Perception-Localization Collaborative Adaptive Heuristic Optimization for Vehicle Systems Article Swipe
Remote vehicle perception and localization are core technologies for autonomous driving and intelligent connected vehicles. Their goal is to achieve precise estimation of the vehicle's own pose and high-precision perception of the surrounding environment in dynamic and complex environments. However, existing localization and perception optimization methods are prone to getting trapped in local optima in dynamic scenarios and struggle to fully utilize the uncertainties of multi-sensor information. This paper proposes a Perception-Localization Collaborative Adaptive Heuristic Optimization (PLC-AHO) algorithm. This algorithm achieves global pose optimization through three mechanisms: first, a perception-driven adaptive perturbation mechanism, which adaptively adjusts the direction and magnitude of the search perturbation based on multi-sensor observation errors and uncertainties; second, a local-global pose collaboration mechanism, which combines local gradient guidance, perception perturbation, and global optimum attraction to achieve a balance between local convergence and global exploration; and third, a weight adaptive mechanism, which dynamically adjusts the contribution of each sensor in the optimization process based on sensor accuracy and the environment. This paper describes the algorithm formulas and iterative mechanisms in detail using pure text mathematical form, providing theoretical support for future research on vehicle localization and perception optimization.
Related Topics
Concepts
Computer science
Heuristic
Local optimum
Convergence (economics)
Perturbation (astronomy)
Mathematical optimization
Global optimization
Perception
Iterative and incremental development
Optimization problem
Process (computing)
Iterative method
Artificial intelligence
Adaptive algorithm
Intelligent transportation system
Perspective (graphical)
Optimization algorithm
Vehicle dynamics
Adaptive optimization
Trust region
Adaptive system
Control theory (sociology)
Engineering
Key (lock)
Metadata
- Type
- preprint
- Landing Page
- https://doi.org/10.5281/zenodo.17853179
- OA Status
- green
- OpenAlex ID
- https://openalex.org/W7111407559
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W7111407559Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.5281/zenodo.17853179Digital Object Identifier
- Title
-
Perception-Localization Collaborative Adaptive Heuristic Optimization for Vehicle SystemsWork title
- Type
-
preprintOpenAlex work type
- Publication year
-
2025Year of publication
- Publication date
-
2025-12-08Full publication date if available
- Authors
-
Zhang Jin-chengList of authors in order
- Landing page
-
https://doi.org/10.5281/zenodo.17853179Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.5281/zenodo.17853179Direct OA link when available
- Concepts
-
Computer science, Heuristic, Local optimum, Convergence (economics), Perturbation (astronomy), Mathematical optimization, Global optimization, Perception, Iterative and incremental development, Optimization problem, Process (computing), Iterative method, Artificial intelligence, Adaptive algorithm, Intelligent transportation system, Perspective (graphical), Optimization algorithm, Vehicle dynamics, Adaptive optimization, Trust region, Adaptive system, Control theory (sociology), Engineering, Key (lock)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
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