Inspiration
Traffic crashes continue to occur during everyday driving, while law enforcement and city staff spend significant time manually reviewing automated traffic enforcement data. We wanted to build a system that improves safety proactively while reducing this operational burden.
What it does
SafeDriverAI analyzes traffic and location data to identify high-risk driving behavior, prioritize the most concerning events, and streamline human review—helping prevent crashes and reduce manual enforcement workload.
How we built it
We built a web-based platform using browser geolocation, traffic camera data, and AI-driven risk scoring to automatically triage events and generate concise summaries for reviewers.
Challenges we ran into
Balancing accuracy with privacy, handling noisy location data, and distinguishing genuinely dangerous behavior from legitimate driving maneuvers were key technical challenges.
Accomplishments that we’re proud of
We designed a scalable, human-in-the-loop system that demonstrates how automation can reduce enforcement workload without removing accountability or fairness.
What we learned
Effective safety systems must be context-aware, transparent, and designed to support—not replace—human decision-making.
What’s next for SafeDriverAI
Next steps include pilot testing with real-world datasets, refining risk models, and exploring incentive-based partnerships with insurers to encourage long-term safe driving behavior.
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