Get Started#

The Smart Traffic Intersection Agent (STIA) provides analytics that include real-time intersection, directional traffic density, and VLM-powered traffic, among others. This section shows you how to:

  • Set up the agent using the automated setup script for quick deployment.

  • Run predefined tasks to explore the agent’s functionality.

  • Edit configurations to suit specific requirements.

Prerequisites#

Before you begin, ensure the following:

  • System requirements: Verify that your system meets the minimum requirements.

  • Docker platform: Install Docker platform. For installation instructions, see Get Docker.

  • Message Queuing Telemetry Transport (MQTT) Broker: Ensure access to an MQTT broker for traffic data streaming, or use the included broker.

  • Docker commands and terminal usage: You are familiar with Docker commands and using the terminal. If you are new to Docker, see Docker Documentation for an introduction.

  • Hugging Face token: Set your token using export HUGGINGFACE_TOKEN="<your-huggingface-token>"

  • Registry configuration: To pull pre-built images from a specific registry, set the REGISTRY and TAG parameters. Following is the recommended default setting.

    export REGISTRY="intel"
export TAG="latest"

Quick Start with Setup Script#

Intel recommends using the automated setup script that handles environment configuration, submodule and dependencies setup, secrets generation, building, and deployment of the Smart Traffic Intersection Agent.

1. Clone the Repository#

git clone https://github.com/open-edge-platform/edge-ai-suites.git
cd metro-ai-suite/smart-traffic-intersection-agent/

2. Set the required environment variables#

export VLM_MODEL_NAME=<supported_model_name>  # eg. microsoft/Phi-3.5-vision-instruct, Qwen/Qwen2.5-VL-3B-Instruct

IMPORTANT: See this disclaimer before using any AI Model.

3. Run the Complete Setup#

The easiest way to set up the service is to use default configurations without making any changes. Run the setup script with the --setup option to set up the agent quickly with the default configurations:

source setup.sh --setup

This single command will:

  • Set required environment variables with default values

  • Set up dependencies and submodules required for Smart Traffic Intersection Agent

  • Generate the required TLS certificates and authentication files

  • Download demo video files for testing

  • Build Docker images

  • Start services in the Smart Traffic Intersection Agent’s application stack

4. Run alternative setup options#

For a more granular control, run these commands:

#  Set environment variables without building image or starting any containers
source setup.sh --setenv

# Build service images only (without starting containers)
source setup.sh --build

# Start services without building the image
source setup.sh --run

# Stop services
source setup.sh --stop

# Restart services. The variable `service_type` can be set to `agent`, `deps`, and `all`. Run with --help to get details of each type.
source setup.sh --restart [service_type]

# Clean up containers. Run with --help to get details of the option.
source setup.sh --clean [option]

5. Access Services#

When the script completes, it will show the URLs to access the services. Go to these URLs so that the respective services can access them in a web browser.

Running Multiple Instances (Test or Development Only)#

For testing or development purposes, you can run multiple instances of the Smart Traffic Intersection Agent to simulate multiple intersections on the same development machine or node. The easiest way to do this is to clone and set up the application n times in n different locations on the same machine for n required instances.

Note: In production environments, only a single Traffic Intersection Agent instance is deployed on a given node.

Intel’s recommendation: The number of instances you can run on a single machine depends on available resources. Systems with higher resources can support more instances.

Set up Instance #1#

  1. Clone the repository into a new directory:

    git clone --depth 1 https://github.com/open-edge-platform/edge-ai-suites.git edge-ai-suites-instance1
cd edge-ai-suites-instance1/metro-ai-suite/smart-traffic-intersection-agent/

  2. Edit the deployment configuration file for instance #1:

    nano src/config/deployment_instance.json

    Update name, latitude and longitude values as required. Following is a sample value for the Instance #1 deployment config:

    {
    "name": "intersection_1",
    "latitude": 37.5879818,
    "longitude": -122.0534334,
    "agent_backend_port": "8081",
    "agent_ui_port": "7860"
}

    TIPS: Leave agent_backend_port and agent_ui_port empty to avoid port conflicts. Random ports would be assigned and application URLs with assigned ports will be shown when setup finishes.

  3. Set the required environment variable.

    export VLM_MODEL_NAME=<supported_model_name>  # eg. microsoft/Phi-3.5-vision-instruct, Qwen/Qwen2.5-VL-3B-Instruct

    IMPORTANT: See this disclaimer before using any AI Model.

  4. Run the setup for instance #1:

    source setup.sh --setup

Set up Instance #2#

  1. Open a new terminal window and move to new directory. Clone the repository into the new directory:

    git clone --depth 1 https://github.com/open-edge-platform/edge-ai-suites.git edge-ai-suites-instance2
cd edge-ai-suites-instance2/metro-ai-suite/smart-traffic-intersection-agent/

  2. Edit the deployment configuration for instance #2:

    nano src/config/deployment_instance.json

    The following is a sample value for instance #2 deployment configuration:

    {
    "name": "intersection_2",
    "latitude": 37.33874,
    "longitude": -121.8852525,
    "agent_backend_port": "8082",
    "agent_ui_port": "7861"
}

    TIPS: Leave agent_backend_port and agent_ui_port empty to avoid port conflicts. Random ports would be assigned and application URLs with assigned ports will be shown when setup finishes.

  3. Set the required environment variable.

    export VLM_MODEL_NAME=<supported_model_name>  # eg. microsoft/Phi-3.5-vision-instruct, Qwen/Qwen2.5-VL-3B-Instruct

    IMPORTANT: See this disclaimer before using any AI Model.

  4. Run Setup for Instance #2

    source setup.sh --setup

Ensure each instance has their deployment_instance.json updated with:

  • A unique value for name field

  • Unique latitude and longitude co-ordinates

  • Different agent_backend_port and agent_ui_port values to avoid port conflicts. This is optional. If not specified, an ephemeral port is picked automatically.

Deploying More Instances#

There are functionally no limits on the number of instances that you can spin up to simulate a multi-node setup using the provided script. However, the machine on which the multiple deployments are deployed to, will likely start throttling these instances if resource limits are reached. Hence, deploy new instances only if you have the required resource bandwidth.

To spin-up more instances - say n number of new instances, repeat the steps mentioned in Set up Instance #2, by changing to a new directory n times.

Disclaimer for Using Third-Party AI Models#

Compliance with all license obligations and responsible use for a third-party AI Model is the user’s responsibility.

Advanced Environment Configuration#

For advanced users who need more control over the configuration, you can configure the following environment variables before running the setup script to override the default behaviour:

# Set log level to debug to help in debugging issues, default value is info
export LOG_LEVEL=DEBUG

# Select iGPU as the accelerator to perform VLM inference. By default, it is set to CPU 
export VLM_DEVICE=GPU

# Other VLM related config, sample values
export VLM_TIMEOUT_SECONDS=600          # Default 300
export VLM_MAX_COMPLETION_TOKENS=1000   # Default 1500
export VLM_TEMPERATURE=0.4              # Default 0.1, range 0-1; controls randomness of response
export VLM_TOP_P=0.3                    # Default 0.1, range 0-1; another parameter to control randomness and diversity of response

# Some sample values for Traffic Intersection configuration
export HIGH_DENSITY_THRESHOLD=5        # Default value 10
export MODERATE_DENSITY_THRESHOLD=3    # Default value 5; Make sure this is less than HIGH_DENSITY_THRESHOLD
export TRAFFIC_BUFFER_DURATION=20      # Default value 30; Analysis window of traffic feeds in seconds

# To mock the weather data (say in airgapped deployment)
export WEATHER_MOCK=True

Customizing the video used by sample application#

The video used by this sample application is determined by the configuration in Smart Intersection application. Refer to its documentation for further details.

Accessing the Services#

After the setup process completes, the URLs for all services are displayed on the terminal. You can get the URL for Traffic Intersection Agent UI and Traffic Intersection Agent API Docs from the response, and access it in a web browser.

The following is a sample response that you might get at script completion, which displays the URLs for accessing the relevant services:

Service endpoints displayed after setup completion

Troubleshooting#

Port Conflicts for Traffic Intersection Agent Backend or UI#

Ensure that the config file at src/config/deployment_instance.json for all instances (deployed from different directories, in case of multiple deployment on same machine) has empty values for the agent_backend_port and agent_ui_port fields:

    ...
    "agent_backend_port": "",
    "agent_ui_port": ""
    ...

Intel recommends to keep these values empty and let the Docker engine use ephemeral ports. However, if you need to provide an explicit port, ensure port values for all instances are unique. Additionally, ensure no other external services are running on these ports.