Autonomous Vehicle Routing Around Avoidance Areas
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Solution Overview
Problem
Large-scale mapping operations face challenges in efficiently updating avoidance areas on high-definition maps due to the rapid establishment of inconsistencies, 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, prioritizes their remapping based on metrics such as route length and demand, and generates alternative routes to circumvent identified avoidance areas, thereby improving route efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If avoidance areas are established rapidly to maintain map integrity, then map reliability is improved, but routing efficiency deteriorates due to excessive avoidance areas
Solution Approach 1:
The system continuously monitors routing metrics such as route length, number of avoidance areas, and travel time. When metrics exceed thresholds, the system automatically triggers remapping operations to remove unnecessary avoidance areas, creating a feedback loop that balances map reliability with routing efficiency
Solution Approach 2:
The system dynamically adjusts the status of avoidance areas based on accumulated routing data. Avoidance areas are transitioned from active to inactive status when routing metrics indicate they are no longer necessary, allowing the system to adapt map parameters to current operational conditions
2Measurement precision
If remapping operations are performed frequently to update avoidance areas, then map accuracy is improved, but computational resources are overwhelmed
Solution Approach 1:
Instead of performing comprehensive remapping operations frequently, the system applies partial remapping only to specific avoidance areas that have been identified as problematic through routing metric analysis. This selective approach maintains map accuracy while significantly reducing computational load
Solution Approach 2:
The system performs preliminary analysis of routing metrics and identifies candidate avoidance areas for removal before executing remapping operations. This preliminary filtering step ensures that remapping resources are focused only on areas that will provide the greatest benefit
3Productivity
If statistical approaches are used to prioritize remapping order, then routing optimization is improved, but computational complexity increases
Solution Approach 1:
The system transforms the complex statistical prioritization problem into a simpler parameter-based ranking system. Avoidance areas are scored using key routing metrics such as route length impact and frequency of use, allowing for efficient prioritization without the computational burden of full statistical analysis
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.