Implementation of an In-Vehicle Driver Monitoring Camera System Utilizing AI: A Million Miles Later and Lessons Learned

Abstract

Abstract Transportation incidents remain one of the most significant operational risks in oilfield service operations. Traditional in-vehicle monitoring systems (IVMS) have improved visibility into vehicle behavior and post-incident learning; however, they provide limited capability to measure driver-state behaviors such as distraction, drowsiness, fatigue, and cellphone usage before an incident occurs. This paper describes the phased implementation of an artificial-intelligence-enabled dual-facing driver monitoring system across a large oilfield transportation fleet and evaluates the resulting safety observations over approximately eighteen months of operation. The implementation followed a deliberate ‘Crawl-Walk-Run’ approach that included forward-facing monitoring, pilot testing, driver engagement, privacy and governance controls, coaching workflows, and fleetwide deployment. During the monitoring period, the fleet accumulated more than 12 million km of driving exposure, allowing event trends to be normalized to observations per 100,000 km driven. Normalization demonstrated that several targeted high-risk behaviors improved materially over the monitoring period, including cellphone usage, drowsiness, speeding, and stop sign violations. At the same time, distraction observations increased substantially on an exposure-normalized basis and became the dominant emerging driver-state trend requiring investigation. The results indicate that the greatest value of AI-enabled driver monitoring is not simply event recording or post-incident review. The system provided a practical method to measure driver-state behaviors, support coaching, and identify emerging risk trends before those trends were reflected in conventional lagging indicators such as motor vehicle accident rates. The paper highlights the importance of implementation strategy, workforce acceptance, data governance, and coaching maturity when deploying AI-enabled transportation safety technologies at scale.