Vehicle Trajectory Collision Avoidance Using Object Sector Classification
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Solution Overview
Problem
Current vehicular navigation systems, particularly for airborne drones and autonomous vehicles, face challenges in efficiently avoiding collisions with objects due to high processing loads and the need for real-time obstacle detection and path re-planning, which can lead to delays and increased computational demands.
Innovation Solution
The system employs range finding data from sensors like lidar, radar, and sonar to classify detected objects into subsets adjacent to the vehicle's trajectory, determining whether changing direction would cause a collision, and adjusts the path accordingly, with processing either on-board or in a ground control station to minimize latency and optimize navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time obstacle detection and path re-planning are performed using current navigation systems, then collision avoidance capability is improved, but processing load and computational demands increase significantly
Solution Approach 1:
The observation area is divided into multiple subsets (e.g., left, center, right sectors) relative to the vehicle's trajectory. Objects are classified into these subsets based on their position, allowing the system to process spatial information in manageable segments rather than as a single complex scene, thereby reducing computational load while maintaining collision avoidance capability
Solution Approach 2:
Objects are pre-classified into trajectory subsets before path re-planning is initiated. This preliminary classification organizes spatial data in advance, enabling faster decision-making during critical moments when avoidance maneuvers are needed, thus reducing real-time processing demands
2Measurement precision
If comprehensive obstacle detection is performed across the entire observation area, then detection precision is improved, but processing time increases causing delays
Solution Approach 1:
The observation area is segmented into multiple subsets, allowing the system to process spatial information in parallel across different sectors. This segmentation enables comprehensive coverage of the entire observation area while reducing the computational burden on any single processing unit, thereby maintaining detection precision without excessive processing time
Solution Approach 2:
The system classifies objects into trajectory subsets based on their relevance to potential collision risks. By focusing processing resources on objects within or near the trajectory subsets rather than uniformly processing all detected objects, the system achieves sufficient detection precision for safety-critical applications while reducing overall processing time
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 enables efficient collision avoidance with reduced processing load, allowing for real-time path adjustments and improved safety by classifying objects into sectors, validating trajectory changes, and optimizing vehicle movement to prevent collisions.
Implementation Method 1
The range finding data may be obtained using a range finding device, such as a lidar, radar, sonar or ultrasonic device
Implementation Method 2
The range finding data may be obtained using a range finding device, such as a lidar, radar, sonar or ultrasonic device
Implementation Method 3
The range finding data may be obtained using a range finding device, such as a lidar, radar, sonar or ultrasonic device
Data Source
Figure 1
Figure 2A~2B
Figure 2C
AI summary
The present disclosure relates to vehicular navigation and collision avoidance with objects. The detected object is classified into one of the one or more subsets based on the location within the observation area. When a direction the vehicle is moving on needs to change, the detected object in a particular subset of the one or more subsets in the observation area may be taken into account in further control of the vehicle.