Navigation Route Selection Using Real-Time Vulnerable Road User Data
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Solution Overview
Problem
Current systems face challenges in efficiently detecting and mapping vulnerable road users (VRUs) over wide geographic areas, which is resource-intensive and difficult due to dynamic movements, making it hard to provide real-time data for autonomous driving applications.
Innovation Solution
A computer-implemented method and apparatus that generates and uses VRU data by querying geographic databases for candidate navigation routes, incorporating sensor data and VRU messages from vehicles to determine the presence or patterns of VRUs on road links, enabling vehicles to adjust routes and sensor usage accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If VRU data is collected over wide geographic areas using traditional methods, then comprehensive VRU coverage is achieved, but resource consumption increases significantly
Solution Approach 1:
Vehicles equipped with sensors independently detect and report VRU data to the geographic database, eliminating the need for dedicated mapping resources. Each vehicle serves itself and contributes to the collective database, achieving comprehensive coverage without proportional increase in centralised resource consumption.
Solution Approach 2:
The geographic database serves multiple functions: storing VRU data, providing routing information, and enabling real-time queries for navigation. This multi-functionality consolidates what would otherwise require separate systems, reducing overall resource consumption while maintaining comprehensive VRU coverage.
2Quantity of substance
If traditional VRU mapping methods are used, then VRU data is obtained, but the process is difficult due to dynamic movements of VRUs
Solution Approach 1:
The system transitions from static VRU mapping to dynamic real-time detection. Sensors on moving vehicles continuously detect VRUs in real-time, adapting to their dynamic positions and movements. This dynamic approach inherently handles the difficulty of tracking moving VRUs by making the detection system mobile and continuous rather than fixed and periodic.
Solution Approach 2:
The system implements continuous feedback loops where sensors detect VRUs, data is immediately updated in the geographic database, and this information is real-time queried for routing decisions. This continuous feedback mechanism ensures VRU data remains current despite dynamic movements, making detection and response straightforward through iterative refinement.
3Reliability
If real-time VRU data is provided for autonomous driving, then road safety improves, but data collection becomes more resource-intensive
Solution Approach 1:
Vehicles independently perform real-time VRU detection and reporting without requiring dedicated centralised collection infrastructure. Each vehicle's sensor system serves its own safety needs while contributing to the collective database, achieving enhanced road safety through distributed rather than集中ised resource consumption.
Solution Approach 2:
The geographic database acts as an intermediary that consolidates VRU data from multiple vehicles and provides it back to them for routing decisions. This intermediary approach allows efficient sharing of detection resources while maintaining real-time safety information, reducing the overall resource burden compared to each vehicle independently maintaining full detection capabilities.
Data Source
AI summary
An approach is provided for determining a navigation route based on vulnerable road user data. The approach, for example, involves generating one or more candidate navigation routes for a vehicle. The approach also involves querying a geographic database for vulnerable road user data for a respective set of road links comprising each of the one or more candidate navigation routes. The approach further involves selecting the navigation route from among the one or more candidate navigation routes based on the vulnerable road user data.