Dynamic Particle Filter for Vehicle Position Estimation
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Solution Overview
Problem
Existing vehicle position estimation technologies face challenges in efficiently estimating the position of other vehicles on the road while managing processing time and load, leading to increased data processing requirements and potential inaccuracies.
Innovation Solution
The proposed apparatus uses a particle filter to estimate the position of other vehicles by dynamically adjusting the number of particles based on the relative relationship between the subject vehicle and other vehicles, actual vehicle speed, and the distribution state of particles, thereby limiting processing time and maintaining high estimation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the position of each other vehicle is mapped to road map data using coordinates, then the position estimation accuracy is improved, but the data processing load and processing time increase
Solution Approach 1:
The patent applies local quality by differentiating the treatment of multiple other vehicles based on their individual characteristics. Each other vehicle is assigned a different number of particles according to its relative position to the subject vehicle, actual speed, and particle distribution state. This selective allocation ensures that vehicles requiring higher precision (e.g., those closer to the subject vehicle or moving at higher speeds) receive more computational resources, while others receive fewer, thereby optimizing the balance between position estimation accuracy and processing efficiency
Solution Approach 2:
The patent implements dynamics by making the number of particles used in position estimation variable rather than fixed. The particle number is dynamically adjusted based on real-time conditions including the relative relationship between the subject vehicle and other vehicles, actual vehicle speed, and the distribution state of particles. This dynamic adaptation allows the system to maintain high estimation accuracy when needed while reducing processing load during less critical situations
2Measurement precision
If the number of particles in the particle filter is increased to improve estimation accuracy, then the position estimation precision is improved, but the processing time increases
Solution Approach 1:
The patent applies parameter changes by dynamically modifying the number of particles (a key parameter in the particle filter algorithm) based on specific conditions. The particle number is adjusted according to the relative relationship between vehicles, actual speed, and particle distribution state. This parameter adaptation enables the system to achieve high position estimation precision when necessary while minimizing processing time during less critical situations, effectively resolving the trade-off between accuracy and speed
Data Source
AI summary
An other vehicle position estimation apparatus for estimating the position of another vehicle on the road using a particle filtering process includes an other vehicle map matcher and a particle number controller. The other vehicle map matcher includes an updater for updating the position of a particle distributed on a map, a likelihood calculator for calculating the likelihood of the particle position, and a position estimator configured to estimate the position of the other vehicle based on the position of the particle. The particle number controller determines a number of particles to distribute based on at least one of (i) a relative positional relationship between a subject vehicle and the other vehicle, (ii) an actual vehicle speed of the other vehicle, (iii) a distribution state of the particles, and (iv) a relationship between the position of the other vehicle and the road.