Autonomous Vehicle Remote Operation Queueing for Safety-Critical Requests
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Solution Overview
Problem
Autonomous vehicles often encounter situations they cannot navigate confidently, requiring remote operator assistance, but existing systems struggle to prioritize and manage these requests efficiently, leading to potential delays and safety risks.
Innovation Solution
A system that prioritizes and generates a queue for remote operator requests based on safety considerations, filters available remote operators by experience and familiarity with vehicle types or environments, and rapidly apprises assigned remote operators of the situation to provide real-time guidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple autonomous vehicles simultaneously request remote operator assistance, then the system receives more requests for processing, but the processing time and response delay increase
Solution Approach 1:
The system performs preliminary actions by pre-prioritizing requests in a queue based on safety scores before operators are assigned. This advance organization ensures that when operators become available, they can immediately work on the most critical requests without delay, resolving multiple requests efficiently as operators become available.
Solution Approach 2:
The system segments the fleet of autonomous vehicles into different priority groups based on safety scores. High-priority requests (those with higher safety scores indicating greater risk) are separated and handled separately from lower-priority requests, allowing operators to focus on critical situations first while maintaining organized processing of all requests.
2Ease of operation
If the system processes all remote operator requests equally, then simplicity is maintained, but safety-critical requests may be delayed
Solution Approach 1:
The system applies local quality by assigning different processing priorities to different requests based on their specific safety characteristics. Each request receives a customized safety score that determines its priority level, allowing the system to maintain simple overall operation while implementing targeted priority handling for safety-critical situations without complex manual intervention.
3Productivity
If remote operators are assigned without filtering by expertise, then assignment speed increases, but the quality of guidance decreases
Solution Approach 1:
The system uses feedback mechanisms by continuously monitoring operator performance, response times, and guidance quality. This feedback informs the matching algorithm to progressively improve operator-request pairings, maintaining fast assignment speeds while ensuring that operators with demonstrated expertise in specific scenarios are assigned to appropriate requests.
Solution Approach 2:
The system changes parameters by dynamically adjusting operator selection criteria based on the specific characteristics of each request. Rather than using fixed assignment rules, the system modifies matching parameters in real-time to optimize both assignment speed and guidance quality, selecting operators whose expertise best matches the current situation's requirements.
Data Source
AI summary
An autonomous vehicle fleet may include multiple autonomous vehicles. The autonomous vehicles of the fleet may be configured to request remote operator input in response to encountering a situation internally or in the environment that the vehicle is unable to resolve. The autonomous vehicle of the fleet requests remote operator input through a fleet queue system that prioritizes the input requests and matches requests to available remote operators for processing and resolving the situations.