Radar Reflection Recognition Using Velocity Comparison
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Solution Overview
Problem
Autonomous vehicles face challenges in accurately navigating through environments due to inaccurate or incorrect sensor data from radar systems, which can result in phantom objects being detected, leading to unnecessary actions like braking or steering.
Innovation Solution
The techniques involve identifying reflected returns in radar sensor data by comparing radar returns using pair-wise comparisons, projecting object velocities onto radial directions, and confirming the presence of objects using additional sensor information like LiDAR or image data to exclude phantom objects from route planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If radar sensor data is used for autonomous vehicle navigation, then the vehicle can detect objects in the environment, but reflections off intermediate objects cause false positives and inaccurate sensor data
Solution Approach 1:
The patent uses velocity information as an intermediary to distinguish between direct radar returns from actual objects and reflected returns from intermediate objects. By comparing the velocity of detected objects with the projected velocity based on radar data, the system can identify and exclude false positives caused by reflections, thereby improving measurement precision and reliability simultaneously
Solution Approach 2:
The patent implements a feedback mechanism where velocity information from multiple sources (radar data, object tracking, and projected velocity calculations) is continuously compared and used to adjust the identification of valid objects. This feedback loop allows the system to refine its object detection accuracy by eliminating false positives detected through reflection analysis
2Loss of information
If all radar returns are processed for object detection, then comprehensive environment sensing is achieved, but processing load increases due to inclusion of reflected returns
Solution Approach 1:
The patent extracts and removes reflected returns from the radar data processing pipeline by identifying them through velocity comparison. By separating valid object returns from reflected returns, the system maintains comprehensive environment sensing while reducing processing load by excluding irrelevant reflected data from further processing and route planning
Solution Approach 2:
The patent applies partial processing by focusing computational resources only on returns that pass the velocity validation test. Instead of processing all radar returns equally, the system performs detailed analysis only on candidate objects with consistent velocity information, thereby maintaining detection completeness while improving processing efficiency
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 improves the accuracy of autonomous vehicle navigation by reducing false positives, enhancing safety, and reducing processing load by excluding irrelevant data, thereby improving the rider experience.
Implementation Method 1
a radar sensor may emit radio energy that reflects (or bounces) off objects in the environment before returning to the sensor
Implementation Method 2
radio energy may reflect off multiple objects in the environment before returning to the radar sensor
Implementation Method 3
LiDAR sensor data, image data, and/or the like
Data Source
AI summary
Techniques are discussed for determining reflected returns in radar sensor data. In some instances, pairs of radar returns may be compared to one another. For example, a reflection point may be determined from a first position of a first radar return and a second position of a second radar return. Additional data, e.g., sensor data and/or map data, may be used to determine the presence of objects in the environment. The first return or the second return may be a reflected return if an object is disposed at the reflection point. In some instances, a vehicle, such as an autonomous vehicle, may be controlled at the exclusion of information from reflected returns.