Automated Vehicle ODD Route Planning for Predicted Driving Conditions
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Solution Overview
Problem
Automated vehicles face challenges in efficiently navigating road networks due to restrictions on driving conditions, such as adverse weather, which limits their operational design domain and reduces utilization rates, as they must stop when conditions are not compliant, leading to reduced fleet efficiency.
Innovation Solution
A computer-implemented method and apparatus that determine estimated driving conditions for road segments using sensor data and supplementary information to plan routes that satisfy the vehicle's operational design domain restrictions, allowing for dynamic re-routing and scheduling to maintain motion while ensuring safety and compliance with driving conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If automated vehicles operate with strict compliance to driving condition restrictions, then safety is improved, but utilization rate decreases due to frequent stops
Solution Approach 1:
The system dynamically adjusts vehicle routes and schedules based on real-time and predicted driving conditions. Instead of static route planning, the system continuously adapts to changing environmental conditions, allowing vehicles to maintain motion by switching to alternative routes when conditions become unfavorable, thereby resolving the contradiction between safety compliance and utilization rate
Solution Approach 2:
The system performs preliminary route planning that incorporates predicted future driving conditions. By anticipating adverse conditions before they occur, the system can proactively select routes and schedules that avoid future stoppages, maintaining both safety compliance and high utilization rates through advance preparation
2Reliability
If automated vehicles stop when driving conditions are not compliant, then operational safety is maintained, but fleet efficiency reduces
Solution Approach 1:
The system implements dynamic fleet management where vehicle schedules are continuously adjusted based on predicted driving conditions. When adverse conditions are forecasted, the system dynamically reroutes vehicles or reschedules operations, preventing stops rather than reacting to them, thereby maintaining both safety and fleet efficiency
Solution Approach 2:
The system uses feedback from multiple data sources including sensor data, weather forecasts, and traffic information to continuously monitor and adjust fleet operations. This feedback loop enables the system to respond to changing conditions in real-time, optimizing both safety compliance and fleet efficiency through continuous adaptation
3Productivity
If route planning considers future driving conditions, then vehicle utilization improves, but computational complexity increases
Solution Approach 1:
The system divides the road network into discrete road segments and evaluates driving conditions for each segment independently. This segmentation allows for more manageable computational processing while still considering future conditions across the entire route, resolving the contradiction between comprehensive route planning and computational complexity
Solution Approach 2:
The system changes the temporal parameter by planning routes based on predicted future conditions rather than only current conditions. By incorporating time-based predictions and evaluating conditions at different future time points, the system optimizes vehicle utilization while managing computational complexity through parameter transformation
Data Source
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AI summary
According to an example aspect of the present invention, there is provided a method of route planning for a vehicle with automated driving capabilities, the method comprising obtaining a driving condition restriction for the vehicle, obtaining plural data items originating in plural distinct sources, determining, based at least in part on the data items, at least one estimated driving condition for each one of a plurality of road segments in a road network, each driving condition being determined for a future time interval, determining at least one route in the road network for the vehicle which includes a first road segment with a first estimated driving condition, determined for a first time interval, which satisfies the driving condition restriction and sending scheduling instructions including the at least one determined route such that the vehicle is capable of traversing the first road segment during the first time interval.