Vehicle detection and tracking method based on adaptive model

Abstract

The invention relates to a vehicle detection and tracking method based on an adaptive model, and belongs to the field of automatic driving. The method comprises the following steps: 1) preprocessing point cloud data at t-1 moment and t moment; 2) converting the point clouds at the t-1 moment into a laser radar coordinate system at the t moment, and constructing a grid map under a polar coordinate system according to the point clouds of two continuous frames; 3) determining possible dynamic objects at the moment t, and carrying out data association to find an associated object of each dynamic object at the moment t-1; 4) fitting a dynamic object and an associated object thereof based on a convex hull model, and screening dynamic vehicle candidates by using a motion evidence criterion; 5) deducing the real size and pose of the vehicle; 6) finding an associated object of the dynamic vehicle at the t + 1 moment through data association, and then screening the dynamic vehicle by using a motion consistency criterion; 7) judging whether the new vehicle and other vehicles have a vehicle following relationship or not; and 8) tracking the vehicle by using a tag multi-Bernoulli filter.

Description

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AI Technical Summary

Benefits of technology

This technology helps predict how well an autonomous car's sensor accurately tracks its own position based upon measurements from sensors attached inside them. It does this through calculating velocity data for each individual wheel separately without relying solely on their previous positions. By doing these calculations over time, the system becomes more accurate than previously possible due to factors like road conditions and traffic congestion.

Problems solved by technology

Technological Problem addressed by this patented technology describes how current methods have limitations such as being limited in their ability to accurately detect moving targets like cars without having any significant impact from surrounding obstacles (such as approaching sensors). Additionally, existing approaches either use an accurate representation of the target's surface area or require expensive equipment.

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