Occluded Obstacle Classification for Autonomous Vehicles
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Solution Overview
Problem
Existing vehicle navigation systems face challenges in accurately classifying and responding to occluded obstacles in the external environment, which can lead to unsafe or inefficient driving maneuvers.
Innovation Solution
A method and system that utilize sensors to acquire data, determine the occlusion status of obstacles, and classify them based on this status to inform driving maneuvers, enabling the vehicle to adapt its navigation accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the vehicle uses basic obstacle detection without occlusion analysis, then the system complexity is low, but the measurement precision of obstacle classification deteriorates
Solution Approach 1:
The patent segments the obstacle classification process into distinct stages: initial obstacle detection, occlusion status determination, and classification based on occlusion levels. This segmentation allows the system to achieve high classification accuracy by systematically analyzing different aspects of obstacle detection separately, rather than attempting to solve all problems simultaneously with a single complex system.
Solution Approach 2:
The patent applies preliminary action by determining the occlusion status of obstacles before final classification. The system first identifies whether sensor data for an obstacle is occluded or unoccluded, then uses this preliminary information to guide the classification process. This preliminary analysis improves overall classification accuracy without requiring the entire system to be maximally complex.
2Measurement precision
If the vehicle implements comprehensive occlusion analysis for all obstacles, then the obstacle classification accuracy improves, but the processing time increases
Solution Approach 1:
The patent applies local quality by determining occlusion status selectively for specific obstacles based on their relevance to the vehicle's path. Rather than performing comprehensive occlusion analysis on all detected objects uniformly, the system focuses computational resources on obstacles that require classification for navigation decisions. This selective approach maintains high accuracy for critical obstacles while reducing overall processing time.
3Reliability
If the vehicle uses simple obstacle detection without occlusion status, then the processing speed is high, but the reliability of driving maneuvers deteriorates
Solution Approach 1:
The patent determines occlusion status as a preliminary step before making driving maneuver decisions. By establishing whether sensor data is occluded or unoccluded in advance, the system ensures that subsequent classification and maneuver selection are based on reliable information. This preliminary occlusion analysis significantly improves the reliability of driving maneuvers without requiring continuous re-analysis during execution.
Data Source
AI summary
Obstacles located in an external environment of a vehicle can be classified. At least a portion of the external environment can be sensed using one or more sensors to acquire sensor data. An obstacle candidate can be identified based on the acquired sensor data. An occlusion status for the identified obstacle candidate can be determined. The occlusion status can be a ratio of acquired sensor data for the obstacle candidate that is occluded to all acquired sensor data for the obstacle candidate. A classification for the obstacle candidate can be determined based on the determined occlusion status. A driving maneuver for the vehicle can be determined at least partially based on the determined classification for the obstacle candidate. The vehicle can be caused to implement the determined driving maneuver. The vehicle can be an autonomous vehicle.