Autonomous Vehicle Compartment Configuration via Occupancy Data
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Solution Overview
Problem
Autonomous vehicles face challenges in dynamically adapting their compartment configurations to accommodate varying loads and user demands, leading to inefficient space utilization and potential safety issues.
Innovation Solution
A computer-implemented method that receives occupancy data and compartment data to determine optimal compartment configurations, generating configuration signals to control the spatial relations of compartment components, allowing for dynamic reconfiguration of vehicle compartments based on the state of objects and compartments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If compartment configurations are fixed, then device complexity is reduced, but adaptability to varying loads and user demands deteriorates
Solution Approach 1:
The patent applies the dynamics principle by making compartment configurations changeable rather than fixed. The system dynamically adjusts compartment layouts, seat positions, and cargo space allocations based on real-time occupancy data and detected object states, allowing the vehicle interior to adapt flexibly to varying loads and user demands while maintaining manageable complexity through automated control.
Solution Approach 2:
The patent implements universality by designing compartment components that can serve multiple functions. Seats can be reconfigured or removed to create different cargo configurations, and compartment walls can be adjusted to serve both passenger containment and cargo storage purposes, allowing a single vehicle configuration to handle diverse transportation needs.
2Productivity
If compartment configurations are dynamically adjusted, then space utilization is improved, but device complexity increases
Solution Approach 1:
The system applies self-service by using sensors and computing devices to automatically detect object states, determine optimal compartment configurations, and execute reconfiguration without human intervention. The vehicle's control system autonomously processes occupancy data and adjusts compartment settings based on detected conditions, maximizing space utilization while keeping the control architecture manageable through automated decision-making.
Solution Approach 2:
The patent implements feedback by continuously monitoring occupancy data and object states within compartments, then using this information to dynamically adjust compartment configurations. The system receives feedback from sensors about cargo positions and passenger occupancy, processes this data to determine optimal arrangements, and executes reconfiguration to maximize space utilization efficiently.
3Productivity
If compartment configurations are optimized for cargo, then productivity is improved, but safety may deteriorate due to potential cargo shifting
Solution Approach 1:
The patent applies preliminary action by detecting the state of cargo objects and predicting potential shifting issues before they occur. The system proactively configures compartment arrangements, barrier positions, and support structures based on detected object characteristics and anticipated vehicle movements, preventing cargo shifting before it happens rather than reacting to it after the fact.
Solution Approach 2:
The system implements preliminary anti-action by using detected object state information to pre-position barriers, adjust compartment configurations, and create stabilizing arrangements that counteract potential cargo shifting forces. The control system anticipates problematic configurations and takes corrective action in advance to maintain cargo stability during transit.
Data Source
AI summary
Systems, methods, tangible, non-transitory computer-readable media, and devices for configuration of a vehicle compartment are provided. For example, a method can include receiving, by a computing system, occupancy data based in part on one or more states of one or more objects. Based in part on the occupancy data and compartment data, a compartment configuration can be determined for one or more compartments of an autonomous vehicle. The compartment data can be based in part on a state of the one or more compartments. The compartment configuration can specify one or more spatial relations of one or more compartment components associated with the one or more compartments. One or more configuration signals can be generated based in part on the compartment configuration to control the one or more compartments of the autonomous vehicle.