LiDAR Object Tracking with Heading History at Low Speeds
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Solution Overview
Problem
The heading angle calculated based on absolute velocity in LiDAR tracking systems can be inaccurate, particularly at low speeds or during traffic congestion, due to low velocity accuracy.
Innovation Solution
A method that uses current velocity-based heading angles from point clouds, accumulates scores for heading angles, and incorporates these scores into a heading history to correct the heading angle using shape information, ensuring accurate tracking by filtering candidate angles based on reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If the heading angle is calculated based on absolute velocity, then the calculation is simple and fast, but the accuracy deteriorates at low speeds or during traffic congestion
Solution Approach 1:
The system dynamically changes the calculation parameters based on velocity thresholds. When absolute velocity is below a first threshold, it switches from using absolute velocity components to using relative velocity components and shape-based calculations. This parameter change resolves the contradiction by adapting the calculation method to the current motion state, maintaining accuracy at low speeds while preserving computational efficiency through threshold-based decision making.
Solution Approach 2:
The heading angle calculation system transitions from a static method (always using absolute velocity) to a dynamic method that adapts based on velocity conditions. The system dynamically selects between different calculation approaches: absolute velocity-based calculation for high speeds, and relative velocity combined with shape-based calculation for low speeds. This dynamic adaptation resolves the contradiction by optimizing both speed and accuracy according to real-time conditions.
2Measurement precision
If the heading angle is calculated using relative velocity and shape information, then the accuracy is improved at low speeds, but the calculation complexity increases
Solution Approach 1:
The system uses parameter changes to manage complexity by only activating complex calculations when necessary. Shape-based heading angle calculation and relative velocity processing are enabled only when absolute velocity falls below the first threshold. When velocity is high, the system reverts to the simpler absolute velocity-based calculation. This conditional parameter change resolves the contradiction by balancing accuracy needs with computational complexity based on real-time velocity conditions.
Solution Approach 2:
The velocity range is segmented into different zones (high velocity above first threshold, low velocity below first threshold, and intermediate velocity between first and second thresholds). Each segment has a tailored calculation strategy: simple absolute velocity-based calculation for high speeds, complex relative velocity and shape-based calculation for low speeds, and interpolated or transitional calculation for intermediate speeds. This segmentation resolves the contradiction by applying appropriate complexity only where needed.
Data Source
AI summary
An object-tracking method includes using the velocity of a track for tracking an object, generated from a point cloud related to the object, in a current recognition period to obtain a current velocity-based heading angle of the track. The method includes accumulating scores associated with information about the current velocity-based heading angle and incorporating the accumulated scores into a heading history having regions sectioned for respective heading angles of the track. The method includes obtaining a history-based heading angle of the track using the heading history, correcting the history-based heading angle using information about the shape of the track, and determining the result of correction to be a final heading angle in the current recognition period.