Occupant Motor Response Prediction in Vehicle Accidents
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Solution Overview
Problem
Current vehicle and airline accident simulations fail to account for active motor responses of occupants, such as muscle contractions and defensive actions, which significantly influence injury outcomes, leading to inadequate injury prediction and compensation.
Innovation Solution
A system that uses a spinal reflex model to generate proprioceptive signals based on accident scenarios, a neuromuscular model to determine muscle contraction dynamics, and a musculoskeletal model to predict motor responses, which can initiate active vehicle compensation and adjust design parameters to minimize injuries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If passive computational models and anthropomorphic test devices are used to simulate accidents, then the simulation structure is simple and easy to implement, but they cannot model active defensive actions and muscle responses of occupants
Solution Approach 1:
The patent transforms static passive models into dynamic active models by incorporating real-time muscle activation states and motor responses. The system continuously updates occupant posture and muscle force based on detected accident scenarios, enabling the model to adapt and respond dynamically rather than remaining fixed and passive throughout the simulation.
Solution Approach 2:
The system performs preliminary detection of accident scenarios and pre-calculates appropriate muscle activation patterns and defensive actions before the actual impact occurs. This allows the simulation to incorporate preparatory motor responses that occupants would naturally execute in real accident situations, improving prediction accuracy.
2Productivity
If autonomous vehicles maneuver in evasive/emergency contexts without considering passenger injury potential, then the vehicle response time is fast and productivity is high, but passenger safety is compromised
Solution Approach 1:
The system implements a feedback loop where muscle response predictions and injury risk assessments are continuously fed back to the autonomous vehicle control system. This allows the vehicle to adjust its evasive maneuvers in real-time based on predicted passenger responses, optimizing both the speed of evasion and the safety outcomes by selecting maneuvers that minimize injury risk.
Solution Approach 2:
The system predicts harmful injury outcomes from various evasive maneuvers in advance and selects maneuvers that pre-emptively counteract these harmful effects. By calculating potential injury risks before executing evasive actions, the system chooses paths and maneuvers that naturally compensate for and prevent anticipated passenger injuries.
3Measurement precision
If current accident simulations model only passive structural elements, then the computational requirements are low and processing time is short, but they fail to capture kinematics that significantly influence injury outcome
Solution Approach 1:
The patent segments the complex musculoskeletal system into modular components (individual muscles, muscle groups, and skeletal elements) that can be simulated independently and then integrated. This segmentation allows for detailed kinematic measurements of specific muscle-bone interactions without requiring computationally expensive full-system simulations, reducing overall computation time while maintaining precision.
Data Source
AI summary
Described is a system for prediction and active compensation of occupant motor response in a vehicle accident. The system uses a spinal reflex model to generate a stimulus based on an accident scenario of an occupant in a vehicle, the stimulus being a set of proprioceptive signals induced by the accident scenario. A neuromuscular model then determines activation and contraction dynamics based on the stimulus. The activation and contraction dynamics represent muscle contraction forces spanning a skeletal system of the occupant. A musculoskeletal model then generates a predicted motor response of the occupant based on the activation and contraction dynamics. The predicted motor response can be used for a variety of purposes, such as initiating active compensation in a vehicle or modifying airline cabin design parameters to decrease the likelihood of injury to the occupant.