Nearby Vehicle Condition Detection From Transient Velocity Data
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Solution Overview
Problem
Existing vehicle trajectory prediction systems fail to timely detect abrupt changes due to operating conditions such as loss of traction or component failure, leading to inefficient and abrupt adjustments in vehicle travel paths.
Innovation Solution
A system utilizing transient velocity data from sensors to determine the operating conditions of nearby vehicles, predict their trajectories, and adjust the host vehicle's path accordingly, incorporating a neural network trained on transient velocity signatures for precise condition identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional trajectory detection methods are used, then the system is simple to implement, but the detection timing is delayed and cannot detect operating conditions early enough
Solution Approach 1:
The system performs preliminary detection of operating conditions by analyzing transient velocity data before actual trajectory changes occur. Sensors continuously monitor velocity variations and the neural network classifies operating conditions in advance, enabling early warning before the vehicle's trajectory visibly changes, thus reducing detection time delay
Solution Approach 2:
The patent replaces traditional mechanical trajectory detection methods with a sensor-based velocity measurement system combined with neural network classification. Instead of relying on visual or mechanical trajectory analysis, the system uses transient velocity data from sensors and processes it through a neural network to detect operating conditions, achieving earlier detection without excessive complexity
2Productivity
If traditional trajectory change detection is used, then the detection method is straightforward, but the trajectory adjustments become abrupt and inefficient
Solution Approach 1:
The system implements continuous feedback by monitoring transient velocity data and classifying operating conditions in real-time. The neural network processes velocity variations and provides feedback about the second vehicle's operating state, allowing the first vehicle's trajectory to be adjusted progressively based on detected conditions rather than reacting abruptly to visible trajectory changes
Solution Approach 2:
By detecting operating conditions before trajectory changes manifest, the system can prepare and execute trajectory adjustments in advance. This preliminary detection allows for smoother, more efficient trajectory modifications as the system can plan adjustments based on predicted vehicle behavior rather than reacting to already-visible changes
Data Source
AI summary
A system includes a processor programmed to: identify, based on data from first sensors included in a first vehicle, a second vehicle moving within a first threshold distance of a travel path of the first vehicle and within a second threshold distance of the first vehicle. The processor is further programmed to receive, from second sensors included in the first vehicle, transient velocity data of the second vehicle; and determine an operating condition of the second vehicle based on the transient velocity data.