Lane-Level AV Routing Using Predicted Lane Performance
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Solution Overview
Problem
Current autonomous vehicle navigation systems lack the ability to effectively select routes based on lane-level performance data, which can lead to inefficient routing and increased risk due to unpredictable traffic patterns and obstacles, as they primarily rely on roadway-level data rather than lane-specific conditions.
Innovation Solution
The system collects and analyzes lane-level performance data from a fleet of autonomous vehicles, using clustering algorithms and machine learning to predict the performance of candidate lanes and select the most suitable route by aggregating data from similar lanes, even when individual lane data is sparse.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If navigation systems use roadway-level data for routing, then the routing system is simpler to implement, but the routing accuracy and safety are reduced due to inability to account for lane-specific conditions
Solution Approach 1:
The patent segments the roadway into individual lanes and evaluates performance metrics at the lane level rather than roadway level. Each lane is assigned a performance score based on autonomous vehicle trip data, allowing the navigation system to select specific lanes for routing decisions. This segmentation enables more precise routing accuracy by accounting for lane-specific conditions such as visibility, traffic patterns, and collision rates.
Solution Approach 2:
The patent introduces an intermediary layer of lane performance evaluation that bridges the gap between simple roadway-level routing and complex lane-level analysis. The system collects trip data from autonomous vehicles, processes it through machine learning models to generate lane performance scores, and uses these scores to inform routing decisions. This intermediary performance evaluation layer enables accurate lane selection without requiring direct complex control of each lane parameter.
2Reliability
If the system collects and analyzes lane-level performance data from fleet vehicles, then the routing safety and performance prediction accuracy improve, but the data processing complexity and computational requirements increase
Solution Approach 1:
The patent merges data from multiple autonomous vehicles in the fleet to establish lane performance metrics. By aggregating trip data across numerous vehicles and lanes, the system creates a comprehensive performance evaluation that improves routing safety through statistically significant patterns. The machine learning models combine multiple data sources including trip outcomes, traffic conditions, and vehicle behaviors to generate reliable lane performance scores.
Solution Approach 2:
The system uses data generated by autonomous vehicles during normal operation to continuously improve lane performance metrics without requiring separate dedicated data collection missions. Each trip by an autonomous vehicle contributes to the performance evaluation of the lanes it traverses, allowing the system to self-upgrade its routing capabilities using operational data already being collected for other purposes.
3Measurement precision
If the system uses machine learning and clustering algorithms to predict lane performance, then the ability to handle sparse data and make accurate predictions improves, but the computational load and processing time increase
Solution Approach 1:
The patent performs preliminary processing of lane performance data by collecting and analyzing trip outcomes from fleet vehicles to establish baseline performance metrics for each lane before routing decisions are needed. The system pre-computes performance scores and stores them for quick retrieval during navigation. This preliminary action allows the system to make rapid routing decisions without performing complex real-time analysis, reducing processing time while maintaining prediction accuracy.
Solution Approach 2:
The system uses partial action by focusing computational resources on evaluating only the candidate lanes that appear in potential routing options, rather than analyzing all lanes in the service area. The clustering algorithms process data for relevant lanes with higher priority, allowing the system to achieve sufficient prediction accuracy without the excessive computational burden of comprehensive analysis of every possible lane.
Data Source
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AI summary
A map database stores data describing a set of connected roadways, each having one or more lanes, A navigation system selects a route for an autonomous vehicle (AV) by identifying a set of candidate lanes for routing the AV in the map database and retrieving at least one parameter of each candidate lane from the map database. The navigation system determines a performance metric for each of the candidate lanes, the performance metric for a given candidate lane based on the retrieved at least one parameter of the given candidate lane. The navigation system selects a candidate lane for routing the AV based at least in part on the determined performance metrics.