Lane Change Success Prediction Using Lane-Group Position Offsets
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Solution Overview
Problem
The accuracy of predicting the success rate of lane changes in vehicle navigation is low due to various factors, leading to potential safety hazards in traffic.
Innovation Solution
A method and apparatus that utilize lane-group data to determine feature sets, remove specific feature subsets, and calculate the success rate of lane changes based on current and target position offsets, traveling information, and lane-line types to enhance prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple factors are considered in lane changing prediction, then prediction accuracy is improved, but system complexity increases
Solution Approach 1:
The patent segments the lane changing prediction process into multiple independent modules: obtaining current lane information, predicting target lane information, determining position offsets, and calculating success rates. Each module handles a specific aspect of the prediction, allowing the system to consider multiple factors while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The patent introduces a new dimension of prediction by calculating position offsets in both longitudinal and lateral directions, and by predicting not just whether lane changing is possible but also the success rate. This multi-dimensional approach improves prediction accuracy by considering spatial relationships and temporal factors simultaneously.
2Productivity
If feature subsets are removed based on preset conditions, then processing efficiency is improved, but information loss occurs
Solution Approach 1:
The patent changes the parameters of feature subsets by removing only those that meet specific preset conditions (such as features with zero values or redundant information). This selective parameter modification maintains processing efficiency by reducing data volume while preserving critical information needed for accurate lane changing prediction.
Solution Approach 2:
The patent extracts and removes specific feature subsets that meet preset conditions from the complete feature set. By selectively taking out only the unnecessary features (such as redundant or zero-value features), the system improves processing efficiency without losing essential information required for prediction.
Data Source
AI summary
A method for predicting a success rate of lane changing by a vehicle, including obtaining lane-group data of the plurality of lane groups, determining feature sets respectively corresponding to a plurality of lane lines of the traveling road, removing a feature subset in the plurality of feature subsets that meets a first preset condition from a feature set corresponding to the feature subset to obtain a remaining feature set, determining a current position offset of a traveling position of the vehicle in the plurality of lane groups during traveling of the vehicle, determining a target position offset of a preset target position of the vehicle in the plurality of lane groups, and predicting a success rate of changing from the first lane to the second lane by the vehicle based on the current position offset, the target position offset, the remaining feature set, and traveling information of the vehicle.