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Prerequisites and Dependencies

Prerequisites

  • Operating System

  • Platform

    • Intel® Core™ Ultra 7 265H + Ubuntu24.04 (2C+1L/4C+2L usecase)
    • Intel® Core™ i7-13700 and Intel® B580 Graphics + Ubuntu24.10 (8C+4L/12C+2L/12C4L usecase)

  • Intel® OpenVINO™ Toolkit

    • Version Type: 2025.2

  • Ensure that proxy settings are configured if target system is within proxy environment

    export http_proxy=<Your-Proxy>
export https_proxy=<Your-Proxy>
    sudo vim /etc/environment
# set proxy in /etc/environment
# http_proxy=<Your-Proxy>
# https_proxy=<Your-Proxy>

Modules

  • AI Inference Service:

    • Media Processing (Camera)
    • Radar Processing (mmWave Radar)
    • Lidar Processing
    • Sensor Fusion

  • Demo Application

AI Inference Service

AI Inference Service is based on the HVA pipeline framework. In this SW RI, it includes the functions of DL inference, radar signal processing, and data fusion.

AI Inference Service exposes both RESTful API and gRPC API to clients, so that a pipeline defined and requested by a client can be run within this service.

  • RESTful API: listens to port 50021

  • gRPC API: listens to port 50052

vim $PROJ_DIR/ai_inference/source/low_latency_server/AiInference.config
...
[HTTP]
address=0.0.0.0
RESTfulPort=50021
gRPCPort=50052

Demo Application

Demo-2C1L

Figure 1. Visualization of 2C+1L results

Currently we support four display types: media, lidar, media_lidar, media_fusion.

Make sure you meet the system requirements.

Install Dependencies and Build Project

  • install driver related libs

    Update kernel, install GPU driver.

    bash install_driver_related_libs.sh

    Note that this step may restart the machine several times. Please rerun this script after each restart until you see the output of All driver libs installed successfully.

  • install project related libs

    Install Boost, Spdlog, Thrift, MKL, OpenVINO, GRPC, Level Zero, oneVPL etc.

    bash install_project_related_libs.sh

  • set $PROJ_DIR

    cd metro-ai-suite/sensor-fusion-for-traffic-management
export PROJ_DIR=$PWD

  • build liblidar.so

    cd $PROJ_DIR/ai_inference/liblidar
rm liblidar.so
bash build.sh

  • prepare global radar configs in folder: /opt/datasets

    sudo ln -s $PROJ_DIR/ai_inference/deployment/datasets /opt/datasets

  • prepare models in folder: /opt/models

    sudo ln -s $PROJ_DIR/ai_inference/deployment/models /opt/models

  • build project

    bash -x build.sh