BYD

Last updated February 1, 2026

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Vision-based spatial coordinate mapping: BYDRecent Research Landscape

Manual parking space identification is prone to high error rates in low-visibility environments, which is mitigated through the deployment of trained neural network controllers. This automation reduces collision risks and improves autonomous docking precision.

What technical problems is BYD addressing in Vision-based spatial coordinate mapping?

Inaccurate vehicle spatial positioning

(63)evidences

Inconsistent alignment between vehicle sensors and environmental coordinates leads to parking failures. Resolving this ensures collision avoidance and precise maneuver execution in confined spaces.

Inaccurate parking boundary localization

(28)evidences

Inaccurate detection of physical space limits leads to vehicle collisions or failed automated maneuvers. Precise boundary recognition ensures safe autonomous navigation in constrained environments.

Inaccurate spatial environment representation

(9)evidences

Inconsistent spatial data capture during vehicle maneuvers leads to unreliable navigation maps. Resolving this ensures safe autonomous docking and path planning in complex structures.