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AINARA Report Summary

Project ID: 666736

Periodic Reporting for period 2 - AINARA (Automation and INtelligence solutions for Automated Road trAnsport systems)

Reporting period: 2016-05-01 to 2017-04-30

Summary of the context and overall objectives of the project

According to a report from Navigant Research, worldwide sales of vehicles with autonomous capability will grow from zero in 2014 to 94.7 million in 2035. Without waiting for the legal framework, which is currently being implemented thanks to the efforts of CityMobil2 and several European governments, the market study conducted as part of the TAXISAT project revealed that the first wave of user demand would emerge in Europe in 2015 for new, more flexible and efficient transport services in pedestrian city centres and closed areas such as industrial sites or amusement parks. These needs are be based on the use of conventional cars but rather on “pod-type vehicles” (lightweight electric vehicles, either individual, or collective). If major automotive manufacturers invest massively in the automation of their vehicles, this is not the case of these special vehicles manufacturers.

We believe that these short and medium term markets will grow so fast that current vehicle manufacturers, Transport Network Operators vehicles and Transport Organising Authorities won’t be able to adjust their services to these new challenges as quickly as required. These organisations and companies will need key skills in robotics, intelligence and fleet management.

This is the point where we come in: meeting the emerging requirements by providing off-the-shelf ARTS solutions, instead of the highly customised engineered applications which are currently in use. The latter are incompatible with a wider uptake of the technology that requires to deal with a wide variety of environments while offering a high quality of service. To take a solid position on the emerging global autonomous vehicles market and acquire an industrial dimension, we are convinced that we must combine the best tools and practices from software engineering, with deep robotics expertise in areas such as sensing, localisation, navigation.

Our ambition is to become a leader in the provision of software solutions for vehicle automation and fleet management, for passengers and goods transportation.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

From a very high level point of view, the project is split in two phases:
- Phase one focuses on the development of the robotisation kit including all software components and algorithms together with the definition of an appropriate validation plan, and the set up of validation tools.
- Phase two focuses on the actual validation of the software kit and the demonstration of the developed solution for a large-scale deployment on various types of vehicles and various ARTS transportation configurations

During phase 2, we have finished some developments that were necessary for the delivery of a stable version of the software and the activity has been concentrated on the validation of the system, both with simulation and on test track. The long term demonstration has been prepared, including the integration on the vehicle. The vehicle dediacted to technological demonstration has also been prepared, embedding special sensors for latest technology demonstration.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

General progress beyond the state of the art can be summarised by the delivery of an industrial-grade software for autonomous navigation of driverless vehicles. Our system composed of software and related sensors meets constraints of quality, safety, reliability and has a cost compatible with a large deployment.

From a technical point of view, our progress deals with the following domains:
Localisation: we have developed a 2D Lidar and a 3D visual localisation technique, that can be fused together.
Obstacle detection: we have developed an obstacle detection technique based on Lidars technology. We have characterized our obstacle detection sensors and algorithms in various environmental conditions (fog, rain, etc.)
Path planning and following: we have developed a path planning and following method adapted to the kinematic of the EZ10 vehicle
Fleet Management: a centralised server is in charge of monitoring vehicle’s status and sending mission to the vehicles on a site.

These developments have a direct impact on the market of autonomous vehicles. We are currently able to provide potential customers with a system for the autonomous navigation of their platforms. With the first demonstrations of our solution integrated on EZ10 vehicles, we prove that these systems are already operational and economically profitable.
On the long term, as these autonomous transportation systems become more and more accepted and widespread, they will profoundly change the way we move, reducing congestion, gas emission, road accidents, and transportation time.

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