mlautomotivehackathon

Road Entertainment System - Hack Dearborn Winner

ML-powered in-cabin recommendation system with 89% accuracy, winner of Hack Dearborn 2023 Automotive Track & ZF Challenge.

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The Problem

In-car entertainment systems lacked personalized, intelligent recommendations based on trip context and user preferences.

The Solution

Built ML-powered system using trip ETA, age detection, and user genres to suggest media, integrated with Google Maps API and hand gesture controls.

Impact & Results

89% recommendation accuracy with gesture controls for collaboration tools and volume adjustments

89% recommendation accuracy

Winner of Hack Dearborn 2023

Integrated with Google Maps API

Hand gesture controls for collaboration

Tech Stack

Machine LearningGoogle Maps APIGesture RecognitionPython

Links

Source Code

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