Remote Assistance Operator Alertness System for Autonomous Vehicles
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Solution Overview
Problem
Autonomous vehicles face challenges in accurately identifying objects with low detection confidence, leading to potential safety issues, as existing systems may struggle with uncertain or unfamiliar scenarios, necessitating remote assistance to ensure safe operation.
Innovation Solution
A computing system operates in two modes: a default mode for providing remote assistance by receiving environment data with low detection confidence and offering control instructions, and a secondary mode that triggers user interface alerts and receives responses from human operators to maintain alertness and confirm object identifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If autonomous vehicle systems rely solely on automated object detection, then automation extent increases, but measurement precision deteriorates for objects with low detection confidence
Solution Approach 1:
A remote operator serves as an intermediary between the autonomous vehicle system and uncertain detection scenarios. When the detection confidence falls below the threshold, the system transitions control to a human operator who can accurately identify objects and provide control inputs, thereby maintaining measurement precision while preserving overall automation for confident detections
Solution Approach 2:
The system dynamically adjusts the level of automation based on detection confidence levels. For high-confidence detections, fully automated control is maintained; for low-confidence detections, control dynamically shifts to human operators. This dynamic adjustment resolves the contradiction by optimizing both automation extent and measurement precision according to real-time conditions
2Measurement precision
If remote assistance operators continuously monitor all vehicle operations, then measurement precision improves, but loss of time increases due to operator fatigue and reduced alertness
Solution Approach 1:
Instead of requiring continuous operator monitoring of all operations, the system applies partial action by engaging human operators only when detection confidence falls below the threshold. This selective engagement maintains measurement precision for critical cases while minimizing time loss by avoiding unnecessary operator involvement during routine high-confidence operations
Solution Approach 2:
The system implements periodic transitions to human operator control based on detection confidence thresholds rather than continuous monitoring. This periodic engagement strategy maintains operator alertness by providing regular but not excessive involvement, thereby balancing measurement precision with response time efficiency
3Measurement precision
If the system requests human operator intervention for every low-confidence detection, then measurement precision improves, but productivity decreases due to frequent mode switching
Solution Approach 1:
The system applies partial action by requesting human operator intervention only when necessary (below threshold confidence) rather than for every detection. This selective approach maintains measurement precision for uncertain cases while preserving productivity by avoiding frequent mode switching for routine high-confidence operations
Solution Approach 2:
Different quality levels of object identification are applied locally based on detection confidence. High-confidence detections proceed with automated processing (maintaining productivity), while low-confidence detections receive enhanced human review (ensuring precision). This local quality differentiation resolves the contradiction by optimizing both measurement precision and productivity for different operational contexts
Data Source
AI summary
Examples described may enable provision of remote assistance for an autonomous vehicle. An example method includes a computing system operating by default in a first mode and periodically transitioning from operation in the first mode to operation in a second mode. In the first mode, the system may receive environment data provided by the vehicle and representing object(s) having a detection confidence below a threshold, where the detection confidence is indicative of a likelihood of correct identification of the object(s), and responsive to the object(s) having a confidence below the threshold, provide remote assistance data comprising an instruction to control the vehicle and/or a correct identification of the object(s). In the second mode, the system may trigger user interface display of remote assistor alertness data based on pre-stored data related to an environment in which the pre-stored data was acquired, and receive a response relating to the alertness data.