Remote Intervention Forecasting for Autonomous Vehicle Fleets
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Solution Overview
Problem
Existing systems do not adequately address the burden on operators due to a sudden increase in the number of remote interventions required for autonomous vehicles within a short period, leading to increased mental strain and delayed responses.
Innovation Solution
An information processing method that estimates the number of autonomous vehicles requiring remote intervention and, when necessary, outputs control instructions or notifications to mitigate the operator's burden by preventing a sudden increase in interventions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system allows multiple autonomous vehicles to request remote intervention simultaneously, then the system can respond to all vehicles requiring assistance, but the operator's burden increases due to sudden increases in intervention requests
Solution Approach 1:
The system performs preliminary estimation of the number of vehicles requiring remote intervention before the actual intervention requests occur. By predicting future intervention needs based on current vehicle states and trajectories, the system can prepare operators in advance, distributing requests over time to avoid suddenbursts that overwhelm operators.
Solution Approach 2:
The system dynamically adjusts the intervention request distribution by controlling vehicle behaviors. When prediction indicates a sudden increase in intervention requests, the system modifies vehicle travel plans to delay or prevent certain interventions, thereby smoothing the temporal distribution of requests and reducing operator burden while maintaining necessary safety responses.
2Speed
If the system processes all intervention requests immediately, then all vehicles receive timely assistance, but operator fatigue increases due to high-frequency requests
Solution Approach 1:
The system performs preliminary estimation of intervention needs, allowing operators to mentally prepare for upcoming tasks. This advance notice enables operators to organize their attention and resources before the actual intervention sequence begins, reducing the cognitive shock of sudden high-frequency requests.
Solution Approach 2:
The system transforms the intervention request pattern from irregular bursts to a more periodic and predictable sequence. By controlling vehicle behaviors to stagger intervention timing, the system creates a rhythm that operators can anticipate and maintain, reducing fatigue associated with unpredictable high-frequency interruptions.
3Quantity of substance
If the system increases remote monitoring capacity to handle more vehicles, then more vehicles can be monitored simultaneously, but the complexity of the monitoring system increases
Solution Approach 1:
The system employs autonomous vehicles to perform preliminary self-assessment of their intervention needs. Vehicles autonomously evaluate their own states and generate predictions about requiring remote intervention, reducing the burden on the central monitoring system to continuously analyze every vehicle's detailed state. This self-service approach allows the system to scale to more vehicles without proportionally increasing monitoring complexity.
Solution Approach 2:
The intervention prediction function serves as an intermediary layer between raw vehicle data and operator monitoring. Instead of operators directly monitoring all vehicle parameters, the prediction system filters and processes information to generate simplified intervention probability estimates, reducing the information complexity presented to operators while enabling monitoring of larger vehicle fleets.
Data Source
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AI summary
An information processing method according to one aspect of the present disclosure is an information processing method performed in an information processing device, the information processing method including: obtaining, for each of two or more autonomous mobile bodies, first information related to the autonomous mobile body (S11); estimating, based on the first information, second information indicating a total number of two or more target autonomous mobile bodies beginning to be operated or monitored by a remote operator during a predetermined period, the two or more target autonomous mobile bodies being among the two or more autonomous mobile bodies (S12); determining whether the total number indicated by the second information is greater than or equal to a predetermined value (S13); and outputting at least one of third information for controlling the target autonomous mobile bodies or fourth information for providing the remote operator with notification, when the total number indicated by the second information is determined to be greater than or equal to the predetermined value (S15 to S18).