Autonomous Vehicle Object Detection Using Point Cloud Property Estimation
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Solution Overview
Problem
Existing methods for detecting and tracking objects in autonomous vehicles lack sufficient speed, precision, and accuracy, necessitating a more effective approach to ensure safe operation.
Innovation Solution
A computer-implemented method using a machine-learned detector model that processes sensor data from LIDAR systems to classify, cluster, and estimate properties of objects, incorporating techniques like LIDAR Background Subtraction and multi-channel data matrices for improved object detection and tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing object detection and tracking methods are used, then the system can operate with current technology, but the speed, precision, and accuracy of detection are insufficient
Solution Approach 1:
The patent segments the object detection process into distinct stages: sensor data acquisition, point cloud generation, object hypothesis generation, and property estimation. Each stage processes specific data types independently, allowing parallel computation and improving overall detection speed while maintaining precision through specialized processing at each stage.
Solution Approach 2:
The patent transitions from traditional 2D image-based detection to 3D point cloud processing, adding spatial dimensionality to the detection process. This dimensional change enables more accurate depth perception and object localization, improving both precision and speed by leveraging the third dimension for faster geometric computations.
2Measurement precision
If existing object detection methods are used, then the system can function with current capabilities, but the accuracy of object instance property estimations is insufficient
Solution Approach 1:
The patent introduces an intermediary point cloud representation that bridges sensor data and object property estimation. This intermediate data structure contains enriched geometric and semantic information that facilitates more accurate property estimation without requiring direct complex processing of raw sensor data, thereby improving accuracy while managing system complexity.
Solution Approach 2:
The patent performs preliminary processing of sensor data into structured point clouds with pre-computed geometric properties before the main detection and estimation stages. This preliminary action prepares the data in advance, reducing the computational complexity required during real-time detection while improving the accuracy of subsequent property estimations.
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
Enhances the speed and accuracy of object detection and tracking, enabling safer and more efficient autonomous vehicle operations by providing robust object instance property estimations for perception, prediction, and motion planning systems.
Implementation Method 1
a sensor system including at least one LIDAR sensor configured to transmit ranging signals relative to the autonomous vehicle and to generate LIDAR data
Data Source
AI summary
Systems, methods, tangible non-transitory computer-readable media, and devices for detecting objects are provided. For example, the disclosed technology can obtain a representation of sensor data associated with an environment surrounding a vehicle. Further, the sensor data can include sensor data points. A point classification and point property estimation can be determined for each of the sensor data points and a portion of the sensor data points can be clustered into an object instance based on the point classification and point property estimation for each of the sensor data points. A collection of point classifications and point property estimations can be determined for the portion of the sensor data points clustered into the object instance. Furthermore, object instance property estimations for the object instance can be determined based on the collection of point classifications and point property estimations for the portion of the sensor data points clustered into the object instance.