Radar Landmark Mapping for Cost-Effective Vehicle Localization
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Solution Overview
Problem
Existing vehicle localization systems for autonomous vehicles often require expensive and high-quality sensors, such as optical cameras and high-end GNSS systems, which are ineffective in less-than-ideal lighting and weather conditions, and are unaffordable for most consumer vehicles.
Innovation Solution
The use of radar detection-based methods and systems that generate and update a radar reference map, allowing for accurate vehicle localization using inexpensive radar sensors and lower-quality navigation systems by processing radar detections and navigation data to determine the vehicle's pose relative to its environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If expensive high-quality sensors (cameras, LiDAR, high-quality GNSS) are used for vehicle localization, then localization accuracy is improved, but system cost increases
Solution Approach 1:
The patent replaces expensive, fragile sensors (cameras, LiDAR, high-quality GNSS) with inexpensive radar sensors and lower-quality navigation systems. Radar sensors are more durable, work in adverse conditions, and are affordable for consumer vehicles, while still achieving sub-meter localization accuracy through sophisticated signal processing and map-matching algorithms
Solution Approach 2:
The patent changes the operational parameters of the localization system by using radar frequency waves instead of optical frequencies (cameras/LiDAR). This parameter change allows the system to operate effectively in less-than-ideal lighting and weather conditions while maintaining acceptable localization accuracy at lower cost
2Measurement precision
If optical cameras and LiDAR are used for vehicle localization, then localization accuracy is improved, but performance deteriorates in less-than-ideal lighting and weather conditions
Solution Approach 1:
The patent substitutes optical-based sensing systems (cameras and LiDAR that rely on light) with radar-based sensing that uses electromagnetic waves in the radio frequency range. This substitution eliminates the system's vulnerability to lighting conditions and allows operation in adverse weather such as fog, rain, and snow where optical systems fail
Solution Approach 2:
The patent changes the physical parameter of the sensing mechanism from optical frequency to radio frequency. This parameter change fundamentally alters the interaction with environmental conditions, making the system immune to lighting variations and capable of penetrating adverse weather conditions that block or scatter light
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 enables accurate vehicle localization at a sub-meter level, overcoming the limitations of expensive sensors and adverse environmental conditions, while being cost-effective and reliable for consumer vehicles.
Implementation Method 1
receiving, by at least one processor, radar detections from one or more radar sensors of the vehicle
Data Source
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AI summary
This document describes methods and systems for vehicle localization based on radar detections. Radar localization starts with building a radar reference map. The radar reference map may be generated and updated using different techniques as described herein. Once a radar reference map is available, real-time localization may be achieved with inexpensive radar sensors and navigation systems. Using the techniques described in this document, the data from the radar sensors and the navigation systems may be processed to identify stationary localization objects, or landmarks, in the vicinity of the vehicle. Comparing the landmark data originating from the onboard sensors and systems of the vehicle with landmark data detailed in the radar reference map may generate an accurate pose of the vehicle in its environment. By using inexpensive radar systems and lower quality navigation systems, a highly accurate vehicle pose may be obtained in a cost-effective manner.