Autonomous Vehicle Collaboration for Object Detection Safety
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Solution Overview
Problem
Autonomous vehicles face challenges in safely detecting and responding to road objects and animal presence, requiring improved object detection and collaboration methods to enhance safety and efficiency on roads with increasing autonomous vehicle integration.
Innovation Solution
A system and method for vehicle collaboration that involves receiving and analyzing data on road detections, determining comparative vehicle priorities, forming protective layers, and retraining machine learning models to suggest structured movement paths, utilizing a networked computer environment and cloud computing for data sharing and vehicle communication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous vehicles use multiple sensors (camera, radar, sonar, GPS, odometry, IMU, LiDAR) for object detection, then detection capability and safety are improved, but system complexity and computational requirements increase
Solution Approach 1:
The patent combines data from multiple sensor types (camera, radar, sonar, GPS, odometry, IMU, LiDAR) into a unified object detection and tracking system. By merging sensor inputs and processing them through a common framework, the system achieves comprehensive detection capability while managing complexity through integrated processing rather than separate systems for each sensor.
Solution Approach 2:
The object detection system is designed to handle multiple sensor types and detection scenarios through a universal processing framework. The system can detect various objects (animals, debris, other vehicles) using the same core detection algorithms, making the system multi-functional and reducing the need for separate specialized systems for each detection task.
2Reliability
If autonomous vehicles communicate and collaborate with each other to form protective layers, then safety and coordination are improved, but communication overhead and system coordination complexity increase
Solution Approach 1:
The system performs preliminary actions by pre-establishing communication protocols and coordination rules for vehicle collaboration. Vehicles are pre-configured with algorithms for forming protective layers and determining priority, so that when collaboration is needed, the vehicles can quickly execute pre-planned coordination strategies without complex real-time negotiations.
Solution Approach 2:
Each autonomous vehicle independently determines its own priority level and makes self-service decisions about whether to join or form protective layers. The vehicles use their own sensor data and communication with others to autonomously decide their role in collaboration scenarios, reducing the need for centralized control and simplifying the overall coordination system.
3Object-affected harmful factors
If the system determines comparative priority of vehicles and forms protective layers, then animal and human safety is improved, but decision-making complexity and processing time increase
Solution Approach 1:
The system uses parameter changes by assigning priority levels (comparative priority) to different vehicles based on their situation, sensor data, and mission criticality. This parameter-based approach allows the system to quickly categorize and rank vehicles without complex real-time analysis of every possible factor, enabling fast decision-making while still achieving comprehensive safety considerations.
4Measurement precision
If machine learning models are retrained continuously to improve detection accuracy, then detection precision is improved, but computational resource consumption and processing time increase
Solution Approach 1:
The system implements periodic action by retraining machine learning models at scheduled intervals or based on accumulated data thresholds rather than continuously. This approach allows the system to improve detection precision through regular model updates while avoiding the excessive computational energy consumption of continuous retraining, balancing accuracy improvements with resource conservation.
Data Source
AI summary
A method, computer system, and a computer program product for vehicle collaboration is provided. The present invention may include receiving data about a detection in a road. The present invention may include gathering additional data based on the received data. The present invention may include determining a comparative priority of at least one available vehicle. The present invention may include forming a protective layer using the at least one available vehicle. The present invention may include determining a structured movement path. The present invention may include retraining a machine learning model.