Moving object detection system
US-9030349-B2 · May 12, 2015 · US
US9255988B2 · US · B2
| Field | Value |
|---|---|
| Publication number | US-9255988-B2 |
| Application number | US-201414156681-A |
| Country | US |
| Kind code | B2 |
| Filing date | Jan 16, 2014 |
| Priority date | Jan 16, 2014 |
| Publication date | Feb 9, 2016 |
| Grant date | Feb 9, 2016 |
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A method of detecting and tracking objects using multiple radar sensors. Objects relative to a host vehicle are detected from radar data generated by a sensing device. The radar data includes Doppler measurement data. Clusters are formed, by a processor, as a function of the radar data. Each cluster represents a respective object. Each respective object is classified, by the processor, as stationary or non-stationary based on the Doppler measurement data of each object and a vehicle speed of the host vehicle. Target tracking is applied, by the processor, on an object using Doppler measurement data over time in response to the object classified as a non-stationary object; otherwise, updating an occupancy grid in response to classifying the object as a stationary object.
Opening claim text (preview).
What is claimed is: 1. A method of detecting and tracking objects using multiple radar sensors comprising: detecting objects relative to a host vehicle from radar data generated by a sensing device, the radar data including Doppler measurement data; forming clusters, by a processor, as a function of the radar data, each cluster representing a respective object; classifying, by the processor, each respective object as stationary or non-stationary based on the Doppler measurement data of each object and a vehicle speed of the host vehicle; and applying target tracking, by the processor, on an object using Doppler measurement data over time in response to the object classified as a non-stationary object, otherwise, updating an occupancy grid in response to classifying the object as a stationary object. 2. The method of claim 1 wherein forming clusters comprises the following steps: identifying each point detected by the first sensing device, each point including a location position and an associated range relative to the host vehicle; assigning each point as a separate cluster; comparing neighboring points and merging neighboring points into a same cluster if similarity metrics between the neighboring points is within a similarity threshold. 3. The method of claim 2 further comprising the step of indexing each point using a k-d tree. 4. The method of claim 3 further comprising the step of indexing each point using a hash look-up tree. 5. The method of claim 3 wherein the similarity metrics includes Doppler measurement data. 6. The method of claim 1 wherein classifying each respective object as stationary or non-stationary comprises the following steps: identifying a velocity for each point within a cluster; identifying a unit vector for each point with the cluster; determining a range rate residue value for each point within the cluster; determining that the cluster is a stationary cluster if a predetermined percent of the range rate residue values are within a residue threshold; otherwise determining that the cluster is a dynamic cluster. 7. The method of claim 6 wherein the velocity of each point within the cluster is determined by the following equation: ν xi =y i ω H −ν H ν yi =−x i ω H where ν xi is a lateral velocity of an i th point, ν yi is a longitudinal velocity of the i th point, y i is a longitudinal coordinate relative to the vehicle of the i th point, x i is a latitudinal coordinate relative to the vehicle of the i th point, ω H is yaw rate, and, ν H is a speed. 8. The method of claim 7 wherein the range rate residue value is determined by the following equation: ε i =|{right arrow over (ν)} i ·{right arrow over (n)} i −d i | where ν i is the determined speed, and n i is a unit vector. 9. The method of claim 6 wherein the predetermined percentage is substantially 80%. 10. The method of claim 1 wherein applying tracking comprises the following steps: determining an orientation and position of the cluster at a previous instance of time using radar data; determining an orientation and position of the cluster at a current instance of time using radar data; determining a translation velocity in response to the orientation and position of the cluster at the previous and next instance of time; updating an object tracking model of the cluster at the current instance of time utilizing the translation velocity. 11. The method of claim 10 wherein the transformation velocity is determined by the following equation: v = arg min v ∑ kj a kj ( s k - T v ( m j ) 2 σ 1 + d k - v j · n j 2 σ 2 ) where s k is a radar point, m j is a model point, T ν(n) is an operator applying rigid motion during ν during Δt for a point x, α kj is the probability that radar point s k is associated with model point m j (i.e., the measurement of the model point m j ), d k is the Doppler measurement of the radar point s k , n j is the unit direction from the radar center to the model point m j , and ν j is the related velocity for the model point m j . 12. The method of claim 11 wherein the related velocity for the model is determined by the following formula: ν j =ν t +( m j −o )×ω where (ν t , ω) are translation and angular velocities in ν. 13. The method of claim 12 wherein the object tracking model for the current instance of time is updated as a function of the transformation velocity determined for a current instance of time. 14. The
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