Vehicle Event Recording Reference Implementation

Version: 2022.3   Published: 04/12/2021  

Last Updated: 12/13/2022

Overview

The Vehicle Event Recording Reference Implementation (RI) applies Artificial Intelligence (AI) to monitor the exterior of a vehicle and send events to the cloud dashboard. This includes event-based recording, remote view, driver coaching, and traffic violation detection features. The information is used for historical analysis, evidence support, and driver coaching by fleet management.

The RI includes an interface for the driver to be able to review events and check camera status on demand when the vehicle is not moving. The RI also sends event notifications and video clips to Amazon Web Services* (AWS*), which can be displayed on the cloud dashboard.

Select Configure & Download to download the reference implementation and the software listed below.

Configure & Download

Screenshot of running Vehicle Event Recording.


  • Time to Complete:  Approximately 60 minutes
  • Programming Language:  Python*
  • Available Software:  Intel® Distribution of OpenVINO™ toolkit 2021.4.2 Release

Recommended Hardware

The below hardware is recommended for use with this reference implementation. For other suggestions, see Recommended Hardware.

Target System Requirements

  • Ubuntu* 20.04

  • 6th to 10th Generation Intel® Core™ processors with Intel® Iris® Plus graphics or Intel® HD Graphics

How It Works

Vehicle Event Recording utilizes external facing cameras to detect objects and provide event-based video recording, remote view and driver coaching, and traffic violation detection features.

The RI is used for historical analysis, evidence support and driver coaching by fleet management.

Vehicle Event Recording has an interface for the driver to be able to review events and check camera status on demand when the vehicle is not moving.

The RI sends event notifications and video clips to AWS, which can be displayed on a dashboard.

The architecture is represented by a complex block diagram.

Figure 1: Architecture Diagram

Get Started

Step 1: Install the Reference Implementation

Select Configure & Download to download the reference implementation and then follow the steps below to install it.

Configure & Download

NOTE: If the host system already has Docker images and containers, you might encounter errors while building the reference implementation packages. If you do encounter errors, refer to the Troubleshooting section at the end of this document before starting the reference implementation installation.

  1. Open a new terminal, go to the downloaded folder and unzip the downloaded RI package.

    unzip vehicle_event_recording.zip
    
  2. Go to the vehicle_event_recording/ directory.

    cd vehicle_event_recording/
    
  3. Change permission of the executable edgesoftware file.

    chmod 755 edgesoftware
    
  4. Run the command below to install the Reference Implementation.

    ./edgesoftware install
    
  5. During the installation, you will be prompted for the Product Key. The Product Key is contained in the email you received from Intel confirming your download.

    A console window showing a system prompt to enter the product key.

    Figure 2: Product Key

  6. When the installation is complete, you see the message "Installation of package complete" and the installation status for each module.

    A console window showing system output during the install process. At the end of the process, the system displays the message “Installation of package complete” and the installation status for each module.

    Figure 3: Installation Success

    NOTE: If you encounter any issues, refer to the Troubleshooting section at the end of this document. Installation failure logs are available at the path: /var/log/esb-cli/Vehicle_Event_Recording_<version>/output.log

  7. To start the application, change the directory using the cd command printed at the end of the installation process:

    cd /opt/intel/eif/EII-UseCaseManager
    

Step 2: Run the Application

Prerequisites

  1. Run the application. Copy and run the make webui command from the end of the installation log:

    make webui
    
  2. Open the Web UI and go to 127.0.0.1:9090 on your web browser.

    A browser window showing the reference implementation dashboard.

    Figure 4: Reference Implementation Dashboard

  3. If you installed your ThingsBoard Cloud Server and you have enabled S3 Bucket Server on your AWS account, you can provide your configured AWS Access Key ID, AWS Secret Access Key, Thingsboard IP, Thingsboard Port and Thingsboard Device token on the Cloud Data Configuration tab. After you complete the Cloud configuration, make sure you click on the Save Credentials and Save Token buttons. Now you can import the ThingsBoard dashboard as described at the end of the Set Up ThingsBoard* Cloud Data to enable all dashboard features, including the cloud storage.

    A web app dashboard showing the Configuration tab. Certain fields arecovered with a blue bar for security

    Figure 5: Configuration Tab Contents

    NOTE: If you don't have an AWS account, you will not be able to access Storage Cloud. You can still enable the ThingsBoard Cloud Data if you configured it locally or on another machine.

  4. Access the Video Event Recording Dashboard with the following steps.

    • Go to sidebar and select the Run Application menu option.

      A web app dashboard showing the Run Application menu option.

      Figure 6: Select Run Application Menu Option

    • Configure the use case. Select video samples for all available streams and configure target devices for the selected models.

    • Optionally, you can also set the simulation data that you want to use. You can choose between using the KnowGo Simulator or simply use the CSV pre-recorded simulation data.

      A web app dashboard showing the Dashboard.

      Figure 7: Configure Use Case

    • Click on the Browse button and search for one of the sample videos delivered with the application at the following path: /opt/intel/eif/EII-UseCaseManager/modules/EII-EVMSC-UseCase/config/VideoIngestion/test_videos/

    • After you configure all four videos, select the target CPU, GPU or Hetero (which will combine CPU and GPU) for all models selected. Click on Run Application.

    Model Description

    • Obstacles Detection is enabled by default - this model will detect cars, pedestrians or other obstacles on the street.
    • Road Segmentation will classify each pixel into four classes: BG, road, curb, mark.

    Depending on the number of cameras configured, the application will start the Visualizer App that will analyze the video sample or video samples selected.

    A web app dashboard showing output from the visualizer.

    Figure 8: Visualizer Output

    At this point, you can see that the algorithm is analyzing the traffic from the video streams.

  5. After the visualizer starts, you can go to the ThingsBoard link and check the alerts sent by the reference implementation. If you configured the AWS credentials, you will also have access to video snapshots taken by the application on the video stream.

    A browser window showing the ThingsBoard link with the Intel Fleet Manager dashboard in the main view. Several components are displayed, including Alerts, Temperature, and a map showing the vehicle location.

    Figure 9: Intel Fleet Manager Dashboard shown in ThingsBoard

  6. You can also check the cloud storage from the Reference Implementation Storage menu option.

    A web app dashboard showing the Storage menu option.

    Figure 10: Reference Implementation Storage Menu Option

Run in Parallel with Driver Behavior Analytics Reference Implementation

To run this task, you will need to download and install Driver Behavior Analytics Reference Implementation.

For more details about parallel execution, see the Edge Insights for Fleet Use Case Manager documentation.

Prerequisites

Steps to Run the Application

  1. Change directory to Use Case Manager:

    cd /opt/intel/eif/EII-UseCaseManager
    
  2. Run the following command on your terminal to start the web server application.

    make webui
    
  3. Open your browser and go to 127.0.0.1:9090.

  4. Configure both installed reference implementations by setting the video source and the target (CPU, GPU or HETERO). Click on Run Application.

    NOTE: Configure each reference implementation by selecting the desired tab. For example, click the Run Application menu option, then click on VER to configure the Vehicle Event Recording RI. Next, click on DBA to configure the Driver Behavior Analytics RI.

    A browser window showing application with VER and DBA tabs - VER selected.

    Figure 11: Configure Vehicle Event Recording Reference Implementation

    A browser window showing application with VER and DBA tabs - DBA selected.

    Figure 12: Configure Driver Behavior Analytics Reference Implementation

  5. Wait for both Visualizers to get up and running.

    A browser window showing output of 2 visualizers in a side-by-side view.

    Figure 13: Visualizer Output for 2 Reference Implementations

Summary and Next Steps

This reference implementation successfully implements Intel® Distribution of OpenVINO™ toolkit plugins for detecting objects and provides event-based video recording. It uses Edge Insights for Fleet framework to cover historical analysis, evidence support, driver coaching, remote view, and traffic violation detection.

As a next step, try one the following:

  • Use deep learning models, Edge Insights for Fleet framework and a live external video camera stream to capture evidence support, remote view, traffic violations and coach the decisions that must be made by the algorithm.

  • This reference implementation uses Intel® Distribution of OpenVINO™ toolkit Open Model Zoo pre-trained models and 3rd party models, but you can extend it to use your own models.

Learn More

To continue your learning, see the following guides and software resources:

Known Issues

Uninstall Reference Implementation

If you uninstall one of the reference implementations, you need to reinstall the other reference implementations because the Docker images will be cleared.

Troubleshooting

Installation Failure

If the host system already has Docker images and its containers running, you will have issues during the RI installation. You must stop/force stop existing containers and images.

  • To remove all stopped containers, dangling images, and unused networks:

    sudo docker system prune --volumes
    
  • To stop Docker containers:

    sudo docker stop $(sudo docker ps -aq)
    
  • To remove Docker containers:

    sudo docker rm $(sudo docker ps -aq)
    
  • To remove all Docker images:

    sudo docker rmi -f $(sudo docker images -aq)
    

Docker Image Build Failure

If Docker image build on corporate network fails, follow the steps below.

  1. Get DNS server using the command:

    nmcli dev show | grep 'IP4.DNS'
    
  2. Configure Docker to use the server. Paste the line below in the /etc/docker/daemon.json file:

    { "dns": ["<dns-server-from-above-command>"]}
    
  3. Restart Docker:

    sudo systemctl daemon-reload && sudo systemctl restart docker
    

Installation Failure Due to Ubuntu Timezone Setting

While building the reference implementation, if you see /etc/timezone && apt-get install -y tzdata && ln -sf /usr/share/zoneinfo/${HOST_TIME_ZONE} /etc/localtime && dpkg-reconfigure -f noninteractive tzdata' returned a non-zero code: 1 make: *** [config] Error 1

Run the following command in your terminal:

sudo timedatectl set-local-rtc 0

Installation Encoding Issue

While building the reference implementation, if you see ERROR: 'latin-1' codec can't encode character '\\u2615' in position 3: ordinal not in range(256)

Run the following command in your terminal:

export LANG=en_US.UTF-8

Can't Connect to Docker Daemon

If you can't connect to docker daemon at http+docker://localhost, run the following command in your terminal:

sudo usermod -aG docker $USER

Log out and log back in to Ubuntu.

Check before retrying to install if group Docker is available for you by running the following command in a terminal:

groups

The output should contain "docker".

Installation Timeout When Using pip or apt Commands

You may experience a timeout issue when using the People's Republic of China (PRC) internet network.

Make sure that you have a stable internet connection while installing the packages. If you experience timeouts due to Linux* apt or Python* pip installation, try to reinstall the package.

Support Forum

If you're unable to resolve your issues, contact the Support Forum.

Product and Performance Information

1

Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.