Vehicle Interior Driver State Confidence via 3D Imaging and Neural Networks
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Solution Overview
Problem
Current systems for assessing driver awareness in vehicles are prone to errors and ambiguities due to limitations in sensors such as pressure sensors, steering wheel sensors, and cameras, which can fail to accurately distinguish between a person and objects or provide incomplete views of the driver's state.
Innovation Solution
A system utilizing a 3D imaging unit, such as a time-of-flight camera, combined with a convolutional neural network to generate confidence values for various states within the vehicle without requiring detailed models of people or vehicle interiors, and without the need for multiple sensors, allowing for accurate assessment of driver awareness and distraction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sensors (pressure sensors, steering wheel sensors, driver facing cameras) are used to assess driver awareness, then the system can detect driver presence and basic states, but the measurement precision and reliability are reduced due to inability to distinguish between persons and objects and ambiguous output
Solution Approach 1:
The patent combines multiple sensor types (pressure sensors, steering wheel sensors, driver facing cameras, and 3D imaging sensors) into a fused sensor system. This merging allows the system to cross-validate signals and distinguish between actual driver states and false conditions (such as objects on the steering wheel), thereby improving both measurement precision and reliability simultaneously.
Solution Approach 2:
The patent introduces 3D imaging sensors as an intermediary component that provides depth information and spatial context to disambiguate sensor readings. This intermediary sensor type helps resolve ambiguities from other sensors by providing geometric verification of driver presence and state, improving the reliability of the overall assessment system.
2Measurement precision
If 3D imaging sensors and convolutional neural networks are used to improve driver state assessment accuracy, then measurement precision increases, but device complexity and processing power requirements increase
Solution Approach 1:
The patent segments the driver monitoring task into distinct functional components: 3D imaging for spatial mapping, pressure sensing for contact detection, steering wheel sensing for hand position, and camera analysis for facial expressions. Each sensor type processes specific aspects of driver state, and the convolutional neural network integrates these segmented inputs. This segmentation allows the use of simpler, specialized sensors rather than one complex system, reducing overall device complexity while maintaining high precision.
3Measurement precision
If multiple sensors are deployed to increase confidence level of driver state estimation, then measurement precision improves, but device complexity and cost increase
Solution Approach 1:
The patent designs the sensor system where each component serves multiple functions: the 3D imaging sensor provides both depth mapping for spatial awareness and direct driver presence detection; pressure sensors monitor both seat occupancy and driver posture; steering wheel sensors detect both hand presence and interaction patterns. This multi-functionality reduces the need for separate specialized sensors, decreasing device complexity while maintaining high measurement precision through sensor fusion.
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 system provides high-accuracy confidence values for driver states, enabling effective monitoring of driver awareness and distraction without the need for extensive processing power or multiple sensors, thereby enhancing safety by accurately determining the presence and actions of drivers within the vehicle.
Implementation Method 1
Automotive grade time-of-flight camera sensors monitoring the interior of a vehicle
Data Source
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AI summary
A system for generating a confidence value for at least one state in the interior of a vehicle, comprising an imaging unit configured to capture at least one image of the interior of the vehicle, and a processing unit comprising a convolutional neural network, wherein the processing unit is configured to receive the at least one image from the imaging unit and to input the at least one image into the convolutional neural network, wherein the convolutional neural network is configured to generate a respective likelihood value for each of a plurality of states in the interior of the vehicle with the likelihood value for a respective state indicating the likelihood that the respective state is present in the interior of the vehicle, and wherein the processing unit is further configured to generate a confidence value for at least one of the plurality of states in the interior of the vehicle from the likelihood values generated by the convolutional neural network.