Vehicle Context Risk Assessment for Occupant and Cargo Safety
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Solution Overview
Problem
Existing vehicle systems lack the capability to effectively predict and minimize risks associated with various usage contexts, including injuries to occupants and cargo, as well as risks to other vehicles, due to insufficient analysis of sensory feedback and environmental factors.
Innovation Solution
A vehicle safety management system that utilizes onboard sensors, machine learning, and artificial intelligence to analyze occupant and cargo positions, environmental conditions, and historical data to assess risks, generate alerts, and adjust vehicle operations to mitigate potential hazards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If vehicle systems use basic sensor detection without advanced analysis, then device complexity is reduced, but risk prediction capability and safety are insufficient
Solution Approach 1:
The system performs preliminary risk assessment by analyzing sensory feedback and environmental factors before actual incidents occur. Machine learning models pre-process sensor data to predict potential risks, allowing the system to prepare mitigation strategies in advance rather than reacting after problems arise.
Solution Approach 2:
The patent introduces machine learning algorithms and AI processors as intermediary components between basic sensors and safety decisions. These intermediaries transform raw sensor data into meaningful risk assessments, enabling complex predictions without requiring direct complex mechanical or electronic control systems.
2Measurement precision
If the system comprehensively analyzes sensory feedback and environmental factors, then measurement precision and risk assessment accuracy improve, but loss of information processing time and computational load increase
Solution Approach 1:
The system continuously pre-processes sensor data in the background even when no immediate risk is detected, building contextual understanding and identifying patterns over time. This allows the system to rapidly assess risks when critical events occur without needing to process all data from scratch.
Solution Approach 2:
The patent implements selective analysis where the system focuses computational resources on the most critical sensors and factors based on current vehicle context. When certain risk conditions are detected, the system intensifies analysis of relevant data streams while reducing processing of less critical information, balancing accuracy with processing efficiency.
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.