New Truck Collision Avoidance System 2020 (Full List of Features)

Truck Collision Avoidance System

Truck Collision Avoidance System for Postal Companies

Postal companies face enormous cost pressure and have to innovate to stay competitive. A vision-based solution connects distribution vehicles with the environment, to digitize last-mile delivery processes. This article will tell more about the truck collision avoidance system, its features and how latest deep learning technologies are leveraged in a high-ROI business case. is a cloud platform that enables businesses to easily build, manage and operate their own AI vision applications in multiple industries. All use cases run on the edge to leverage the latest hardware technologies and take advantage of edge device capabilities in resource constrained environments.

One major use case was developed together with a worldwide leading postal organization to digitize Delivery Logistics and accident prevention using Deep Learning and visual AI together with Intel.

Number of Accidents at All-Time High!

The global parcel delivery market is growing at an unprecedented speed due to online sales; as a result, traditional postal companies face enormous cost pressure and must take action to stay competitive.

Delivery vans, which often make up a large share of postal fleets, typically have many offline processes. They are expensive to manage and maintain due to process inefficiencies, disconnected systems, property damage and personal injuries.

To manage the rapidly growing number of dispatched parcels, the fleets are generally expected to grow in accordance. At the same time, delivery vans are the direct touch point to the end client, hence crucial for the company’s reputation and perceived product quality.

Serious cost-related problems to be addressed include:

  • High costs due to accidents and damage
    As time pressure increases with a growing number of packages out for delivery, the number of accidents and associated direct costs for repair are at an all-time high. Additionally, van damage has a direct impact on the company’s reputation – especially if an accident causes bodily harm.
  • High costs due to time-consuming processes
    Along with accidents comes time-consuming processes such as legal fees, indirect costs to fill in damage reports or arranging replacement vehicles to avoid interruption to delivery service. Moreover, current ways of reporting accidents often face media discontinuity (e.g. fill in forms by hand and on paper, copy it into another system).
  • High costs due to parcel theft
    Parcel delivery vans are a popular target for theft. Urban areas face increasing numbers of stolen packages, mainly electronic goods. While postal organizations try to control this, there’s still no effective way to prevent and solve these crimes. Often, the only way is to increase staffing for certain routes, hence driving up costs and further impacting profitability.

A multi-functional collision avoidance system for trucks in delivery logistics will help to solve these challenges.

AI Vision To Overcome the Challenges

While vehicles become smarter, operations often cannot benefit from automotive systems because of sensitive business information, certification and development processes or operative challenges (e.g. leased fleet, changing manufacturer).

A vision-based solution, based on the platform powered by Intel® AI technologies, uses optical sensors around delivery vans to connect the vehicles with the environment, hence automating last-mile delivery processes. The business automation system is independent of any van manufacturer and can be equipped at or after van procurement in less than two hours.

As the vans are now connected to the cloud and have computing resources locally available, the system is easily able to integrate future innovations. The initial product is focusing on decreasing the costs related to delivery vans by providing the following main functionalities:

  1. Accident Prevention
    The van driver gets a 360° view of the delivery van without any blind spot on a tablet and can easily access each single stream. Compared to automotive-grade systems, the cameras are mounted at optimized positions (above the surroundings) and provide high resolution streams.
  2. People Safeguard functionality
    For accident prevention, the deep learning model detects people around the delivery van and warns the driver in real-time about potential obstacles.
  3. Automated workflows
    All workflows related to damage management are digitized and can be completed directly on the tablet before the report is synced on a cloud-based damage management portal.
  4. Theft detection
    The system detects anomalies around the van, flags the situation and starts recording automatically. If an incident happens, the video file can be exported.

Integrate With Latest Intel Technology

The environment in delivery vans is harsh, causing technical challenges to implement a vision-based system:

  • Next-gen AI computing performance
    Vision-based applications are resource-intensive. Additionally, the environment often has limited electricity, cooling capabilities and space. Hence, the hardware must be a small form factor and running at limited power supply while being able to process video material in real-time.
  • Power and Vibration Constraints
    Van batteries are exposed to continuous stop-and-start activity and are easily depleted, so additional device power consumption must be minimized. The hardware is exposed to continuous vibration inside of the delivery van, so it must be vibration-proof.
  • Scalability and Cost Effectiveness
    As the industry is facing financial pressures and the system needs to be scalable to thousands of vans, it must come at a cost low enough to achieve a return on investment. Therefore, expensive technologies or non-modular systems cannot be used. The total cost of implementation matters as well as the possibility to dynamically replace or upgrade single parts of the system. software leverages the latest hardware and software technology from Intel to meet these diverse and challenging requirements. The use case runs on Intel® Core™ processors, combined with the Intel® Movidius™ Myriad™ X VPU  for Deep Learning inference, in a robust and industrial housing. Additionally, the Deep Learning models are optimized using the Intel® Distribution of OpenVINOTM Toolkit , a software kit that helps developers and data scientists speed up computer vision workloads, streamline deep learning inference and deployments, and enable execution across a range of Intel® hardware.

The computer is mounted inside of the delivery vans. The Movidius Myriad X VPU provides enough power to perform object detection on three input camera streams in real time at low electrical power consumption. The output is used to warn the driver about potential obstacles, while the Intel Core i3 processor performs geometrical transformation tasks to display the live video streams to the driver inside of the delivery van.

Use of the latest, optimized Intel® AI technologies has resulted in significant damage-related cost reduction, according to initial KPI assessments after the first month of system usage. Further features are now planned to be integrated into the same system, in order to scale the benefits observed even more.

According to Arkadiusz Hruszowiec, Intel Business Development Manager for the region, “this project is a proof-point that our strategy of enabling AI on edge devices works in practice. By using the Intel® Distribution of OpenVINO™ toolkit and Intel® Vision Accelerator Solutions, is able to take video streams, analyze them in a deep-learning model and draw insights in real time at the edge, which result in real business impact for the end customer.”

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