Vehicle Stuck-Condition Alerts Using Distance Trend Thresholds
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Autonomous vehicles often experience stuck conditions due to weather or road conditions, leading to inefficiencies and the need for continuous operator attention to determine the appropriate response.
Innovation Solution
Implementing systems and methods to detect stuck conditions by analyzing vehicle data over a time window and comparing it to a threshold, generating alerts only when a true stuck condition is identified, distinguishing it from temporary stops like traffic lights or traffic.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If continuous operator monitoring is implemented to detect stuck conditions, then vehicle safety and operational reliability are improved, but operator workload and system complexity increase
Solution Approach 1:
The system performs preliminary analysis of vehicle data using machine learning models to predict potential stuck conditions before they occur. By continuously analyzing sensor data, vehicle speed, location, and environmental factors, the system prepares alert recommendations in advance, allowing operators to respond proactively rather than reactively to actual stuck conditions.
Solution Approach 2:
An intermediate machine learning-based alert system is introduced between the vehicle operations and human operators. This intermediary automatically processes raw vehicle data, identifies patterns indicating stuck conditions, and presents processed alert recommendations to operators, filtering out normal operational variations and focusing operator attention only on genuine issues.
2Measurement precision
If alert thresholds are set to be highly sensitive to detect all potential stuck conditions, then detection accuracy is improved, but false positive rate increases leading to operator alert fatigue
Solution Approach 1:
The system dynamically adjusts detection parameters and thresholds based on contextual factors such as vehicle type, route, time of day, weather conditions, and traffic patterns. Rather than using fixed thresholds, the machine learning model adapts parameters in real-time to distinguish between normal variations (such as temporary stops at traffic lights) and genuine stuck conditions, maintaining high detection accuracy while minimizing false positives.
Solution Approach 2:
The system implements feedback loops where operator responses to alerts and actual vehicle outcomes are continuously fed back into the machine learning model. This allows the system to learn from past decisions, refine its detection algorithms, and improve its ability to distinguish true stuck conditions from normal operations over time, reducing false positives and maintaining operator confidence in the alert system.
Data Source
AI summary
Provided are methods for optimizing alerts for vehicles experiencing stuck conditions, which can include receiving, using at least one processor, data associated with a distance between a location of a vehicle and a destination; determining, using the at least one processor, a derivative of the distance between the location of the vehicle and the destination with respect to a window of time; determining, using the at least one processor, a threshold based on the data associated with the distance between the location of the vehicle and the destination; comparing the derivative to the threshold; and based on the comparison, generating data representing at least one alert indicative of a stuck condition of the vehicle. Systems and computer program products are also provided.