Autonomous Vehicle Compartment Reconfiguration for Load Adaptation
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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 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 compartment configuration system transitions from static to dynamic by enabling real-time reconfiguration based on detected occupancy and load conditions. The system continuously monitors compartment states and automatically adjusts partition positions to optimize space utilization for varying loads and user demands.
Solution Approach 2:
The compartment system performs self-configuration by automatically detecting its own state through sensors and adjusting partition positions without external intervention. The control system monitors occupancy data and autonomously determines optimal compartment arrangements, eliminating the need for manual configuration.
2Productivity
If compartment configurations are fixed, then device complexity is reduced, but space utilization efficiency deteriorates
Solution Approach 1:
The system dynamically adjusts compartment volumes and shapes in response to detected occupancy patterns and load distributions. By continuously monitoring sensor data and reconfiguring partitions, the system maximizes space utilization efficiency for different transportation scenarios.
Solution Approach 2:
The compartment configuration is changed by adjusting physical parameters such as partition positions, compartment volumes, and spatial relationships. The system varies these parameters based on detected occupancy data to optimize space utilization for different load conditions.
3Adaptability or versatility
If compartment configurations are dynamically adjusted, then adaptability to varying loads improves, but reliability for secure transportation deteriorates
Solution Approach 1:
The system implements closed-loop feedback by continuously monitoring compartment occupancy and load conditions through sensors, comparing detected states with desired configurations, and automatically adjusting partitions to maintain secure transportation. The feedback mechanism ensures that reconfiguration actions are based on actual real-time conditions.
Solution Approach 2:
The system performs preliminary detection and analysis of occupancy data before executing compartment reconfiguration. By assessing load conditions and user demands in advance, the system determines optimal configuration changes that maintain security and stability during transportation.
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.