Autonomous Vehicle Visibility Grids for Occlusion Collision Avoidance
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Solution Overview
Problem
Autonomous vehicles face challenges in accident avoidance due to occlusions that limit sensor visibility, and the narrower field of view of human road users can lead to blind spots and accidents.
Innovation Solution
The method involves determining a visibility grid for external objects within the operational environment of an autonomous vehicle, identifying when the vehicle is in an occluded region, and altering driving parameters to avoid collisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensors are used to detect external objects, then detection capability is improved, but occlusions create blind spots that reduce detection reliability
Solution Approach 1:
The system performs preliminary action by determining visibility grids for external objects before the autonomous vehicle enters occluded regions. By predicting which areas will be occluded by objects like parked cars or pedestrians, the system proactively identifies potential hazards in advance and prepares appropriate driving responses, rather than reacting after detection fails in blind spots
Solution Approach 2:
The system transitions from traditional 2D sensor detection to 3D visibility grid reasoning. By constructing three-dimensional visibility grids that model spatial occlusions and generate multiple hypotheses about potential objects in occluded regions, the system adds a dimensional layer of reasoning that compensates for sensor blind spots and improves detection reliability
2Device complexity
If the autonomous vehicle maintains a narrow field of view like human drivers, then device complexity is reduced, but blind spots increase leading to more accidents
Solution Approach 1:
The system introduces visibility grids as an intermediary computational layer between sensor data and driving decisions. These grids act as a mediator that fills in information about occluded regions by reasoning about object positions and potential hazards, allowing the system to maintain a practical field of view while compensating for blind spots through computational modeling rather than adding more physical sensors
3Reliability
If the vehicle alters driving parameters frequently to avoid occluded regions, then safety is improved, but productivity decreases due to reduced speed and efficiency
Solution Approach 1:
The system applies partial action by selectively altering driving parameters only when and where necessary based on visibility grid analysis. Rather than conservatively slowing down in all occluded regions, the system identifies specific high-risk areas where hazards are likely to exist and adjusts driving behavior针对性地 (targetedly) in those zones, maintaining normal speed in safer areas to preserve overall driving efficiency
Data Source
AI summary
A visibility grid is determined for at least one external object within an operational environment of an autonomous vehicle. The visibility grid represents areas visible and areas obstructed for the external object. When the autonomous vehicle is positioned within an occluded region relative to the visibility grid of the external object is identified. Driving parameters of the autonomous vehicle are altered in response to being in the occluded region to avoid collision with the external object.