Lane Change Prediction Using Vehicle Overlap Degree
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Solution Overview
Problem
Existing vehicle behavior prediction systems fail to accurately estimate lane change destination positions of adjacent vehicles due to complexity and decreased prediction accuracy when using indirect parameters related to overlap degrees between vehicles, without directly incorporating the overlap degree in the prediction model.
Innovation Solution
A vehicle behavior prediction apparatus and method that acquires periphery and vehicle states, calculates an overlap degree between adjacent vehicles, and uses this overlap degree, along with position and speed information, to estimate lane change destination positions using a prediction model, thereby simplifying the model and improving prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If indirect parameters related to overlap degree are used for prediction, then prediction accuracy may be improved, but the number of input parameters increases, the prediction model becomes complex, and the processing load increases
Solution Approach 1:
The patent extracts the essential feature of overlap degree as a single direct parameter from multiple indirect parameters (such as relative position, vehicle length, and width information). By directly calculating and inputting the overlap degree between the ego vehicle and adjacent vehicle, the system achieves accurate prediction without requiring numerous indirect parameters, thus simplifying the model while maintaining prediction accuracy.
2Measurement precision
If multiple input parameters are used to express overlap degree indirectly, then prediction accuracy decreases, but the processing load using the prediction model increases
Solution Approach 1:
The patent merges multiple parameters (relative position, vehicle length, width) into a single integrated parameter representing overlap degree. This consolidation reduces the number of inputs required by the prediction model from multiple separate parameters to a single comprehensive overlap degree parameter, thereby improving processing speed while maintaining or enhancing prediction accuracy.
3Device complexity
If the prediction model uses representative position and vehicle length without overlap degree, then the number of parameters is reduced, but prediction accuracy decreases because overlap degree cannot be sufficiently expressed
Solution Approach 1:
The patent transforms the representation of spatial relationship from using separate parameters (representative position and vehicle length) to using a derived parameter (overlap degree). This parameter transformation maintains model simplicity by using a single calculated value that directly captures the essential interaction between vehicles, while significantly improving prediction accuracy by explicitly representing the overlap relationship that influences lane change behavior.
Data Source
AI summary
To provide a vehicle behavior prediction apparatus and a vehicle behavior prediction method which can estimate a lane change destination position of the adjacent vehicle to an object lane considering an overlap degree between an object vehicle and an adjacent vehicle. A vehicle behavior prediction apparatus calculates an overlap degree between a position range of a prediction object vehicle and a position range of an object vehicle in a longitudinal direction, based on position information and shape information on the prediction object vehicle which is set from adjacent vehicles, and shape information on the object vehicle; and estimates a lane change destination position of the prediction object vehicle to the object lane, using a prediction model into which the position information and the speed information on the prediction object vehicle and the overlap degree is inputted.