Vehicle Response Control for Detected Outrigger States
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Solution Overview
Problem
Existing vehicle control systems lack the ability to effectively respond to various external conditions and objects, such as cranes, lifts, outriggers, pumps, pipes, and other vehicles, leading to potential collisions or unsafe operations.
Innovation Solution
Implementing image sensor-based systems that analyze captured images to detect and determine the state of these external conditions or objects, allowing vehicles to initiate appropriate actions or withhold actions based on their states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If image sensor-based systems are implemented to detect and analyze external conditions, then vehicle safety and situational awareness are improved, but device complexity increases
Solution Approach 1:
The system employs a multi-functional apparatus that integrates image capture, audio recording, and machine learning-based analysis capabilities within a single vehicle-mounted device. This universal system can detect and respond to multiple types of external conditions (cranes, lifts, outriggers, pumps, pipes, and other vehicles) using the same hardware platform, thereby improving safety without proportionally increasing complexity through separate dedicated systems for each detection task.
2Measurement precision
If the system analyzes multiple image parameters to determine object states, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary actions by continuously capturing images and audio data, pre-processing this data through machine learning models to identify potential external conditions before they become critical hazards. The apparatus maintains ready-state detection capabilities, so when a crane, lift, or other vehicle is detected, the system has already begun analyzing the situation, reducing the time required for accurate state determination and response initiation.
Solution Approach 2:
The system replaces traditional mechanical or manual detection methods with machine learning-based image and audio analysis. Instead of requiring manual inspection or simple sensor thresholds, the system uses trained machine learning models to automatically identify and classify external conditions, achieving high measurement precision through algorithmic pattern recognition rather than time-consuming manual assessment.
3Productivity
If the vehicle initiates responsive actions based on detected states, then productivity is improved, but object-affected harmful factors increase
Solution Approach 1:
The system implements continuous feedback loops where detected external conditions (cranes, lifts, outriggers, pumps, pipes, or vehicles) trigger responsive actions, which are then monitored to ensure safety. The apparatus provides feedback to the vehicle operator or autonomous control system about the detected state and recommended actions, allowing for real-time adjustments that improve operational efficiency while minimizing collision risks through ongoing monitoring and correction.
Data Source
AI summary
Systems, methods and non-transitory computer readable media for controlling vehicles in response to outriggers are provided. In some examples, images captured using image sensors from an environment of a first vehicle may be obtained. The images may be analyzed to detect a second vehicle. The images may be analyzed to determine that the second vehicle is connected to an outrigger. The images may be analyzed to determine a state of the outrigger. The first vehicle may be caused to initiate an action responding to the second vehicle based on the determined state of the outrigger. For example, in response to a first determined state of the outrigger, the first vehicle may be caused to initiate the action responding to the second vehicle, and in response to a second determined state of the outrigger, causing the first vehicle to initiate the action may be avoided.