Multi-Sensor Cargo Loss Detection for Autonomous Vehicles
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Solution Overview
Problem
Autonomous vehicles lack effective systems to detect and report internal cargo loss, which can occur during loading, unloading, or transit due to malicious actions or improperly secured trailer doors, posing safety risks.
Innovation Solution
A system utilizing multiple sensors (infrared, magnetic, electrical contact, cameras, and location sensors) to monitor cargo conditions, determining a cargo loss event based on specific sequences of sensor data events, and transmitting a detection signal to an external receiver.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple sensors are used to detect cargo loss conditions, then detection reliability is improved, but device complexity increases
Solution Approach 1:
The detection system is divided into multiple independent sensor modules, each responsible for detecting specific cargo loss conditions (door status, cargo movement, weight changes). This segmentation allows reliable detection through multiple indicators while keeping each sensor module relatively simple and modular.
Solution Approach 2:
The sensor system is designed to detect multiple types of cargo loss conditions using a unified multi-sensor platform. The same sensor array can detect door openings, cargo displacement, and weight changes, making the system versatile without requiring separate dedicated systems for each detection type.
2Measurement precision
If sensor data processing is performed to determine cargo loss events, then detection accuracy is improved, but processing time increases
Solution Approach 1:
The system continuously monitors and pre-processes sensor data during normal operation, maintaining a ready state for rapid cargo loss detection. By keeping sensors active and data processing pipelines primed, the system can quickly determine cargo loss events without requiring extensive processing time when an event occurs.
Solution Approach 2:
The system uses feedback from multiple sensor inputs to continuously update the cargo loss detection state. When sensor data indicates a potential cargo loss condition, the system cross-references multiple sensor readings and previously established baseline data to rapidly confirm or rule out cargo loss events, balancing accuracy with speed.
Data Source
AI summary
A system for detecting and reporting cargo lost from a vehicle is disclosed. The system includes a plurality of sensors, and a cargo loss detection system including a processor and a memory device storing instructions that when executed by the processor configure the processor to: (i) receive, from the plurality of sensors, sensor data representing a plurality of cargo loss-related conditions; (ii) determine a cargo loss event has occurred based on a combination of more than one cargo loss-related conditions of the plurality of cargo loss-related conditions; and (iii) transmit a lost cargo detection signal to an external receiver.