Remote Operator Queueing for Autonomous Vehicle Safety Requests
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Solution Overview
Problem
Autonomous vehicles face challenges in navigating unpredictable scenarios, requiring rapid and accurate guidance to ensure safety and minimize delays, as they may encounter situations where their confidence levels in path planning and object detection are insufficient, necessitating remote operator assistance.
Innovation Solution
A system that prioritizes and queues requests for remote operator input based on safety considerations, filters operators by experience and familiarity, and rapidly conveys sensor data and operation state information to assigned operators, enabling timely guidance through processor-executable instructions to assist the vehicle in navigating complex situations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple autonomous vehicles send requests for remote operator assistance simultaneously, then the system receives multiple requests that need 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 assigning them to operators. This advance organization ensures that when operators become available, they can immediately work on the most critical requests without delay, resolving the contradiction between handling multiple requests and maintaining fast response times.
Solution Approach 2:
The system segments the fleet of autonomous vehicles into different priority groups based on safety scores. By dividing requests into priority tiers (high, medium, low), the system can process multiple requests simultaneously at different priority levels, maintaining high throughput while ensuring critical requests receive immediate attention, thus reducing overall response delay.
2Reliability
If the system prioritizes requests based on safety scores, then critical requests are handled first ensuring passenger safety, but the complexity of request management increases
Solution Approach 1:
The system changes the parameter of request management by introducing a quantitative safety score metric. Instead of complex multi-criteria evaluation, each request is assigned a numerical safety score that automatically determines its priority. This parameter-based approach ensures reliable safety prioritization while keeping the management system simple and computationally efficient.
Solution Approach 2:
The system implements self-service by automatically prioritizing and queueing requests based on their safety scores without requiring manual intervention. The automated queue management system handles request sorting, assignment, and tracking independently, reducing management complexity while maintaining high reliability through consistent safety-based prioritization.
3Reliability
If remote operators are filtered by experience and familiarity with specific situations, then the quality of guidance improves, but the time to find and assign appropriate operators increases
Solution Approach 1:
The system performs preliminary action by pre-filtering and pre-matching operators with requests based on experience and situational familiarity before assignment. Operators are pre-qualified for specific request types, and when a request enters the queue, the system can quickly assign it to a pre-identified suitable operator, eliminating the need for time-consuming search and evaluation during critical moments.
Solution Approach 2:
The system introduces an intermediary matching layer between requests and operators. This intermediary component automatically evaluates operator qualifications against request requirements and makes assignments based on compatibility scores. This mediator ensures high guidance quality through careful matching while keeping assignment time minimal through automated decision-making.
4Productivity
If the system processes requests in real-time with minimal delay, then operational efficiency is maintained, but the accuracy and thoroughness of remote operator assessment may be compromised
Solution Approach 1:
The system performs preliminary assessment and prioritization of requests before full operator engagement. By pre-evaluating request urgency and safety scores, the system prepares the most critical requests for immediate operator attention, ensuring that real-time processing focuses only on high-priority cases where speed is essential, while less urgent requests can undergo more thorough assessment.
Solution Approach 2:
The system applies local quality by providing different levels of assessment thoroughness based on request priority. High-priority requests receive immediate attention with streamlined assessment for speed, while lower-priority requests undergo more comprehensive evaluation. This localized approach to assessment quality maintains operational efficiency for critical cases while ensuring thoroughness where time permits.
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.