Autonomous Vehicle Route and Speed Planning for Adverse Weather
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Solution Overview
Problem
Autonomous vehicles face reliability and safety issues during long trips due to unpredictable bad weather conditions, which can lead to safety concerns, sensor reliability issues, damage, and delays in freight transport.
Innovation Solution
A method for an autonomous vehicle to identify potential route segments, receive spatiotemporal weather information, and evaluate a cost function that includes an adverse weather risk factor to select optimal route segments and target speeds, minimizing exposure to adverse weather conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If autonomous vehicles operate during long trips, then productivity increases, but reliability decreases due to unpredictable bad weather conditions
Solution Approach 1:
The system performs preliminary weather forecasting and route planning before the vehicle departs on long trips. By evaluating predicted weather conditions along potential route segments in advance and selecting routes that minimize exposure to adverse weather, the system ensures reliable operation throughout the journey while maintaining high productivity
2Reliability
If vehicles avoid adverse weather conditions, then safety improves, but travel time increases due to route deviations and speed reductions
Solution Approach 1:
The system dynamically adjusts the vehicle's speed and route in real-time based on predicted weather conditions. By continuously evaluating the cost function that balances safety risks against travel time, the system determines optimal speed profiles and route deviations that minimize exposure to adverse weather while maintaining efficient travel times
3Productivity
If vehicles travel faster to maintain schedules, then productivity increases, but exposure to adverse weather increases
Solution Approach 1:
The system changes the speed parameter dynamically based on predicted weather conditions along the route. By evaluating the cost function that incorporates both travel time and weather exposure risks, the system optimizes speed profiles to maintain productivity while minimizing exposure to adverse weather conditions through controlled speed adjustments
Data Source
AI summary
An example method involves identifying one or more potential route segments that collectively connect at least two geographical points, receiving spatiotemporal weather information that predicts future weather conditions along each of the potential segments, and, for each potential segment, evaluating a partial cost function that comprises a summation of a set of segment-weighted cost factors, where at least one segment-weighted cost factor comprises an adverse weather risk factor based on the future weather conditions along the potential segment. The method also involves selecting, based on a minimization of a total cost function, a set of selected segments and corresponding segment target speeds for the vehicle to utilize while traversing between the at least two geographical points so as to avoid adverse weather conditions, the total cost function being the sum of partial cost functions associated with a set of segments that collectively connect the at least two geographical points.