[TMP-134] XAI model for human readable data aimed at connected car crash detection

Contact person: Francesco Spinnato Riccardo Guidotti (francesco.spinnato@sns.it)

Internal Partners:

1. Generali Italia
2. CNR Pisa
3. Università di Pisa

For insurance business a connected car is a vehicle where an embedded telematics device streams acceleration data, GPS position and other physical parameters of the moving car. This live streaming is used for automatic real time detection of car crashes. The project is focused on the development of an XAI layer which translates the logical outcome of an underneath LSTM used for crash detection into a human readable format.

Results Summary

• Industrial outcome: the LSTM automatic labeling of a signal event from a car telematics box as a ‘crash’ triggers an emergency live call from a contact center to the driver’s phone for health assessment and further help. If the driver is not responding or is out of reach, more actions could follow (e.g. call to emergency service). In order to improve the efficiency of this emergency procedure, is vital for the contact center operator to reduce the number of false positive events (e.g. being able to read the outcome of the box and discriminate a false positive event)
• Societal outcome: an improved efficiency in connected car crash detection (reduction of false positives) can reduce the number of car crashes with fatal or severe injury outcome and also improve road safety.

Tangible Outcomes

1.  Explaining Crash Predictions on Multivariate Time Series Data  The Lecture Notes in Computer Science book series (LNAI,volume 13601) https://link.springer.com/chapter/10.1007/978-3-031-18840-4_39