Vehicle Travel Control Using Split State Matrices
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Solution Overview
Problem
In complex traffic scenarios, existing vehicle control methods face high calculation complexity and dimensionality issues due to large matrices representing vehicle states, leading to inefficiencies in determining control policies, especially in multi-vehicle interaction scenarios.
Innovation Solution
The method involves obtaining a target matrix based on vehicle and environmental information, splitting it into sub-matrices using nonparametric estimation, and performing iteration calculations to obtain updated matrix elements, which are then used to determine target travel control information considering surrounding vehicle interactions, thereby reducing calculation complexity and ensuring safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large target matrix is used to represent vehicle states in complex traffic scenarios, then the control policy can capture more comprehensive state information, but the calculation complexity and dimensionality increase significantly
Solution Approach 1:
The patent divides the large target matrix into multiple sub-matrices based on different state dimensions (position, velocity, acceleration). Each sub-matrix handles a specific aspect of vehicle state, reducing the computational burden of processing the entire state space while maintaining comprehensive state representation capability
Solution Approach 2:
The patent transforms the high-dimensional matrix operation problem into a lower-dimensional problem by using nonparametric estimation methods. Instead of directly computing with the full target matrix, the system estimates transition probabilities through sampling and kernel density estimation, effectively reducing dimensionality while preserving state transition information
2Measurement precision
If iteration calculations are performed to update matrix elements for accurate probability estimation, then the control policy accuracy improves, but the computation time increases
Solution Approach 1:
The patent implements iterative updates of matrix elements at discrete time steps rather than continuously. The iteration process updates transition probabilities periodically based on new observations, balancing accuracy improvement with computational efficiency by avoiding constant recalculation
Solution Approach 2:
The patent pre-computes certain matrix elements and transition probabilities that are less likely to change, storing them for reuse. This preliminary computation reduces the need for repeated calculations during real-time control, decreasing computation time while maintaining accuracy for dynamically changing state transitions
3Reliability
If the target matrix includes interactions with surrounding vehicles in multi-vehicle scenarios, then the safety of the target vehicle is improved, but the matrix dimensionality and calculation complexity increase
Solution Approach 1:
The patent segments the interaction model by creating separate sub-matrices for different surrounding vehicles and interaction types (collision risk, lane changing, merging). This segmentation allows the system to process vehicle interactions independently and combine results, reducing the overall matrix dimensionality while capturing multi-vehicle interaction effects for improved safety
Solution Approach 2:
The patent extracts only the critical interaction parameters from the full vehicle state space when modeling multi-vehicle interactions. Instead of including all state variables for each surrounding vehicle, the system extracts relevant features (relative position, relative velocity, interaction type) to build compact interaction sub-matrices that maintain safety analysis capability with reduced dimensionality
Data Source
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AI summary
Vehicle control is provided, : obtaining vehicle information of a target vehicle and environmental information of a reference environment in which the target vehicle is located; obtaining a target matrix based on the vehicle information and the environmental information; splitting the target matrix to obtain a plurality of sub-matrices; and obtaining target travel control information of the target vehicle based on matrix elements in the sub-matrices and travel control information of a surrounding vehicle of the target vehicle.