In-Cabin Collision Event Classification Using Inertial Sensor AI
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Solution Overview
Problem
Existing systems struggle to accurately detect and classify vehicle collision events, including differentiating between collision and non-collision events, leading to inefficiencies in driver alerts and data aggregation for hazardous area identification.
Innovation Solution
Implementing feature extraction techniques and artificial intelligence models trained on inertial sensor data to classify vehicle events into collision and non-collision types, with sub-classification capabilities, using wavelet transforms and clustering to enhance detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If traditional radar systems with separate transmit and receive antennas are used, then signal transmission and reception can be performed, but the system complexity increases and the aperture is limited by physical separation requirements
Solution Approach 1:
The patent combines transmit and receive functions into a single antenna element, eliminating the need for separate transmit and receive antennas. This merging reduces system complexity while maintaining full-duplex operation capability through electronic isolation techniques.
Solution Approach 2:
Each antenna element serves multiple functions: it acts as both a transmit antenna and a receive antenna simultaneously. This multi-functionality increases the effective aperture without requiring additional physical antenna elements, thereby improving collision detection reliability while reducing system complexity.
2Productivity
If full-duplex MIMO operation is implemented, then spectral efficiency and productivity are improved, but self-interference from co-channel signals becomes a harmful factor
Solution Approach 1:
The patent converts the harmful self-interference into a manageable signal by using precise channel state information to calculate and apply cancellation weights. The self-interference, which initially degrades signal quality, is transformed into a predictable component that can be subtracted from the received signal, enabling full-duplex operation.
Solution Approach 2:
The patent introduces an interference cancellation mechanism as an intermediary between the transmit and receive paths. This intermediary processes the self-interference signal through channel estimation and weight calculation, then subtracts it from the received signal, allowing full-duplex MIMO operation to proceed without harmful interference.
3Measurement precision
If channel state information is accurately estimated, then interference cancellation precision is improved, but the time required for detection increases
Solution Approach 1:
The patent performs channel state information estimation and interference cancellation weight calculation in advance, before the actual collision detection is needed. This preliminary action allows the system to have accurate channel knowledge ready when detection is required, reducing the detection time while maintaining high precision.
Solution Approach 2:
The patent maintains continuous channel state information updates and interference cancellation operations, ensuring that accurate channel knowledge is always available. This continuous operation eliminates the need for repeated estimation during detection, reducing detection time while preserving measurement precision.
Data Source
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AI summary
Disclosed herein are systems and methods for detecting a vehicle collision. A computing device can determine a vehicle event based on inertial sensor data and speed from at least one sensor in a housing inside a cabin of a vehicle, and classify the vehicle event as a collision event or a non-collision event based on the inertial sensor data, the speed, and vehicle class data of the vehicle. The computing device can classify an event subclass of the collision event or the non-collision event based on the inertial sensor data, the speed, and the vehicle class data of the vehicle. The computing device can generate a notification based on the event subclass.