Vehicle Vision Collision Severity Estimation for Airbag Control
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Solution Overview
Problem
Current vehicle safety systems rely on mechanical sensors that are often inaccurate and require extensive cable harnesses, leading to delayed and potentially incorrect airbag deployment, especially in complex collisions where kinetic energy estimation is crucial for assessing accident severity.
Innovation Solution
A vehicle vision system utilizing cameras and AI-powered perception sensors to estimate kinetic energy by classifying objects and calculating potential collision severity, correlating with mechanical sensor data to improve damage estimation and airbag deployment accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If mechanical sensors are used for collision detection, then the system can detect collisions, but the accuracy is poor and deployment is delayed
Solution Approach 1:
The patent replaces mechanical collision sensors with a vision-based system using cameras and AI processing. The camera captures images of the collision scene, and AI algorithms analyze the images to estimate collision severity, object mass, and kinetic energy, providing more accurate and timely detection without the limitations of mechanical sensors and their extensive cable harnesses.
2Reliability
If extensive cable harnesses are used for mechanical sensors, then sensors can be connected, but the system complexity increases
Solution Approach 1:
The vision-based system using wireless or integrated camera modules eliminates the need for extensive mechanical cable harnesses required by traditional mechanical sensors. The camera and processing unit can be integrated into the vehicle's existing electronic architecture, significantly reducing wiring complexity while maintaining reliable data transmission.
3Measurement precision
If kinetic energy estimation is implemented using vision system, then collision severity assessment improves, but processing complexity increases
Solution Approach 1:
The system performs preliminary classification of detected objects into predefined categories (pedestrian, cyclist, motor vehicle, animal, etc.) with associated mass ranges. This preliminary action allows the AI to select appropriate calculation models and parameters for kinetic energy estimation, reducing the complexity of real-time processing while maintaining accuracy.
Solution Approach 2:
The system changes parameters dynamically based on object classification. Different object types have different assumed mass ranges and kinetic energy calculation models. The AI adjusts these parameters in real-time based on the classification result, enabling accurate kinetic energy estimation without requiring complex universal models for all possible objects.
Data Source
AI summary
A vehicular safety feature control system includes a camera disposed at a vehicle that views exterior of the equipped vehicle. The vehicular safety feature control system includes an electronic control unit (ECU) for processing image data captured by the camera to detect presence of objects. The ECU, responsive to processing of image data captured by the camera, detects an object on a collision course with the equipped vehicle. The ECU, responsive to detecting the object on the collision course with the equipped vehicle, classifies the detected object. The ECU estimates a mass of the detected object based on the classification. The ECU estimates a kinetic energy of a potential collision and, based on the estimated kinetic energy of the potential collision, estimates a severity of the potential collision. The ECU controls a safety feature of the equipped vehicle based on the estimated severity of the potential collision.