Radar Motion Detection for Low-Speed Autonomous Vehicle Velocity
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Solution Overview
Problem
Current vehicle velocity measurement systems, such as GPS and inertial sensors, face challenges in accuracy and reliability, particularly in determining whether a vehicle is stationary, due to issues like high noise, latency, and the inability to accurately detect low velocities.
Innovation Solution
A method using radar data from multiple sensors to detect stationary objects in the environment, calculate the vehicle's linear and angular velocity, and control the vehicle based on these calculations, by determining the likelihood of scatterers being stationary and utilizing coherent change detection and Doppler measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS and inertial sensors are used for velocity measurement, then the system can provide velocity data, but the accuracy and reliability deteriorate due to high noise and inability to detect low velocities
Solution Approach 1:
The patent replaces mechanical velocity sensors (GPS, inertial sensors) with a radar-based optical/electromagnetic sensing system. The radar system measures velocity by detecting Doppler frequency shifts in reflected electromagnetic waves from stationary scatterers, eliminating the noise and latency issues of mechanical sensors and achieving millimeter-per-second velocity detection accuracy.
Solution Approach 2:
The patent introduces stationary scatterers in the environment as intermediaries for velocity measurement. Instead of directly measuring vehicle motion, the system measures the Doppler shift of radar signals reflected from these stationary objects, using them as reference points to indirectly determine vehicle velocity with high precision.
2Measurement precision
If radar data from multiple sensors is processed to detect stationary objects and calculate velocity, then the detection accuracy improves, but the computational complexity increases
Solution Approach 1:
The patent extracts and processes only the necessary features from radar data - specifically Doppler frequency information from stationary scatterers - to calculate vehicle velocity. By focusing on extracting only the relevant velocity-related parameters rather than processing all radar data comprehensively, the system achieves high detection accuracy while managing computational complexity.
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 enhances vehicle motion detection accuracy, sensitivity, and reduces latency, enabling precise velocity calculations down to millimeters per second, improving the reliability of autonomous vehicle operations.
Implementation Method 1
utilizing coherent change detection and Doppler measurements
Implementation Method 2
receiving, from at least one radar sensor mounted on the autonomous vehicle, radar data representative of a physical environment of the autonomous vehicle
Data Source
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AI summary
Examples relating to vehicle motion detection using radar technology are described. An example method may be performed by a computing system and may involve receiving, from at least one radar sensor mourned on an autonomous vehicle, radar data representative of an environment of the vehicle. The method may involve, based on the radar data, detecting at least one scatterer present in the environment and. making a determination of a likelihood, that the at 'least one scatterer is stationary with respect to the vehicle. The method may involve, in- response to the likelihood being at least equal to a predefined confidence threshold, calculating a velocity of the vehicle based on the radar data, where calculating the velocity comprises one of: determining an indication that the vehicle is stationary, and determining an angular and linear velocity of the vehicle. And the method may involve controlling the vehicle teed on the calculated velocity.