Remote Assistance Initiation for Autonomous Vehicles via Navigation Anomaly Detection
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Solution Overview
Problem
Autonomous vehicles face challenges in navigating navigation anomalies without manual intervention, as existing systems require autonomous vehicles to request assistance, which can be inefficient and delay response times.
Innovation Solution
A computer-implemented method that uses data from human-driven vehicles to identify navigation anomalies and automatically initiate remote assistance sessions for autonomous vehicles by creating geofence entries in an anomaly database, allowing for proactive support when an autonomous vehicle enters a known anomaly area.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous vehicles request assistance manually when encountering navigation anomalies, then the system can provide remote support, but response time is delayed and efficiency is reduced
Solution Approach 1:
The system performs preliminary actions by collecting navigation data from human-driven vehicles, identifying anomalies proactively, and preparing remote assistance sessions before autonomous vehicles encounter issues. This allows the system to have assistance ready in advance, eliminating delays associated with manual requests.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring navigation data from multiple vehicles, comparing actual paths against expected routes, and using this information to proactively identify and prepare for potential anomalies before they affect autonomous vehicles.
2Loss of time
If the system proactively initiates remote assistance sessions based on anomaly data, then response time is reduced, but system complexity increases
Solution Approach 1:
The system introduces an intermediary anomaly detection and management layer that sits between data collection and remote assistance execution. This intermediary automatically processes navigation data, identifies anomalies, manages geofence definitions, and triggers assistance sessions, thereby managing complexity centrally rather than distributing it across multiple components.
Solution Approach 2:
The system implements self-service by automatically detecting anomalies from collected data, defining geofences around anomaly locations, and initiating remote assistance sessions without human intervention. This automation reduces the need for manual system configuration and operation, managing complexity through self-managing processes.
3Extent of automation
If manual intervention is required for navigation anomalies, then autonomous vehicles can operate independently, but assistance efficiency is reduced
Solution Approach 1:
The system performs preliminary anomaly detection and assistance preparation based on data from human-driven vehicles, so that when autonomous vehicles encounter anomalies, assistance is already prepared and can be provided immediately, maintaining vehicle independence while improving assistance efficiency.
Solution Approach 2:
The system uses feedback from monitoring navigation data of multiple vehicles to continuously improve anomaly detection accuracy and assistance timing, enabling more efficient automated support while preserving autonomous vehicle operation independence.
Data Source
AI summary
The present disclosure is directed to using anomaly data detected in traffic data to efficiently initiate remote assistance sessions. In particular, a computing system can receive, from a computing device associated with a human-driven vehicle, travel data for the human-driven vehicle. The computer system can identify a navigation anomaly associated with the human-driven vehicle based on the travel data. The computer system can generate, based on the identified navigation anomaly, an anomaly entry for storage in an anomaly database, the anomaly entry comprising geofence data describing a geographic area associated with the navigation anomaly. The computer system can determine, based on location data received from an autonomous vehicle and the geofence data, that the autonomous vehicle is entering the geographic area associated with the navigation anomaly. The computer system can initiate a remote assistance session with the autonomous vehicle.