Autonomous Vehicle Routing Around Avoidance Areas
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Solution Overview
Problem
Large-scale mapping operations face challenges in efficiently remapping avoidance areas on high-definition maps due to the rapid establishment of new avoidance areas, leading to suboptimal routing for autonomous vehicles, which can result in longer routes and increased costs.
Innovation Solution
A computing system that analyzes network effects of avoidance areas on routing by identifying and prioritizing the removal of specific avoidance areas based on their impact on route efficiency, generating alternative routes that circumvent these areas, and evaluating metrics to determine which areas to remove, thereby improving route generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If avoidance areas are established rapidly to maintain map accuracy, then map reliability is improved, but routing efficiency deteriorates due to increased route length and complexity
Solution Approach 1:
The patent segments the set of avoidance areas into different priority levels based on their impact on routing efficiency. By analyzing network effects and identifying high-impact avoidance areas, the system divides the remapping task into prioritized segments, allowing resources to be allocated efficiently to the most critical areas first, thereby improving route efficiency while maintaining map accuracy.
Solution Approach 2:
The patent performs preliminary analysis of avoidance areas to identify which ones have the greatest network effect on routing efficiency before actual remapping occurs. This preliminary prioritization action allows the system to prepare an optimized remapping sequence in advance, reducing the computational burden during real-time routing and improving overall route efficiency.
2Loss of time
If statistical approaches are used to prioritize remapping order, then routing efficiency is improved, but computational complexity increases making the approach impractical
Solution Approach 1:
The patent extracts the essential prioritization logic from complex statistical approaches by identifying and removing only the critical analysis steps needed to determine avoidance area priority. Instead of performing full statistical analysis on all avoidance areas, the system extracts key network effect metrics to create a simplified prioritization model that maintains routing efficiency improvements while reducing computational complexity to practical levels.
Data Source
AI summary
A computing system that analyzes the network effects of avoidance areas on autonomous vehicle routing is described herein. The computing system includes a data store that comprises a set of avoidance areas through which the autonomous vehicle is prohibited from being routed. A routing system generates an initial route from a source location to a target location irrespective of avoidance areas included on the initial route. When a number of avoidance areas on the initial route exceed a predetermined threshold, one or more alternative routes are generated from the source location to the target location that respectively circumvent a corresponding identified avoidance area on the initial route. Metrics are evaluated for the one or more alternative routes and a subset of avoidance areas are outputted to desirably be removed from the set of avoidance areas.