Vehicle Ego-Motion Estimation Using Multi-Radar Velocity Vectors
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Solution Overview
Problem
Current radar systems on vehicles face challenges in accurately estimating ego-motion, particularly in lateral motion and rotation rate, which is crucial for precise vehicle control and environmental response.
Innovation Solution
The system employs multiple onboard radar transceivers and a common processor to preprocess data using velocity vector processing techniques, estimating velocity vectors at predefined points in the field-of-view, and applying these to improve ego-motion estimation by suppressing noise from moving targets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If single radar systems are used for ego-motion estimation, then device complexity is reduced, but measurement precision of velocity and angular rate deteriorates
Solution Approach 1:
The patent combines data from multiple radar transceivers (first radar transceiver unit and second radar transceiver unit) into a unified velocity vector processing framework. The processor integrates echo signals from both radars to compute velocity vectors at multiple points in the field-of-view, thereby improving ego-motion estimation accuracy through merged sensor data while managing system complexity through integrated processing.
2Measurement precision
If velocity vector processing at multiple points is implemented, then measurement precision of turn rate improves, but computational complexity increases
Solution Approach 1:
The patent segments the field-of-view into multiple discrete points (grid points) where velocity vectors are independently estimated. By dividing the continuous spatial domain into discrete locations, the system can compute velocity vectors at each point separately and then aggregate them to estimate turn rate, improving precision through multiple measurements while managing computational load through structured segmentation.
Solution Approach 2:
The patent computes velocity vectors at more points than the minimum required for basic ego-motion estimation. By calculating velocity vectors at multiple points across the field-of-view (excessive action), the system obtains redundant measurements that improve turn rate estimation accuracy through statistical aggregation, while the processor manages the computational burden by efficiently combining these partial results.
3Reliability
If data from multiple radars is processed together, then reliability of ego-motion estimation improves, but device complexity increases
Solution Approach 1:
The patent implements a universal processor that handles data from multiple different radar transceiver units through a unified velocity vector processing algorithm. The same processing framework processes echo signals from any radar in the network, computing velocity vectors and estimating ego-motion parameters in a consistent manner, thereby improving reliability through multi-sensor fusion while managing complexity through a single multi-functional processing system.
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
This approach significantly enhances ego-motion estimation accuracy, improving discrimination between stationary and moving targets, tracking, detection of skidding, and bias compensation for other sensors, with a six-fold improvement in yaw rate estimates.
Implementation Method 1
a first radar transceiver unit to be positioned on or within the vehicle, the first radar transceiver unit transmit a first signal and receive a first echo signal in response to the transmitted first signal; a second radar transceiver unit to be positioned on or within the vehicle, the second radar transceiver unit to transmit a second signal and receive a second echo signal
Implementation Method 2
wherein the first signal and the second signal are reflected by an environment of the vehicle
Data Source
AI summary
Estimating the ego-motion of a vehicle, e.g., the self-motion of the vehicle, can be improved by pre-processing data from two or more radars onboard the vehicle using a processor common to the two or more radars. The common processor can pre-process the data using a velocity vector processing technique that can estimate a velocity vector at each point of a predefined number of points, such as arranged in a grid in the field-of-view of radars, with coordinates (X, Y, Z (optional)), where U is the component of the velocity in the X-direction, V is the component of the velocity in the Y-direction, and W is the component of the velocity in the optional Z-direction.