Autonomous Delivery Vehicle Theft Detection Using Behavior Analysis
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Solution Overview
Problem
Autonomous driving vehicles (ADVs) used for goods delivery are prone to theft due to lack of effective differentiation between human obstacles and potential thieves, leading to unnecessary alarms and burdens on responders.
Innovation Solution
A theft proofing system that utilizes sensors and a theft detection module to analyze real-time sensor data, comparing it against historical behavior datasets to determine the intention of individuals approaching the vehicle, sending alarms only when a potential theft is detected, and using machine learning to optimize the detection process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If alarms are sent for all persons approaching the ADV, then security coverage is improved, but false alarms increase and burden responders
Solution Approach 1:
The system changes the parameter of detection sensitivity by using behavior analysis to distinguish between benign and malicious approaches. Instead of triggering alarms for all persons, the system analyzes movement patterns,停留 duration, and interaction behaviors to dynamically adjust when alarms are triggered, thereby reducing false alarms while maintaining security coverage
Solution Approach 2:
The system introduces behavior analysis as an intermediary between detecting a person and triggering an alarm. The theft detection module acts as a mediator that processes sensor data, evaluates behavior patterns, and only triggers alarms when theft intent is detected, filtering out benign cases before alarm generation
2Object-generated harmful factors
If behavior analysis is implemented to distinguish thieves from obstacles, then false alarms are reduced, but system complexity increases
Solution Approach 1:
The sensor system serves multiple functions: it detects persons, tracks movement, analyzes behavior patterns, and triggers alarms. By making the existing sensor system multi-functional rather than adding separate dedicated systems for each function, the patent reduces overall system complexity while still implementing sophisticated behavior analysis
Solution Approach 2:
The system uses its own sensor data and processing capabilities to perform behavior analysis independently. The theft detection module leverages the existing sensor network and data processing infrastructure to analyze behavior patterns without requiring external complex systems, making the system self-sufficient and reducing overall complexity
Data Source
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AI summary
Various techniques for theft proofing autonomous driving vehicles (ADV) for transporting goods are described. Sensor data of a moving object representing a person within a predetermined proximity of an ADV for transporting goods (511) are captured for real-time analysis by a theft detection module, to determine a moving behavior of the moving object based on the sensor data in view of a set of known moving behaviors (513). The theft detection module further determines whether an intention of the person is likely to remove at least some of the goods from the ADV based on the moving behavior (515) using a process derived from historical image set, and sends an alarm to a predetermined destination in response to determining such an intention of the person. Other sensor data, for example, real time movements and weights of the ADV, can be used in conjunction with the process derived from historical image sets to determine the intention of the person.