Lane Traffic Efficiency Calculation for Stable Vehicle Lane Changes
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Solution Overview
Problem
Existing vehicle lane change intentions determined by machine learning algorithms are unstable due to the need for large amounts of historical data, leading to increased safety risks and failing to meet stability and safety requirements.
Innovation Solution
A determination method that calculates lane traffic efficiency by identifying obstacle vehicles, determining vehicle and obstacle block cost parameters based on velocities, and using block weight coefficients to select a lane for safe lane changes through linear calculation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If machine learning algorithm is used to determine vehicle lane change intention, then the determination can be performed, but the stability of lane change intention deteriorates due to requirement of large amount of historical data
Solution Approach 1:
The patent changes the determination parameters from complex machine learning model outputs to direct physical parameter comparisons (velocities, positions, accelerations). By using block cost parameters calculated from these physical parameters, the system achieves stable and interpretable lane change intention determination without relying on unstable machine learning predictions.
2Adaptability or versatility
If machine learning algorithm is used for lane change intention determination, then the system can process complex scenarios, but the safety risk increases due to instability
Solution Approach 1:
The patent replaces the machine learning-based determination system with a physics-based calculation system. By substituting algorithmic prediction with direct calculation of block cost parameters from physical measurements (velocity, position, acceleration), the system achieves both scenario processing capability and improved safety through deterministic and transparent decision-making.
3Measurement precision
If block cost parameter calculation is performed based on velocity differences, then the lane change decision accuracy is improved, but the computational complexity increases
Solution Approach 1:
The patent segments the lane change decision problem into distinct block cost parameters (self-block cost, obstacle block cost, different-lane obstacle block cost). Each parameter is calculated independently using simple velocity and position differences, then combined through weighted summation. This segmentation achieves high decision accuracy while keeping individual calculation components simple and manageable.
Data Source
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AI summary
The present disclosure discloses a determination method and a determination device for vehicle traffic efficiency, and a vehicle, and relates to the technical field of intelligent driving. The method includes: determining obstacle vehicles in lanes corresponding to a traveling vehicle, the obstacle vehicles including a same-lane leading vehicle and a different-lane leading vehicle of the traveling vehicle; determining a vehicle block cost parameter based on a first velocity of the traveling vehicle and a second velocity of the same-lane leading vehicle, and obtaining an obstacle block cost parameter of the different-lane leading vehicle; and determining lane traffic efficiency based on the vehicle block cost parameter, the obstacle block cost parameter, and a block weight coefficient, so as to select, based on the lane traffic efficiency, a lane for vehicle lane changing, the lane traffic efficiency representing an expected smooth traffic condition when the traveling vehicle enters a lane. According to the present disclosure, stability and safety requirements of determination of a vehicle lane change intention can be met, and accuracy of lane change planning is significantly improved.