Dynamic Object Tracking Algorithm Selection for Vehicles

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Solution Overview

Problem

Existing vehicle object tracking systems face challenges in efficiently selecting the appropriate object tracking algorithm based on the vehicle's environment, leading to suboptimal performance in urban and non-urban settings, where computational resources and object complexity vary.

Innovation Solution

A system and method that determine the vehicle's setting (urban or non-urban) to select between lower and higher computationally demanding object tracking algorithms, adjusting based on available resources and object complexity, prioritizing robustness in urban areas with more fragile objects.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If a higher computationally demanding object tracking algorithm is used, then object tracking accuracy and robustness are improved, but computational resource consumption increases

Engineering Contradiction:

Improveobject tracking accuracyVSAvoidcomputational resource consumption

Core Design Contradiction:

ReliabilityVSUse of energy by moving object

Solution Approach 1:

The system dynamically selects between different object tracking algorithms based on real-time environmental conditions (urban vs. non-urban settings) and available computational resources. This dynamic adaptation allows the system to optimize the balance between tracking accuracy and computational resource consumption, using more robust algorithms when resources are abundant and simpler algorithms when resources are constrained.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system changes the computational parameters by selecting different algorithms with varying computational demands. In urban settings with higher object complexity, the system may select more computationally demanding algorithms that provide better tracking accuracy, while in non-urban settings or when resources are limited, it selects less computationally demanding algorithms to conserve resources.

Inventive Principle:

Principle #35Parameter changes

2Reliability

If a higher computationally demanding object tracking algorithm is used, then tracking robustness in complex environments is improved, but system productivity decreases

Engineering Contradiction:

Improvetracking robustnessVSAvoidsystem processing efficiency

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The system implements dynamic algorithm selection that adjusts tracking robustness based on environmental context. In urban settings where tracking robustness is critical due to high object density and complexity, the system selects more robust algorithms. In non-urban settings with fewer and simpler objects, the system selects less computationally intensive algorithms to maintain processing efficiency.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system applies different algorithmic qualities locally based on the environmental setting. Urban environments receive the benefit of more robust, computationally demanding algorithms tailored to their complexity, while non-urban environments use simpler algorithms appropriate to their lower complexity, optimizing overall system productivity.

Inventive Principle:

Principle #3Local quality

3Productivity

If computational resources are limited, then resource utilization efficiency is improved, but object tracking accuracy deteriorates

Engineering Contradiction:

Improveresource utilization efficiencyVSAvoidobject tracking accuracy

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system dynamically adapts its algorithm selection based on available computational resources. When resources are limited, the system selects less computationally demanding algorithms that maintain acceptable tracking accuracy while preserving resource utilization efficiency. When resources are abundant, the system can afford to use more accurate but computationally intensive algorithms.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system applies partial action by selecting algorithms with computational demand matched to available resources. Rather than always using the most accurate algorithm, the system uses just enough computational power to achieve acceptable tracking performance given the resource constraints, avoiding excessive computational expenditure.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS11508159B2Object tracking algorithm selection system and method

Publication Date: 2022.11.22 DENSO INTERNATIONAL AMERICA INC

AI summary

A system for utilizing object tracking algorithms for tracking objects external to a vehicle includes a processor and a memory in communication with the processor. The object tracking algorithms include a lower computationally demanding object tracking algorithm and a higher computationally demanding object tracking algorithm. The memory has one or more modules that have instructions that cause the processor to determine if the vehicle is located in an urban setting or a non-urban setting and utilize the lower computationally demanding object tracking algorithm by an object tracking system when the vehicle is located in the non-urban setting. If it is determined that the vehicle is in an urban setting, the instructions cause the processor to determine available computational resources of the object tracking system and utilizing either the lower computationally demanding object tracking algorithm or the higher computationally demanding object tracking algorithm based on the available computational resources.