AI System for Checking Driver and Vehicle Document Validity

The problem

An outdated document version may be treated as current in day-to-day operations. When driver and vehicle identity, document type, date, verification status and work schedule data are scattered across sensor, document, site or team records, the issue may not be identified in time.

This fragmented data delays preparation of the document-to-assignment review list and forces teams to search for supporting evidence again by hand. Before making a decision, the fleet compliance officer must complete missing records and verify each inconsistency individually.

A common exception is that a renewal application or temporary document may require a separate assessment. Incomplete or incorrect automation that does not check this context could lead to an unsuitable assignment or the unnecessary cancellation of a valid assignment, so no physical or operational action can be taken directly.

What we set out to improve

• Number of document alerts confirmed by the compliance officer
• Proportion of outputs corrected or rejected by the fleet compliance officer
• Time between the source event and the review output

How the system works

1. 01 Define the review scope and the role of the authorised fleet compliance officer
2. 02 Retrieve the inputs: driver and vehicle identity, document type, date, verification status and work schedule
3. 03 Check the source identifier, date, calibration or version, and data recency
4. 04 Check the document identifier and version
5. 05 Compare the validity period with the assignment date
6. 06 Check the exception: a renewal application or temporary document may require a separate assessment
7. 07 Place low-confidence or inconsistent records in a separate specialist queue
8. 08 Write the source identifier, rule, output and time to the audit trail
9. 09 Have the fleet compliance officer approve, correct, defer or reject the output

Methods we used

• Document date and verification-status rules
• Source, format, time and required-field checks for driver and vehicle identity, document type, date, verification status and work schedule data
• A missing-data warning instead of an estimate when inputs are incomplete or inconsistent
• A separate exception check outside the main rule
• Joint retention of the source, rule result, document-to-assignment review list and human decision

Where people stay involved

Data and security

Driver and vehicle identity, document type, date, verification status and work schedule data are accessed with the narrowest permissions required for the task. Commercial, location and employee information is masked where possible, and only the necessary portion of the data is sent to the model rather than the complete raw dataset. Access, outputs and the fleet compliance officer's decision are recorded; production does not begin until the organisation approves the retention period and data location.

Who this suits

Frequently asked questions