Blind-Spot Obstacle Probability Estimation for Mobile Route Control
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Solution Overview
Problem
Existing autonomous mobile body control systems face challenges in determining appropriate traveling routes due to blind spots not monitored by cameras, leading to potential collisions when obstacles are misjudged in probability.
Innovation Solution
An information processing apparatus that receives observation data from sensors, determines obstacle presence, and specifies the probability of obstacles in unmonitored areas by using data from nearby monitored areas, adjusting the probability based on the presence or absence of obstacles in those areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a certain probability of obstacle presence is presumed in blind spots, then safety is improved, but the autonomous mobile body cannot travel through blind spot areas even when no obstacle is actually present, reducing productivity
Solution Approach 1:
The patent applies local quality by differentiating the probability of obstacle presence based on specific locations and contexts. Instead of uniformly assuming obstacle presence in all blind spots, the system calculates different probability values for different areas based on local characteristics such as proximity to known obstacles, historical data, and environmental factors. This allows the autonomous mobile body to navigate through areas with low probability while maintaining high safety in areas with high probability.
Solution Approach 2:
The patent dynamically changes the probability parameter for obstacle presence in blind spots based on multiple factors including observation data from sensors, determination results from nearby areas, and temporal variations. By continuously updating this probability parameter rather than using a fixed value, the system can adapt to changing conditions and make more informed navigation decisions, balancing safety and productivity.
2Productivity
If the probability of obstacle presence in blind spots is set to a low value, then productivity is improved, but the autonomous mobile body may collide with obstacles, reducing reliability
Solution Approach 1:
The patent implements feedback mechanisms by continuously monitoring observation data from sensors and determination results from nearby areas to update the probability of obstacle presence in blind spots. This feedback loop allows the system to learn from actual conditions and adjust probability values accordingly, ensuring that low probability values do not lead to collisions while maintaining productivity when conditions permit.
Solution Approach 2:
The patent performs preliminary calculations of obstacle probability in blind spots before the autonomous mobile body reaches those areas. By using determination results from nearby areas and sensor data to pre-assess risk levels, the system can prepare appropriate navigation decisions in advance, preventing collisions before they occur while maintaining efficient travel routes.
3Measurement precision
If monitoring cameras are installed to cover all areas, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent introduces an intermediary computational layer that processes and integrates data from existing sensors and determination results to infer obstacle presence in blind spots. Instead of installing cameras in every area, the system uses this intermediary processing to extend the effective monitoring coverage through probabilistic reasoning based on available data from nearby areas and sensor observations.
Solution Approach 2:
The patent creates a virtual model or representation of the physical environment including blind spots, where obstacle probability is calculated and updated based on sensor data and determination results. This virtual copy allows the system to reason about areas not directly observable by cameras, achieving comprehensive monitoring coverage without physically installing cameras everywhere.
Data Source
AI summary
An information processing apparatus according to the present disclosure includes, a communication unit configured to receive observation data from at least one sensor, the observation data being obtained by observing an obstacle in a plurality of areas, a determination unit configured to determine presence/absence of an obstacle in the plurality of areas by using the observation data, and a specifying unit configured to specify, when the presence/absence of an obstacle in a first area included in the plurality of areas is unknown, a probability of presence of an obstacle in the first area by using a determination result indicating presence/absence of an obstacle in an area near the first area, in which the probability changes depending on presence/absence of an obstacle in the area near the first area.