Wide Aperture Radar Ego-Velocity Estimation Using Static Object Clusters
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Solution Overview
Problem
Current automated driving systems face challenges in accurately estimating host vehicle speed and trajectory, especially in complex and dynamic driving scenarios, due to limitations in object detection and classification, which can lead to reduced reliability and increased collision risks.
Innovation Solution
The implementation of a wide aperture radar system with adaptive logic that differentiates between static and moving objects, using multiple input, multiple output (MIMO) radar technology to estimate velocity vectors and classify objects, allowing for improved host vehicle velocity estimation and target object tracking without the need for additional dedicated sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional object detection and classification methods are used in automated driving systems, then the system structure remains simple, but the accuracy of host vehicle speed and trajectory estimation deteriorates, leading to reduced reliability and increased collision risks
Solution Approach 1:
The patent segments the velocity estimation process by separating static object detection from moving object detection. The system identifies static objects (roadside infrastructure, buildings) and moving objects (other vehicles, pedestrians) independently, then uses only static objects for host vehicle velocity calculation. This segmentation eliminates contamination from moving target detection errors, thereby improving velocity estimation accuracy and system reliability.
Solution Approach 2:
Instead of using moving object detection to estimate host vehicle velocity (traditional approach), the patent inverts the approach by using static object detection. The system detects static objects that should have zero relative velocity, and any measured velocity is attributed to host vehicle motion. This inversion fundamentally improves accuracy by eliminating the source of error (moving object classification uncertainty).
2Measurement precision
If wide aperture radar with MIMO technology is implemented to improve velocity estimation accuracy, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent makes the existing radar system perform multiple functions: it detects both static and moving objects, estimates velocity vectors, and classifies targets. By programming the radar to operate in MIMO mode and implementing the static/moving object differentiation algorithm, the same hardware achieves superior velocity estimation without requiring additional dedicated sensors, thus managing device complexity while improving precision.
Solution Approach 2:
The radar system serves itself by using its own transmitted signals and received echoes to simultaneously achieve object detection, velocity estimation, and static/moving classification. The system processes its own data to derive host vehicle velocity information, eliminating the need for external dedicated measurement devices and reducing overall system complexity despite the enhanced processing requirements.
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 solution enhances autonomous driving capabilities by providing accurate ego-motion estimation, improved object detection, and tracking, thereby enhancing passenger safety and comfort while minimizing collision risks and ensuring consistent automated driving performance across various road topographies.
Implementation Method 1
radio detection and ranging (RADAR) systems detect the presence, distance, and/or speed of a target object by discharging pulses of high-frequency electromagnetic waves that are reflected off the object back to a suitable radio receiver
Implementation Method 2
the range to an object is determined by measuring the time delay between transmission of a pulse and detection of the reflected signal
Data Source
AI summary
Presented are target object detection systems for deriving host vehicle velocities, methods for making/using such systems, and motor vehicles with host vehicle velocity estimation capabilities. A method of automating operation of vehicles includes an electronic transmitter of a vehicle's target object detection system emitting electromagnetic signals, and an electronic receiver receiving multiple reflection echoes caused by each electromagnetic signal reflecting off target objects within proximity of the vehicle. A vehicle controller determines a relative velocity vector for each target object based on these reflection echoes. The relative velocity vectors are assigned to discrete vector clusters. The controller estimates a host vehicle velocity vector as an average of the relative velocity vectors in the vector cluster containing the most relative velocity vectors and having the largest spatial spread. The controller commands one or more vehicle systems to execute one or more control operations responsive to the host vehicle velocity vector.