LiDAR Object Prioritization for Autonomous Driving Displays
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Solution Overview
Problem
Existing LiDAR systems in autonomous vehicles face challenges in accurately identifying and prioritizing objects for display targets, especially when errors occur, which can compromise safety and stability during autonomous driving.
Innovation Solution
An object display apparatus and method that uses a processor to determine object importance based on curvature, lane position, classification, and other factors, assigning priorities and selecting display targets based on these criteria to ensure accurate object representation despite potential errors in LiDAR data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LiDAR data is used to identify objects in autonomous vehicles, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments objects into multiple groups based on their importance levels (first group for high importance, second group for low importance). This segmentation allows the system to process and prioritize critical objects separately, improving detection precision for safety-critical elements while managing system complexity through hierarchical processing.
Solution Approach 2:
The patent applies different processing criteria to different groups of objects. High-importance objects in the first group receive prioritized processing with stricter selection criteria, while low-importance objects in the second group use different criteria. This local quality approach optimizes resource allocation and improves overall system precision where it matters most.
2Reliability
If multiple objects are identified and prioritized based on importance, then reliability is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary classification of objects into importance-based groups before detailed processing. By pre-segmenting objects into first and second groups based on importance criteria (such as object type, position, and potential risk), the system establishes a prioritized processing order that ensures critical objects are identified and handled first, improving reliability without excessive time loss.
Solution Approach 2:
The patent changes processing parameters dynamically based on object importance. Different selection criteria and priority thresholds are applied to objects in the first group versus the second group. This parameter differentiation allows the system to allocate computational resources efficiently, ensuring high-reliability processing for critical objects while reducing processing overhead for less critical ones.
3Measurement precision
If objects are classified into groups based on importance, then object detection accuracy is improved, but device complexity increases
Solution Approach 1:
The patent divides detected objects into distinct groups (first group and second group) based on importance criteria such as object type, spatial position, and potential impact on autonomous vehicle operation. This segmentation improves classification accuracy by applying group-specific criteria while managing complexity through structured categorization rather than uniform complex analysis for all objects.
Solution Approach 2:
The patent applies different classification criteria and importance thresholds to different object groups. Objects in the first group undergo more stringent classification with higher importance weights, while objects in the second group use different criteria. This local quality approach improves overall classification accuracy for critical objects without uniformly increasing complexity across all processing operations.
4Productivity
If priority is assigned based on longitudinal distance, then productivity is improved, but measurement precision requirements increase
Solution Approach 1:
The patent uses longitudinal distance as a key parameter for assigning priority within object groups, particularly for sorting objects within the first group. By changing the priority assignment parameter to include longitudinal position, the system improves processing efficiency and productivity by automatically prioritizing closer objects. This approach balances productivity gains with the existing precision capabilities of LiDAR distance measurement.
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 safety and stability in autonomous driving by prioritizing critical objects for display, reducing the risk of accidents by ensuring accurate object identification and representation even in error conditions.
Implementation Method 1
The LiDAR may measure the distance between the LiDAR and the object based on a time interval between transmitting laser and receiving the laser reflected from the object
Implementation Method 2
The vehicle may identify a position of a point on the surface of the object, within the three-dimensional space which the vehicle occupies, based on an angle of the transmitted laser and the distance to the object
Data Source
AI summary
An object display apparatus and a method thereof are provided. The object display apparatus includes a sensor, such as a light detection and ranging (LiDAR) device, and a processor. The processor determines an object located in front of a vehicle based on contour points in a first frame obtained via the sensor at a first time, determines at least one of lane information, longitudinal position information, lateral position information, classification information, number information, or size information, determines whether the object is included in a first group among a plurality of groups, sorts the object at least one other object included in the first group, determines a priority of the object among all objects identified in the first frame, based on a priority of the first group and a result of the sorting, and selects the object as a display target in a second frame.