Neural Driver Assignment Using Sensor Profiles and Route Ranking
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Solution Overview
Problem
Current fleet management software lacks the ability to efficiently assign drivers and vehicles for delivery tasks due to the absence of data from vehicle sensors and road conditions, leading to suboptimal decision-making.
Innovation Solution
A system that utilizes neural networks to analyze sensor data from dashcams, GPS, IMU, and ELDs to generate driver profiles, and then assigns drivers and routes based on safety risks and efficiency, using a multimodal deep learning model to optimize delivery tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual driver assignment methods are used, then system complexity is low, but assignment efficiency and accuracy deteriorate
Solution Approach 1:
The patent replaces manual driver assignment (mechanical human decision-making) with an automated neural network-based assignment system. The neural network processes driver profiles, task requirements, and route characteristics to automatically assign drivers, eliminating manual intervention and significantly improving assignment efficiency while maintaining systematic control.
Solution Approach 2:
The system enables self-service by allowing the assignment system to automatically evaluate driver profiles against task requirements and make assignments without human intervention. The neural network independently processes multiple data sources (driver behavior data, task characteristics, route information) and generates optimal assignments autonomously.
2Measurement precision
If driver assignment is performed without sensor data, then data processing requirements are low, but assignment accuracy deteriorates
Solution Approach 1:
The system performs preliminary actions by collecting and processing sensor data (driver behavior data from dashcams, GPS data, IMU data, ELD data) before the actual assignment decision. Driver profiles are generated in advance by analyzing historical sensor data, enabling the neural network to make accurate assignments based on pre-processed comprehensive driver characteristics rather than raw data at the moment of assignment.
Solution Approach 2:
The neural network serves multiple functions: it processes diverse data types (video data, GPS coordinates, acceleration data, logging data), generates driver profiles, evaluates task requirements, analyzes route characteristics, and makes assignment decisions. This multi-functional approach consolidates multiple processing tasks into a single unified system, managing data processing requirements efficiently while maintaining high assignment accuracy.
3Reliability
If diverse driver sources are used, then driver selection quality improves, but identification complexity increases
Solution Approach 1:
The patent merges multiple driver identification sources (dashcam video data, GPS location data, IMU motion data, ELD logging data) into a unified driver profile. The neural network integrates these diverse data streams by processing them through shared layers and combining their contributions to generate a comprehensive driver representation, improving identification reliability while managing complexity through unified processing architecture.
Solution Approach 2:
The driver profile serves as an intermediary that bridges diverse identification sources and the final assignment decision. Instead of directly comparing and reconciling multiple raw data sources, the system first transforms them into a standardized driver profile format that captures essential driver characteristics, making the subsequent assignment process simpler and more reliable.
Data Source
AI summary
A management server system may obtain data associated with a vehicle. The management server system may obtain the data from sensors and a dashcam within the vehicle. The management server system may determine a driver and/or vehicle profile based on the collected data and make assignments of drivers and/or vehicles to particular delivery tasks. The driver and/or vehicle profile may be generated by applying an encoder of a neural network. A second ranking neural network can be applied to generate ranking scores for each driver and/or vehicle for a particular delivery task, which can be used to select the optimal driver and/or vehicle for the delivery. The management server system may apply the driver and/or vehicle profile with potential routes for a delivery task to a neural network to identify a recommended route for the delivery task.