The AWARD project (All Weather Autonomous Real logistics operations and Demonstrations) will run for three years as part of the Horizon 2020 European initiative, and set out to develop new technologies for autonomous driving and fleet management for logistics vehicles.
These vehicles will be able to be used at any time, even in poor weather conditions (snow, rain, fog), thereby optimising their use. Different kinds of vehicles operating in a range of scenarios will be researched: forklifts in warehouses, hub-to-hub shuttles on the open road, luggage dispatch in airports, container transfers in ports... to name but a few.
If the technical proof of concept works, and the solutions are then used in real-world applications, we can expect to see the following outcomes:
- 24/7 productivity, availability, and flexibility
- Optimised processes, lower operating costs, and enhanced profitability
- Minimised human error
- Working conditions transitioning to new and/or office jobs
- A solution for the labour shortage
- Faster processes (and delivery) with less risk of error and failure
- The ability to keep working in all weather conditions
- Enhanced transport efficiency
- Lower greenhouse gas emissions
- Enhanced road safety
- Greater interest in, and fascination with, autonomous driving
Cerema is closely involved in the project, with a role to play in a number of phases.
Identifying stakeholder needs
AWARD aims to develop systems for the “operation and demonstration of real autonomous logistics in all weather conditions”. We are currently researching stakeholder expectations of autonomous logistics, by analysing the results of a survey conducted a few months ago. Indeed, autonomous logistics systems are relevant to a wide range of stakeholders, such as operators and current and future fleet managers, teleoperators, maintenance staff, vehicle manufacturers, and many more besides.
The aim of the survey was to obtain detailed information about these various factors, and understand them in order to assess the acceptance of autonomous systems. With this in mind, we sought out to assess the needs and concerns of every single stakeholder. To that end, we proposed four case studies on different kinds of facilities: in warehouses, hub-to-hub on public roads, in ports, and in airports.
Developing technologies for autonomous vehicles in poor weather conditions
After analysing feedback from surveyed stakeholders with input from other partners, Cerema helped to define in which weather conditions the sensors should be tested, as well as the architecture needed to process data obtained from the project, in line with the needs that were expressed.
In June 2021, we welcomed project partners to the Cerema facility in Clermont-Ferrand, home to our PAVIN platform and some of the ITS research team that works on intelligent transport systems, and sensors for autonomous vehicles in particular.
The PAVIN platform enables us to recreate different weather conditions like fog and rain at different intensities (the droplets can be configured to different sizes and densities), in day or night, to test the sensors.
We conducted foul weather sensor tests to assess the effectiveness of 35 sensors using different technological solutions (LWIR thermal camera, radar, Lidar), with a person, vehicle, or obstacle present on the carriageway.
In the third phase of the project, we will conduct other road tests in different weather conditions in parallel, but in less controlled weather conditions.
The results of these tests conducted over a one month time period will be analysed by a range of partners, with support from Cerema.
Defining the regulatory framework for the safe deployment of autonomous heavy goods vehicles
Cerema will also be involved in phase 7, which focuses on regulations and the business model for rolling out autonomous heavy goods and utility vehicles in logistics. Recommendations will be made on standardising regulations and certifications on a European level, in an effort to ensure the economic viability and safety of autonomous transport systems
We will also help disseminate the results in professional and institutional circles.
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101006817.
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