Radar-Based Vehicle Velocity Estimation Through RDB Map Correlation
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Solution Overview
Problem
Existing vehicle velocity estimation methods rely heavily on expensive and inaccurate inertial measurement units (IMUs) and GPS, which have coverage limitations, necessitating a method to estimate velocity using radar data without these dependencies.
Innovation Solution
A method that receives range-Doppler-beam maps from multiple radars, performs spatial registration based on current velocity estimates, and optimizes the correlation score using an optimization algorithm to determine an accurate vehicle velocity, enabling vehicle control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS and IMU are used for vehicle velocity estimation, then velocity data can be obtained, but the system becomes expensive and complex
Solution Approach 1:
The patent extracts the velocity estimation function from traditional GPS/IMU systems and implements it using existing radar data processing capabilities. By taking out the velocity estimation task from dedicated sensors and performing it through radar signal processing, the system eliminates the need for expensive IMUs while maintaining velocity measurement functionality.
Solution Approach 2:
The radar system is made multi-functional by enabling it to perform both its traditional function (detecting objects and ranges) and a new function (estimating vehicle velocity). The same radar hardware and processing infrastructure are used for dual purposes, eliminating the need for separate velocity sensing equipment.
2Reliability
If GPS is used for vehicle velocity estimation, then velocity data can be obtained, but coverage is limited in areas such as tunnels, bridges, and urban canyons
Solution Approach 1:
The patent replaces the GPS satellite-based electromagnetic signal system with a radar-based system that uses reflected radio waves from surrounding objects. This substitution allows velocity estimation to work in environments where GPS signals are blocked, as radar can detect objects and calculate velocity through Doppler effects without requiring line-of-sight to satellites.
3Measurement precision
If IMU is used for vehicle velocity estimation, then velocity data can be obtained, but the system becomes expensive and installation is complex
Solution Approach 1:
The radar system performs self-service by using its own operational data (Doppler shifts and range measurements) to derive velocity information without requiring additional sensors or complex external systems. The velocity estimation is achieved through processing the radar's inherent measurement capabilities, making the system self-sufficient.
4Measurement precision
If radar data is processed through optimization and spatial registration, then velocity estimation accuracy improves, but processing time increases
Solution Approach 1:
The patent applies preliminary spatial registration and optimization techniques to radar data before velocity calculation. By pre-processing the radar data to align and optimize the information from multiple radars, the system prepares the data in advance, making the actual velocity estimation more accurate and potentially faster by reducing computational complexity in the final calculation step.
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 accurate vehicle velocity estimation independent of GPS and IMU data, expanding radar system applications and improving vehicle control systems by optimizing the correlation score through iterative spatial registration of radar data.
Implementation Method 1
Many vehicles utilize radar systems. For example, certain vehicles utilize radar systems to detect other vehicles, pedestrians, or other objects on a road in which the vehicle is travelling.
Implementation Method 2
Radar systems may be used, for example, in implementing automatic braking systems, adaptive cruise control, and avoidance features, among other vehicle features.
Data Source
AI summary
Methods and systems for estimating vehicle velocity based on radar data. The methods and systems include receiving a set of range-Doppler-beam, RDB, maps from radars located on a vehicle and performing an optimization process that adjusts an estimate of vehicle velocity so as to optimize a correlation score. The optimization process includes iteratively: spatially registering the set of RDB maps based on the current estimate of vehicle velocity, determining the correlation score based on the spatially registered set of RDB maps, and outputting an optimized estimate of vehicle velocity from the optimization process when the correlation score has been optimized. The methods and systems control the vehicle based at least in part on the optimized estimate of vehicle velocity.