Remote Image Annotation for Autonomous Vehicle Navigation
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Solution Overview
Problem
Autonomous vehicles often encounter navigation challenges due to complex or unexpected environments, such as obstacles, road conditions, and traffic, where they may struggle to identify and classify objects, leading to temporary navigation disruptions.
Innovation Solution
The implementation of a system that allows autonomous vehicles to request remote assistance from human or computer assistants, who can analyze sensor data, including images and video, to identify and classify objects, and provide navigation strategies to the vehicle.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous vehicles operate without remote assistance, then device complexity is reduced, but navigation reliability deteriorates in complex environments
Solution Approach 1:
A remote assistance system acts as an intermediary between autonomous vehicles and human operators. When the vehicle's autonomous systems encounter uncertain situations or objects they cannot confidently identify, the system transmits sensor data to remote operators who provide guidance and classification information, thereby improving navigation reliability without requiring the vehicle itself to have complete autonomous capabilities
Solution Approach 2:
The navigation system is segmented into autonomous operation modes and remote assistance modes. The vehicle handles routine navigation autonomously, while complex or uncertain situations are handled by remote operators. This segmentation allows the system to maintain simplicity for common tasks while achieving high reliability for complex tasks through human intervention
2Measurement precision
If remote assistance is implemented for object identification, then measurement precision of objects improves, but loss of time in communication and processing increases
Solution Approach 1:
Instead of continuously communicating with remote operators, the system only initiates communication when object identification confidence falls below a threshold. The vehicle performs partial identification autonomously first, then seeks remote assistance only for uncertain cases, minimizing communication time while maintaining precision when needed
Solution Approach 2:
The vehicle's autonomous systems perform preliminary object detection and classification before seeking remote assistance. This preliminary action filters out clearly identifiable objects, so remote operators only need to review and confirm uncertain cases, reducing their workload and the overall time required for accurate identification
3Productivity
If autonomous vehicles navigate complex environments independently, then productivity is maintained, but navigation disruptions increase due to unrecognized objects
Solution Approach 1:
The system implements a feedback loop where remote operators provide classification information back to the vehicle's autonomous systems. This feedback is used to update the vehicle's object recognition models and improve future autonomous identification accuracy, reducing navigation disruptions over time while maintaining productivity
Data Source
AI summary
Example embodiments relate to techniques for enabling one or more systems of a vehicle (e.g., an autonomous vehicle) to request remote assistance to help the vehicle navigate in an environment. A computing device may be configured to receive a request for assistance from a vehicle. The request may include an image frame representative of a portion of an environment. The computing device may also be configured to initiate display of a graphical user interface to visually represent the image frame. Further, the computing device may determine a bounding region for the image frame. The bounding region may be associated with one or more objects in the image frame. Additionally, the computing device may be configured to receive, via the GUI, an input that includes an object identifier, and associate the object identifier with each of the one or more objects in the bounding region.