Swedish technology firms Smart Eye and Greater Than have released initial findings from their collaborative research into the impact of Advanced Driver Distraction Warning (ADDW) alerts on driver risk.
Their study, which analysed real-world data from fleet drivers using Smart Eye’s AI-driven driver monitoring system, confirms that ADDW alerts have a measurable positive effect on reducing crash risk.
The research found that ADDW alerts can immediately lower crash risk by around 10%, though this effect diminishes within two minutes, returning to pre-alert levels. Among the different types of warnings issued, alerts targeting drowsy driving had the most significant impact on overall crash risk. The study suggests that with further collaboration, ADDW systems could be refined to address additional high-risk driving behaviours beyond distraction.
Smart Eye CEO and founder Martin Krantz said, “Our work with Greater Than provides valuable real-world insights into how drivers respond to distraction alerts and how those alerts influence crash risk.
“With a deeper understanding, we can refine our systems to make alerts more effective. These findings help us take in-car safety technology further, making it better equipped to prevent accidents.”
Greater Than gathered and examined data from vehicles fitted with Smart Eye’s AI-based monitoring system, operated by professional fleet drivers over four weeks. Leveraging its AI-driven risk intelligence technology, Greater Than was able to assess crash probability and evaluate how ADDW alerts influenced driver safety.
For Smart Eye, the research offers insights into the effectiveness of current ADDW alerts in addressing various driving behaviours. This could lead to optimised alert mechanisms, including adjustments in frequency, volume, and type, while also supporting the potential development of alerts for other high-risk behaviours such as cognitive distraction and stress.
Greater Than CEO Liselott Johansson said, “The insights our analysis uncovered were fascinating, providing a comprehensive overview of driver risk level, number of alerts per driver, alert categories, and – most importantly – to what degree each alert influenced risk level and for how long. This analysis will advance the adaptiveness and effectiveness of important, lifesaving in-vehicle technologies.”
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