Lidar Mirrored Object Detection via Sensor Clustering
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Solution Overview
Problem
Lidar systems in autonomous vehicles face errors due to noise and interference, leading to false-positive object detections, which can compromise safety and efficiency in navigating driving environments.
Innovation Solution
A mirrored object detector uses clustering techniques to modify lidar data by combining points from multiple sensors, determining a region of overlap, and comparing range differences to a threshold, thereby identifying and mitigating erroneous lidar returns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If lidar systems are used to detect objects in driving environments, then object detection capability is improved, but false-positive detections occur due to noise and interference
Solution Approach 1:
The patent combines lidar data from multiple sensors to create a comprehensive data set. By merging data from multiple sources, the system can cross-validate detections and eliminate false positives that would appear in only one sensor's data, thereby improving detection reliability while reducing harmful false alarms
Solution Approach 2:
The patent introduces clustering algorithms as an intermediary processing step between raw lidar data and object detection. This intermediary layer groups lidar returns into clusters and applies logical analysis to distinguish true objects from noise, acting as a mediator that filters out false positives while preserving genuine detections
2Measurement precision
If multiple lidar sensors are combined to improve detection accuracy, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the processing of combined lidar data by dividing it into discrete clusters based on spatial proximity and characteristics. This segmentation approach allows the system to handle complex multi-sensor data in manageable units, reducing computational complexity while maintaining high measurement precision through cluster-level analysis
Solution Approach 2:
The patent changes processing parameters by applying clustering thresholds and range difference criteria to the combined sensor data. By transforming the raw data through these parameter-based filtering operations, the system achieves high measurement precision without requiring complex hardware integration, as the complexity is managed through software-based parameter manipulation
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 lidar data, reducing false object detections and enhancing the safety and efficiency of autonomous vehicle operations by filtering out mirrored or erroneous lidar points, leading to more precise object classification and trajectory planning.
Implementation Method 1
Sensors, such as lidar sensors, generally measure the distance from a lidar device to the surface of an object by transmitting a light pulse and receiving a reflection of the light pulse from the surface of the object
Implementation Method 2
The sensor may generate a signal based on light pulses incident on the sensor
Data Source
AI summary
This disclosure relates to detecting mirrored lidar returns and modifying lidar data using clustering techniques. In some examples, lidar data including a set of lidar points may be captured by a vehicle operating in an environment. The vehicle may combine the lidar data from numerous lidar devices to have a common frame of reference. In some examples, the vehicle may determine a cluster of the lidar points based on elevation data and azimuth data. The vehicle may compare range data of the clusters to determine whether such lidar points are indicative of a lidar return associated with an error (including multipath reflections). Based on determining that the difference in ranges of two lidar points is above a threshold value, the vehicle may determine how to use the data.