Vehicle Routing Optimization via Automated Driving Reliability
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Solution Overview
Problem
Automated and assisted driving systems face challenges in varying driving environments due to infrastructure, road conditions, and presence of pedestrians or cyclists, leading to inconsistent performance.
Innovation Solution
A method for optimizing vehicle routing by determining route costs based on the reliability of automated driving or assisted driving features, considering factors like lane marking quality, road curvature, and traffic conditions, using aggregated data from multiple sources and a network-based storage system to prioritize routes with better feature availability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If automated driving systems operate in varying driving environments, then the system can handle diverse road conditions and infrastructure, but the reliability of automated driving features becomes inconsistent
Solution Approach 1:
The system performs preliminary actions by determining route costs and selecting optimal routes before the vehicle actually travels, based on aggregated drive data about lane marking quality, road curvature, and infrastructure characteristics. This advance planning ensures the vehicle operates in environments where automated driving features are most reliable, thus resolving the contradiction between adaptability to diverse environments and consistency of performance.
Solution Approach 2:
The system uses aggregated drive data from multiple sources as feedback about driving environment quality and automated driving feature reliability. By continuously gathering and analyzing data about lane markings, road curvature, and infrastructure, the system can identify patterns and make informed routing decisions that maintain reliable automated driving operation across varying environments.
2Reliability
If the vehicle selects routes based on comprehensive environmental analysis, then the reliability of automated driving features improves, but the computational complexity and data processing requirements increase
Solution Approach 1:
The system merges multiple data sources including aggregated drive data, map data, and real-time sensor data into a unified routing determination framework. By combining these diverse data streams and processing them through an integrated system that calculates route costs based on multiple factors (lane marking quality, road curvature, infrastructure), the system achieves reliable automated driving operation while managing complexity through consolidation rather than separate independent systems.
Solution Approach 2:
The routing determination system performs multiple functions: it analyzes lane marking quality, evaluates road curvature, assesses infrastructure characteristics, and calculates optimal routes all within a single multi-functional framework. This universal system handles diverse routing considerations simultaneously, improving reliability without requiring separate specialized systems for each function.
Data Source
AI summary
Systems, methods, and devices for network-based storage of vehicle and infrastructure data for vehicle optimization are disclosed. A method includes receiving sensor data from a vehicle sensor, uploading the sensor data to a network, and storing the sensor data in a vehicle map database. The method includes downloading aggregated drive data from the network and storing the aggregated drive data in the vehicle map database. The method includes optimizing a vehicle route based on route costs for a plurality of potential driving routes based on a reliability of an automated driving feature or driver assistance feature.