Hybrid AI for Advanced Vehicle Event Detection

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Fleet management and transportation safety systems rely on accurate event detection to reduce risk. Traditional ML models struggled with nuanced, context-dependent events such as unsafe driving behaviors or pedestrian proximity. A hybrid AI approach was required.

Industry: Fleet Management & Transportation

Problem:

Traditional ML models lacked the contextual intelligence to accurately identify nuanced vehicular events, such as unsafe driving behaviors or pedestrian proximity. This resulted in low model accuracy and unreliable safety monitoring.

Solution:

Developed a hybrid AI solution combining YOLO-based object detection with a generative, multimodal model (Gemini). The system was capable of contextual reasoning, enabling it to reliably detect complex events that standard models missed.

Technology Stack:

  • Gemini (Vertex AI)

  • Cloud Functions

  • BigQuery

  • Cloud Storage

  • PyTorch

  • YOLO

Impact:

Boosted model accuracy from the low 80% range to above 90% across all event classes, dramatically improving reliability. This enhanced driver safety solutions and delivered a measurable competitive edge.

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