Autonomous Vehicle Occlusion Grid Sharing
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Solution Overview
Problem
Conventional methods for guiding autonomous vehicles through environments rely on limited information, leading to uncertain and erratic actions, especially when dealing with occluded regions that sensors cannot detect, causing the vehicles to be overly cautious.
Innovation Solution
The system receives and combines occupancy data from multiple sources, including other vehicles and remote computing devices, to generate a more complete occlusion grid, allowing the vehicle to make informed decisions about navigating through occluded areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous vehicles rely on limited sensor information for navigation decisions, then the vehicle can operate with simpler sensing requirements, but the navigation certainty and decision-making reliability deteriorate
Solution Approach 1:
The patent combines occupancy data from multiple vehicles and remote computing devices to create a comprehensive occlusion grid. By merging sensor data from multiple sources, the system overcomes the limitations of individual vehicle sensors and achieves reliable navigation certainty without requiring each vehicle to have complete environmental detection capability.
Solution Approach 2:
The patent introduces a communication network and remote computing devices as intermediaries to transfer occupancy data between vehicles. This mediator system enables vehicles to access information beyond their own sensor range, resolving the contradiction between limited sensor coverage and the need for reliable navigation decisions.
2Productivity
If autonomous vehicles operate cautiously due to limited information about occluded regions, then safety is maintained, but navigation efficiency and speed deteriorate
Solution Approach 1:
By combining occupancy data from multiple vehicles, the system creates a more complete picture of occluded regions. This merged information allows vehicles to navigate occluded areas with greater confidence and efficiency while maintaining safety through comprehensive environmental awareness.
3Measurement precision
If autonomous vehicles use only their own sensor data for decision-making, then the system complexity is reduced, but the environmental awareness and decision accuracy deteriorate
Solution Approach 1:
The communication network acts as an intermediary that handles the complexity of data collection and distribution. Individual vehicles receive processed occupancy data without needing to implement complex multi-source data fusion systems themselves, achieving high measurement precision while managing device complexity through distributed architecture.
Data Source
AI summary
Techniques are discussed for controlling a vehicle, such as an autonomous vehicle, based on occluded areas in an environment. An occluded area can represent areas where sensors of the vehicle are unable to sense portions of the environment due to obstruction by another object or sensor limitation. An occluded region for an object is determined by the vehicle as part of an occlusion grid, from the perspective of the vehicle. The vehicle may receive another occlusion grid from another source, such as another vehicle or a remote computing device that stores and distributes occlusion grids. The other occlusion grid may be from a different perspective than the occlusion grid generated by the vehicle, and may include occupancy data for the region that is otherwise occluded from the perspective of the vehicle. The vehicle can be controlled to traverse the environment based on the occupancy data received from the other source.