference of driver activity using sensor data is implemented, then manual interactions are reduced and productivity improves, but device complexity increases
Solution Approach 1:
The system achieves multi-functionality by using a single sensor array to perform multiple detection functions: GPS sensors track location for navigation triggering, accelerometers detect package pickup/delivery actions, and the unified system handles both navigation and package scanning operations. This universal sensor-based approach reduces overall system complexity compared to separate specialized systems.
Data Source
AI summary
Various embodiments are disclosed for providing machine learning routines with peripheral device data to infer driver activity and location. Peripheral device data may be collected on a peripheral device having a machine learning routine executing thereon to infer driver activity and perform improved estimation of driver location. Using driver activity and location estimation, contextually relevant delivery workflow assistance may be automatically provided to a delivery driver or other individual without requiring manual input, thereby improving driver safety and operational efficiency.