+31 (0) 570 74 54 30 info@v-tron.eu

VISTA

Fast and damage free docking of trucks through camaras and software

Objectives of VISTA

Rearward manoeuvring of truck-trailer combinations can be challenging, especially if the vehicle is multi-level.

Logistics companies face significant damage caused by vehicle combinations that are engaged in ‘docking’: parking the vehicle backwards against a dock door to load and unload goods.

VISTA stands for VIsion Supported Truck Docking Assistant.

VISTA stands for VIsion Supported Truck docking Assistant. The purpose of VISTA is to support truck drivers while manoeuvring vehicle combinations to dock doors at logistics sites.

The purpose of VISTA is to support truck drivers while manoeuvring vehicle combinations to dock doors at logistics sites.

The heart of the VISTA solution is accurate, optical localisation of truck and trailers. For this purpose, cameras are mounted on the distribution centre.

Based on this localisation, an optimal, feasible path is calculated. The driver then receives information via an intuitive user interface to help him follow this path properly. The driver is handed the user interface when entering the distribution centre.

Approach project

VISTA does not require fixed instrumentation in the truck. This allows the solution to be made suitable for all existing trucks. VISTA costs, CAPEX and OPEX, are low due to the use of camera technology and because of good integration into existing processes at distribution centres.

Vista overview

VISTA aims to demonstrate the solution at Technology Readiness Level (TRL) 7 level. This means that the solution will be demonstrated in real-life, in a complex, practical environment. Upon successful demonstration, commercial parties will take the solution to market.

Contact V-tron:

Rakshith Kusumakar

Project leader V-tron

T: +31 (0) 570 74 54 30

 

E: info@v-tron.eu

Results achieved

1 July 2022:

At the 15th International Symposium on Advanced Vehicle Control (AVEC) in Kanagawa, Japan, a paper will be presented on the development of Model Predictive Control-based driver support as developed in VISTA. The full paper was recently submitted after acceptance of the extended abstract. After its successful reuse in the EU Horizon 5G-Blueprint project, the paper now covers both applications.

17 November 2021:

A test session took place in the VISTA VR simulator of the Hogeschool van Arnhem en Nijmegen. The drivers performed truck docking manoeuvres in the virtual world presented to them through VR glasses. They drove both manually and with the help of VISTA and gave their valuable feedback on the various aspects of VISTA, such as route planning, road follower-based feedback and the various HMI elements.

30 September 2021:

The first promising results have been presented from the camera-based VISTA Real-Time Localisation System (RTLS). The AI algorithms were trained using data from the cameras installed at DC Visser. This data was first annotated to make it usable for training purposes. The RTLS results compare well with the RTK-DGPS reference data captured in July.

Challenges

The following VISTA work packages should be implemented:

  1. Analysis of business potential
  2. Vision-based RTLs
  3. Vehicle planning & control
  4. Human Machine Interface
  5. Integration

V-tron is leader of work package 5. This work package integrates all elements of work packages 2, 3 and 4.

Two main integration environments are used. First, a scale setup in the HAN lab, based on scaled truck/trailers, so that most integration aspects can be tested on a reproducible, low-cost setup that is always available.

The final setup will be a real-life environment at one or two distribution centres. In this environment, the integrated solution will be tested and demonstrated at TRL7 level.

Project partners:

Co-funded by the European Union through the INTERREG programme Deutschland-Nederland, the Miniserium für Wirtschaft, Innovation, Digitalisierung und Energie of Nordrhein-Westfalen and the Province of Gelderland

Involved employees V-tron:

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