Vehicle Usage Context Risk Prediction for Occupants and Cargo
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Solution Overview
Problem
Existing technologies lack effective methods to predict and minimize risks associated with various vehicle usage contexts, including injuries to occupants and cargo, as well as risks to other vehicles and their occupants.
Innovation Solution
A vehicle safety management system that utilizes onboard sensors to analyze sensory feedback, perform physics analyses, and employ machine learning and artificial intelligence to predict potential injuries and risks, and subsequently alert occupants and modify vehicle operations to minimize these risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If onboard sensors and AI analysis are deployed to predict potential injuries and risks, then safety prediction capability is improved, but device complexity increases
Solution Approach 1:
The system segments the complex safety analysis into distinct functional modules: sensor data acquisition, physics-based injury risk analysis, machine learning risk prediction, and alert generation. Each module handles a specific aspect of risk assessment, making the overall complex system manageable and maintainable while achieving comprehensive safety prediction
Solution Approach 2:
The patent introduces an intermediary processing layer that bridges raw sensor data and final risk predictions. This intermediary layer performs physics-based analysis and machine learning computations, transforming complex sensor inputs into interpretable risk assessments without requiring direct complex interactions between all system components
2Measurement precision
If real-time sensory feedback analysis is performed to predict injuries, then measurement precision is improved, but use of energy increases
Solution Approach 1:
The system performs preliminary physics-based analysis on sensor data to identify high-risk scenarios before executing more computationally intensive machine learning models. This preliminary filtering reduces the amount of data requiring full AI processing, thereby maintaining measurement precision while reducing overall energy consumption
Solution Approach 2:
The patent applies different levels of analysis intensity based on the detected risk level. For low-risk scenarios, simplified physics-based analysis suffices, while excessive full AI processing is reserved for high-risk situations requiring precise measurement, thus optimizing energy usage across varying operational conditions
Data Source
AI summary
Techniques are described that facilitate predicting and minimizing risks associated with vehicle usage contexts. In one example, a method comprises obtaining, by a system comprising a processor, vehicle state information identifying occupants and objects of a vehicle and relative positions of the occupants and the objects about the vehicle in association with a current usage context of the vehicle. The method further comprises determining, by the system, an assessment of potential injuries associated with the current usage context of the vehicle based on analysis of the vehicle state information, wherein the analysis comprises evaluating potential movement of the occupants and the objects. The method further comprises determining, by the system, risk information regarding a risk associated with the current usage context of the vehicle as a function of the assessment.