UWB Vehicle State Sensing for Secure In-Cabin Detection
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Solution Overview
Problem
Traditional LF and UHF RF technologies used in passive entry systems and keyless entry systems are inadequate for detecting users within a vehicle and are vulnerable to security breaches like 'relay' attacks, necessitating a more robust and secure system.
Innovation Solution
An ultra-wideband (UWB) sensing system that uses a network of nodes around a vehicle to determine vehicle states, such as user presence or door positions, by receiving and processing UWB signals, extracting features from channel impulse responses, and applying machine learning algorithms to identify vehicle states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional LF and UHF RF technologies are used for passive entry systems, then keyless entry functionality is achieved, but the system is vulnerable to security breaches like relay attacks and cannot detect users within a vehicle
Solution Approach 1:
The patent transitions from traditional LF/UHF frequency bands to ultra-wideband (UWB) frequency band, fundamentally changing the operational parameters of the RF system. This parameter change enables both enhanced security features and new detection capabilities that were not available with conventional RF technologies
Solution Approach 2:
The UWB system performs multiple functions simultaneously: it provides secure keyless entry authentication and also enables detection of user presence within the vehicle. This multi-functionality resolves the contradiction by making the system both secure and adaptable without requiring separate systems
2Measurement precision
If UWB signals are used to determine vehicle states, then context awareness and detection accuracy are improved, but system complexity increases due to signal processing requirements
Solution Approach 1:
The patent extracts specific features from the complex UWB channel impulse responses, focusing only on the most relevant characteristics for vehicle state detection. This feature extraction process simplifies the data while preserving the essential information needed for accurate detection
Solution Approach 2:
The patent introduces machine learning algorithms as an intermediary between the raw UWB signals and the final vehicle state determination. These algorithms automatically process the complex signal data and translate it into meaningful vehicle state information, reducing the burden on the overall system architecture
3Reliability
If multiple receiving nodes are deployed around the vehicle, then detection reliability and coverage are improved, but device complexity and hardware requirements increase
Solution Approach 1:
The patent combines the data from multiple receiving nodes through signal processing and fusion techniques, creating a unified view of the vehicle environment. This merging approach maintains the reliability benefits of multiple nodes while managing the complexity through integrated processing rather than separate independent systems
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The UWB system provides enhanced context awareness, safety, and security by accurately determining vehicle states without the need for additional hardware, while also improving security by mitigating risks associated with traditional RF technologies.
Implementation Method 1
receiving a UWB signal at a plurality of receiving nodes
Implementation Method 2
using a Fast Fourier Transformation (FFT) algorithm to upsample the plurality of CIRs. The FFT algorithm may operate on the time domain of the plurality of CIRs
Data Source
AI summary
A system and method is disclosed for determining a particular vehicle state based on a UWB signal received at a plurality of receiving nodes. A plurality of channel-impulse responses (CIRs) may be computed from the UWB signal received from the plurality of receiving nodes. A plurality of peak-based features based on a selected position and amplitude may be extracted from the plurality of CIRs. A plurality of correlation-based features may be generated by correlating the plurality of CIRs to a corpus of reference CIRs relating to a plurality of vehicle states. A plurality of maximum likelihood vehicle matrices may be generated by correlating the plurality of CIRs to the corpus of reference CIRs relating to the plurality of vehicle states. The vehicle state may then be determined by processing the plurality of peak-based features and correlation-based features using the machine learning classification algorithm.