Onboard Assistant Checklists for AV Sensor-Based SOP Selection
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Solution Overview
Problem
Existing autonomous vehicles face challenges in correlating complex sensor data with appropriate standard operating procedures (SOPs) for customer service requests, particularly in situations requiring human intervention, due to the nontrivial nature of determining root causes and selecting the correct SOP.
Innovation Solution
A machine learning engine analyzes sensor inputs to correlate them with a database of SOPs, enabling the selection of an appropriate SOP based on real-world examples, which is then displayed to a customer service agent for handling customer service incidents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor data is collected and analyzed to determine root causes for customer service requests, then customer service accuracy is improved, but system complexity increases
Solution Approach 1:
A machine learning engine is introduced as an intermediary component between sensor data collection and SOP selection. This intermediary automatically processes sensor data, determines root causes, and maps them to appropriate SOPs, thereby improving customer service accuracy while managing system complexity through automated intelligence rather than manual processing
Solution Approach 2:
The system creates a digital model or representation of sensor data patterns and their corresponding root causes through machine learning. This copied knowledge model enables the system to accurately identify issues by comparing current sensor readings against learned patterns, improving diagnostic accuracy without requiring complex manual analysis of raw sensor data
2Adaptability or versatility
If multiple SOPs are stored in a data store for different customer service scenarios, then adaptability is improved, but information retrieval time increases
Solution Approach 1:
The machine learning engine uses feedback from sensor data to automatically select the most relevant SOP from the data store. The system continuously learns from past customer service interactions and sensor patterns, refining its ability to quickly retrieve appropriate SOPs based on current sensor readings, thereby reducing retrieval time while maintaining adaptability across diverse scenarios
Solution Approach 2:
Sensor data is pre-processed and correlated with potential root causes before actual customer service incidents occur. The machine learning engine performs preliminary analysis of sensor patterns and pre-identifies likely issues, so when a customer service request is made, the appropriate SOP can be rapidly retrieved based on pre-established correlations rather than searching from scratch
Data Source
AI summary
There is disclosed herein a method of providing customer service, including receiving a customer service request from an autonomous vehicle (AV), the customer service request comprising sensor data collected by the AV; selecting a standard operating procedure (SOP) for responding to the customer service request, the SOP selected from a data store comprising a plurality of SOPs, wherein selecting the SOP is based at least in part on the sensor data from the AV, and wherein the SOP includes a human-usable resolution script; and handling the customer service request, including displaying the human-usable resolution script to a human customer service agent to assist the human customer service agent in handling the customer service request.