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Telecom / Data Science & AI/ML / 5G
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Solution Highlights

  • Integrated AI-powered video analytics (object detection, face recognition, OCR, behavior analysis, LPR).
  • Edge + centralized processing for real-time performance under private 5G network.
  • Connected Car control system: remote acceleration, braking, steering, lights, horn, and driver assistance via mobile/desktop app.
  • High-speed, low-latency 5G private network ensuring secure and reliable data transmission.
  • End-to-end solution combining computer vision, deep learning, ECU programming, and automotive protocols.
Solution Highlights Connected Car Under 5G Private Network
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Problem Statement

Traditional vehicle control and monitoring systems face major limitations:

  • High latency in communication networks makes remote driving and real-time safety features unreliable.
  • Lack of AI-powered analytics to detect incidents, analyze driver/passenger behavior, or recognize license plates and specific objects.
  • Fragmented architecture where video processing, vehicle control, and network communication are not fully integrated.
  • Increasing demand for safer, smarter, and more connected vehicles in industries such as logistics, public safety, and autonomous driving.
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Solutions

  • Built a Connected Car platform leveraging a private 5G network to enable ultra-low latency remote control and monitoring.
  • Developed AI computer vision modules for object detection, face recognition, age/gender detection, license plate recognition, OCR, and behavior analysis.
  • Implemented edge AI processing for instant decision-making and centralized data processing for long-term storage and analytics.
  • Designed a remote control app (mobile & Windows) allowing users to manage acceleration, braking, steering, lighting, horn, and driver assistance (e.g., Cruise Control).
  • Integrated ECU programming and automotive communication protocols (CAN, TCP/IP, USB, Ethernet) with AI inference engines (TensorFlow, PyTorch, TensorRT, OpenVINO). 
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Benefits

  • Ultra-low latency control: Private 5G reduces response time to milliseconds, ensuring real-time safety-critical operations.
  • Improved safety & awareness: AI modules proactively detect risks (objects, people, abnormal behaviors) and issue alerts.
  • Scalable architecture: Edge + cloud model supports multi-vehicle and multi-camera environments.
  • Enhanced user experience: Remote car management and driver assistance features increase comfort and convenience.
  • Business-ready integration: API-based design allows integration with logistics, public transport, and smart city ecosystems.
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Success

This project successfully demonstrated a proof-of-concept (PoC) Connected Car system under a 5G private network.

  • Achieved real-time remote vehicle control with <10ms latency.
  • Successfully tested video streaming and AI analytics from multiple cameras without frame loss.
  • Validated AI-driven safety features (license plate recognition, behavior analysis, face/age detection) in live scenarios.
  • The PoC proved the feasibility of combining AI, edge computing, and 5G for next-generation connected vehicles, opening opportunities in smart mobility, logistics, and autonomous driving research. 
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