Omnidirectional Vehicle Collision Detection Using Machine Learning
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Solution Overview
Problem
Traditional collision avoidance systems are limited to detecting potential collisions within the same lane, failing to address side impacts and other directional collisions, which are common accident types.
Innovation Solution
An omnidirectional collision avoidance system using machine learning to predict future vehicle trajectories and detect potential collisions in any direction, outputting warnings through an interface when the collision is imminent within a predetermined threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional collision avoidance systems are used, then the system complexity is low, but the detection coverage is limited to same-lane collisions only
Solution Approach 1:
The system transitions from traditional one-dimensional same-lane collision detection to omnidirectional detection by adding spatial dimensionality. The machine learning model processes multi-directional data (front, rear, left, right lanes) to predict collisions in three-dimensional space, enabling detection of side-impact and rear-end collisions that were previously undetected.
Solution Approach 2:
The patent replaces traditional mechanical sensor arrays with a machine learning-based computational system. The ML model processes driving data from multiple sources (position, speed, acceleration) to predict collision risk, substituting complex mechanical detection systems with an intelligent algorithmic approach that can handle omnidirectional data processing.
2Measurement precision
If machine learning model is applied for omnidirectional collision detection, then the collision detection accuracy is improved, but the computational resources required increase
Solution Approach 1:
The system applies machine learning selectively rather than universally. The ML model processes data from multiple directions (front, rear, left, right) but only when and where collision risk is suspected based on predefined thresholds and vehicle state conditions, avoiding unnecessary computational processing in safe situations while maintaining high accuracy when needed.
Data Source
AI summary
An example operation includes one or more of receiving driving data of a first vehicle, receiving driving data of a second vehicle driving in a different lane than the first vehicle, determining that the first vehicle and the second vehicle will collide via execution of a machine learning model on the driving data of the first and second vehicles, and displaying a warning on a user interface associated with one or more of the first and second vehicles.