LiDAR Object Heading Estimation for Autonomous Vehicle Control
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Solution Overview
Problem
Existing vehicle control systems face challenges in accurately determining the heading direction of external objects for effective autonomous driving, particularly in complex environments, which affects the vehicle's ability to set optimal driving routes and control speed accordingly.
Innovation Solution
A vehicle control apparatus utilizing LiDAR to generate point clouds, which are processed by a neural network model to identify virtual boxes with precise heading directions, adjusting these directions based on angle thresholds and region assignments, and controlling vehicle operations accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a neural network model is used to determine the heading direction of external objects from LiDAR point clouds, then the accuracy of object detection is improved, but the computational complexity and processing time increase
Solution Approach 1:
The 360-degree field of view around the vehicle is divided into multiple regions (e.g., front, rear, left, right sectors). The neural network processes point clouds from each region separately to determine heading directions, reducing the overall computational burden while maintaining detection accuracy for each sector.
Solution Approach 2:
The system performs preliminary processing of LiDAR point clouds by organizing points into spatial regions and pre-identifying potential external objects before applying the full neural network analysis. This preliminary organization reduces the complexity of the subsequent heading direction determination.
2Reliability
If the neural network model processes all point clouds to determine precise heading directions, then the reliability of vehicle control is improved, but the processing time increases
Solution Approach 1:
The processing system divides the point cloud data into multiple spatial regions and processes each region in parallel or sequentially with reduced computational resources. This segmentation enables faster processing while maintaining reliable heading direction determination for each region through the neural network model.
Solution Approach 2:
The system applies the neural network model selectively to regions where external objects are most likely to be present or where the confidence of detection is below a threshold. For regions with high confidence or low object probability, simplified processing is used, reducing overall processing time while maintaining reliability for critical detections.
3Measurement precision
If the system divides the field of view into multiple regions and processes each separately, then the measurement precision of heading direction is improved, but the device complexity increases
Solution Approach 1:
The 360-degree field of view is divided into multiple manageable regions (e.g., 4-8 sectors around the vehicle). Each region is processed independently by the neural network model to determine heading directions with higher precision. The regional approach improves measurement precision by focusing computational resources on specific areas while the modular structure manages system complexity.
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 accuracy of object detection and vehicle control, enabling safer and more efficient autonomous driving by accurately tracking and predicting the trajectories of external objects, thereby improving collision avoidance and path planning.
Implementation Method 1
a sensor (e.g., light detection and ranging (LiDAR))
Data Source
AI summary
An apparatus for controlling vehicle driving comprises a sensor to obtain a cluster of points, a memory storing a neural network model, and a processor. The processor is configured to determine a first heading direction of a virtual box, generated from the cluster of points, by inputting the cluster into the neural network model. Based on identifying the first heading direction in one of several regions, the apparatus determines an angle between a reference axis within that region and the first heading direction. The regions are divided based on the virtual box's center. Based on this angle, the apparatus outputs the virtual box assigned with the first heading direction or a second heading direction opposite to the first heading direction. The apparatus outputs a signal associated with the virtual box's heading direction and controls vehicle driving based on this signal.