Vehicle Control Transfer Triggers for Pothole Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Autonomous driving systems face challenges in detecting and avoiding obstacles, such as potholes, that are not adequately represented in roadmaps or detected by sensors in a timely manner, which can lead to potential damage or unsafe driving scenarios.
Innovation Solution
A method for generating and using signatures of visual information to identify triggers for human intervention and obstacles, involving dimension expansion and merge operations to create robust and power-efficient object detection, allowing for accurate obstacle detection and avoidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous driving systems use standard sensor detection and roadmap-based navigation, then the system operates with simple control logic, but it fails to detect obstacles like potholes that are not represented in roadmaps or detected by sensors in time
Solution Approach 1:
The system performs preliminary analysis of visual information by generating signatures and comparing them against databases of known obstacle patterns before final detection is required. This allows the system to proactively identify potential obstacles like potholes earlier in the detection pipeline, improving both reliability and response time by preparing detection results in advance.
2Measurement precision
If the system processes visual information from multiple vehicles to identify triggers for human intervention, then detection accuracy improves, but computational complexity and power consumption increase
Solution Approach 1:
The system extracts only the most relevant features from visual information by generating compact signatures that capture essential obstacle characteristics. Instead of processing complete images or videos from multiple vehicles, the system extracts key visual features and compares them against a database of known obstacle patterns, significantly reducing computational power consumption while maintaining high detection accuracy.
Solution Approach 2:
The system merges visual information from multiple vehicles by comparing signatures across different data sources. Instead of processing all raw visual data from each vehicle separately, the system combines signature comparisons to identify consistent obstacle patterns, reducing overall computational load while improving detection reliability through multi-vehicle validation.
3Reliability
If the system uses dimension expansion and merge operations for object detection, then detection robustness improves, but processing complexity increases
Solution Approach 1:
The detection process is segmented into distinct modular operations: signature generation, dimension expansion, merging operations, and database comparison. Each module performs a specific function independently, making the overall complex process manageable and maintainable. The segmentation allows the system to achieve robust detection through multiple processing stages without requiring the entire system to be redesigned as a single complex algorithm.
Data Source
AI summary
A method for finding at least one trigger for human intervention in a control of a vehicle, the method may include receiving, from a plurality of vehicles, and by an I/O module of a computerized system, visual information acquired during situations that are suspected as situations that require human intervention in the control of at least one of the plurality of vehicles; determining, based at least on the visual information, the at least one trigger for human intervention; and transmitting to one or more of the plurality of vehicles, the at least one trigger.