Vehicle Radar Camera Fusion for 2D Motion State
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Solution Overview
Problem
Current vehicle Doppler radar systems can only detect radial velocity components and lack the capability to determine two-dimensional velocity vectors or the full motion state of objects, including yaw rate, which is essential for accurate object tracking and collision prevention.
Innovation Solution
A vehicle environment detection system combining a radar system and a stereo camera, where the processing unit calculates a two-dimensional motion state by integrating Doppler velocity components from the radar and velocity y-components from the camera, using a linear equation system to determine the motion state of objects with three degrees of freedom in a plane.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a Doppler radar system is used to detect objects, then radial velocity component detection is achieved, but two-dimensional velocity vector and full motion state determination is not possible
Solution Approach 1:
The patent combines radar detections (providing radial velocity components and azimuth angles) with stereo camera detections (providing depth and lateral velocity information) into a unified detection system. By merging data from these two different sensing modalities, the system recovers the complete two-dimensional velocity vector and full motion state that neither sensor could provide alone, thus resolving the information loss problem while maintaining measurement precision.
Solution Approach 2:
The patent transitions from one-dimensional radial velocity measurement (radar only) to two-dimensional velocity vector determination by incorporating stereo camera data that provides depth and lateral motion information. This dimensional enhancement allows the system to calculate complete motion states including yaw rate, transforming the limited radial velocity data into comprehensive two-dimensional motion information.
2Device complexity
If radar system alone is used, then device complexity is reduced, but motion state determination capability is insufficient
Solution Approach 1:
The patent creates a multi-functional detection system where the radar provides radial velocity and azimuth information while the stereo camera contributes depth and lateral velocity data. The processing unit integrates these diverse data sources to perform multiple functions: determining two-dimensional velocity vectors, calculating yaw rates, and recovering full motion states. This universal approach allows a single system to handle various motion detection requirements that would otherwise require separate specialized systems.
3Measurement precision
If stereo camera and radar are combined, then two-dimensional motion state calculation is enabled, but data integration complexity increases
Solution Approach 1:
The patent segments the data processing into distinct functional modules: the radar system processes radial velocity and azimuth angle data separately, the stereo camera system processes depth and lateral velocity data separately, and the processing unit integrates these segmented results through a unified mathematical model. This segmentation approach manages data integration complexity by organizing the processing pipeline into manageable, modular stages while maintaining high measurement precision.
Solution Approach 2:
The processing unit acts as an intermediary that receives processed data from both the radar system and stereo camera system, then integrates these data sources through a unified mathematical framework. This intermediary role simplifies the overall system architecture by providing a centralized integration point that handles the complexity of combining heterogeneous sensor data, allowing the radar and camera subsystems to operate independently with well-defined interfaces.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables direct calculation of a complete two-dimensional motion state of extended objects in a single measurement cycle without model assumptions, improving accuracy in object tracking and collision prevention by providing both velocity and direction information.
Implementation Method 1
a detected Doppler velocity component that is constituted by detected Doppler velocity with respect to the radar system
Implementation Method 2
generate so-called chirp signals that are transmitted, reflected and received by means of appropriate antennas comprised in the radar system
Data Source
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AI summary
The present disclosure relates to a vehicle environment detection system (2) arranged to detect at least one detection (6, 6'; 12, 13; 12', 13') and comprises at least one radar system (3), at least one camera device (4) and at least one processing unit (5). For each radar detection (6; 12, 13) at least one azimuth detection angle (ϕd, θ1, θ2) with respect to an x-axis (7, 14) and a detected Doppler velocity component (vD; vD1, vD2) that is constituted by detected Doppler velocity with respect to the radar system (3) are obtained. For each detection, said processing unit (5) is arranged to: - Obtain corresponding camera detections (6'; 12', 13') for at least two image frames, constituting an optical flow. - Determine a velocity y-component (vy; vy1', vy2') from said optical flow, where the velocity y-component (vy; vy1', vy2') is constituted by a projection of a resulting velocity (vr; vr1, vr2; vr1', vr2) onto a y-axis (9, 15) that extends perpendicular to the x-axis (7, 14). - Determine the resulting velocity (vr; vr1, vr2; vr1', vr2') from the detected Doppler velocity component (vD; vD1, vD2) and the velocity y-component (vy; vy1, vy2; vr1', vr2').