Method and device for predicting vehicle events in a vehicle based on past vehicle events with the aid of machine learning

EP4751147A1Pending Publication Date: 2026-06-03ROBERT BOSCH GMBH
View PDF 0 Cites 0 Cited by

Patent Information

Authority / Receiving Office
EP · EP
Patent Type
Applications
Current Assignee / Owner
ROBERT BOSCH GMBH
Filing Date
2024-07-23
Publication Date
2026-06-03

AI Technical Summary

Technical Problem

Existing vehicle maintenance systems fail to effectively predict future error events based on historical vehicle events, as anomalies in vehicle parts or functions often go unnoticed, leading to potential future errors that are not identified during routine maintenance.

Method used

A computer-implemented procedure using a recurrent data-based prediction model, such as an LSTM, to analyze a time sequence of vehicle events, encode them into input vectors, and predict future events, including critical errors, by training on historical data to signal potential issues before they occur.

Benefits of technology

Enables the prediction of future critical vehicle events, allowing for proactive maintenance and reducing the likelihood of unexpected errors by analyzing past events and their temporal connections, thereby improving vehicle reliability and safety.

✦ Generated by Eureka AI based on patent content.

Smart Images

  • Figure EP2024070901_30012025_PF_FP_ABST
    Figure EP2024070901_30012025_PF_FP_ABST
Patent Text Reader

Abstract

The invention relates to a computer-implemented method for predicting a vehicle event that is likely to occur in a vehicle, said method comprising the following steps: - recording (S1) vehicle events (F1 ... Fn) in a temporal sequence during the service life of the vehicle (1); - based on the temporal sequence of vehicle events (F1 ... Fn), creating (S2) a temporal sequence of input vectors (E) in which the recorded vehicle events are encoded; - providing a recurrent data-based prediction model (31) that is trained to assign the temporal sequence of input vectors (E) to a vehicle event (Fn+1 ...) occurring in a next time step; - evaluating (S3, S4) the recurrent data-based prediction model (31) depending on the created sequence of input vectors (E) in order to determine a predefined number of vehicle events (Fn+1 ...) occurring during future time steps; and - signalling (S5) a critical event if one or more vehicle events in the number of vehicle events occurring during future time steps (Fn+1 ...) meet an event criterion.
Need to check novelty before this filing date? Find Prior Art