Data Exploitation

Impacts

Using Patterns of Life For Driver Identification

We have been developing our capability in machine learning methods and in this project we were engaged to look at the problem of detecting anomalous driver behaviour in a security context where only a limited time frame of vehicle tracking data was available. Having looked at the various techniques that could be applied, including Deep Belief Networks (DBN), it was apparent that these multi-level complex networks could not learn each driver’s pattern of life from so little data. We eventually settled on using a Restricted Boltzmann Machine (RBM) per driver as they could be trained quickly and generalised well.

The trained RBMs were then used to detect anomalous events within the routes and stops made by drivers. The anomalies detected could then provide information into the human operator’s decision process as to whether the event was a safety or security issue. For example, the detection of an anomalous stop may indicate vehicle breakdown or high-jacking, the detection of two vehicles stopping together in an unusual place may indicate swapping of goods, the detection of an anomalous turn may be a driver mistake that takes them into an insecure area. With more available data the system could be extended to learn more subtle features within the driver’s pattern of life. Thus alerts for more types of anomalies could be provided.

This work showed that machine learning can fulfil an important support role to human operators within a relatively short time from deployment by alerting them to unusual events of significance. The operator then makes the decisions about potential consequences of the event and any actions that should be taken. Thus a co-operative working between human and machine is enabled with each carrying out the tasks for which they are best equipped.

The Benefits of Using Data in IoT

The Benefits of Using Data in IoT

Contact Us – Email: hello@plextek.com or call: +44 (0) 1799 533 200

Using Patterns of Life For Driver Identification

We have been developing our capability in machine learning methods and in this project we were engaged to look at the problem of detecting anomalous driver behaviour in a security context where only a limited time frame of vehicle tracking data was available. Having looked at the various techniques that could be applied, including Deep Belief Networks (DBN), it was apparent that these multi-level complex networks could not learn each driver’s pattern of life from so little data. We eventually settled on using a Restricted Boltzmann Machine (RBM) per driver as they could be trained quickly and generalised well.

The trained RBMs were then used to detect anomalous events within the routes and stops made by drivers. The anomalies detected could then provide information into the human operator’s decision process as to whether the event was a safety or security issue. For example, the detection of an anomalous stop may indicate vehicle breakdown or high-jacking, the detection of two vehicles stopping together in an unusual place may indicate swapping of goods, the detection of an anomalous turn may be a driver mistake that takes them into an insecure area. With more available data the system could be extended to learn more subtle features within the driver’s pattern of life. Thus alerts for more types of anomalies could be provided.

This work showed that machine learning can fulfil an important support role to human operators within a relatively short time from deployment by alerting them to unusual events of significance. The operator then makes the decisions about potential consequences of the event and any actions that should be taken. Thus a co-operative working between human and machine is enabled with each carrying out the tasks for which they are best equipped.

The Benefits of Using Data in IoT

The Benefits of Using Data in IoT

Contact Us – Email: hello@plextek.com or call: +44 (0) 1799 533 200

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