Autonomous Vehicle Occupant Sensor System for Unfamiliar Scenarios
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Solution Overview
Problem
Modern autonomous vehicles may encounter unfamiliar driving scenarios where they fail to properly identify relevant conditions, such as road markings or obstacles, due to limited training data, leading to inadequate decision-making.
Innovation Solution
An occupant sensor system that collects physiological data from vehicle occupants, including body position, gaze direction, and neural activity, to determine cognitive and emotional loads, and adjusts vehicle navigation based on these inputs, allowing the vehicle to respond to events outside its training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the autonomous vehicle relies solely on its own sensor data and training models, then the system complexity remains manageable, but the vehicle fails to properly identify relevant conditions in unfamiliar driving scenarios
Solution Approach 1:
The patent combines the autonomous vehicle's sensor system with the occupant's sensory system (visual, auditory, physiological) to create a hybrid perception system. This merging allows the vehicle to access the occupant's ability to recognize familiar patterns and conditions, compensating for the vehicle's limited training data in unfamiliar scenarios.
Solution Approach 2:
The occupant acts as an intermediary between the external environment and the vehicle's decision-making system. The occupant's sensory inputs and physiological responses serve as mediators that bridge the gap between unfamiliar external conditions and the vehicle's processing capabilities, enabling recognition of relevant conditions that the vehicle alone might miss.
2Adaptability or versatility
If the autonomous vehicle integrates occupant sensor data and physiological monitoring, then the vehicle can respond to events outside its training data, but the device complexity increases
Solution Approach 1:
The sensor array serves multiple functions: it monitors the external driving environment, tracks the occupant's physiological state, detects cognitive load levels, and identifies emotional responses. This multi-functionality reduces the need for separate specialized systems, managing complexity while enhancing adaptability.
Solution Approach 2:
The system nests multiple monitoring layers within a unified processing framework: physiological sensors are integrated within the cabin environment, which itself is nested within the vehicle's overall sensor network. The processing system nests occupant data analysis within the broader autonomous driving decision-making architecture, creating a hierarchical structure that manages complexity.
3Reliability
If the autonomous vehicle continuously monitors occupant physiological data and behavioral changes, then the vehicle can detect relevant events, but the processing energy consumption increases
Solution Approach 1:
The system applies partial monitoring by focusing computational resources on detecting specific physiological indicators of cognitive load and relevant behavioral changes rather than continuously processing all sensor data at maximum resolution. This selective approach maintains detection accuracy for critical events while reducing overall processing energy consumption.
Solution Approach 2:
The processing system skips detailed analysis of physiological data when the occupant's state is stable and within normal ranges, rapidly processing only through critical thresholds. When physiological indicators suggest potential relevance (such as sudden changes in cognitive load or attention), the system then performs more thorough analysis, rushing through routine states and focusing energy on potentially significant events.
Data Source
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AI summary
An occupant sensor system is configured to collect physiological data associated with occupants of a vehicle and then use that data to generate driving decisions. The occupant sensor system includes physiologic sensors and processing systems configured to estimate the cognitive and/or emotional load on the vehicle occupants at any given time. When the cognitive and/or emotional load of a given occupant meets specific criteria, the occupant sensor system generates modifications to the navigation of the vehicle. In this manner, under circumstances where a human occupant of an autonomous vehicle recognizes specific events or attributes of the environment with which the autonomous vehicle maybe unfamiliar, the autonomous vehicle is nonetheless capable of making driving decisions based on those events and/or attributes.