SENSIBLE 4 OY

In GAMMS we will develop an autonomous terrestrial mobile mapping system; i.e. a mobile mapping system (MMS) robot for geodata acquisition and an AI-based highly automated mapping software. In contrast to today’s manned MMS whose cost is dominated by 2- to 3-people crews, we envision fleets of low-cost, autonomous, electrically-powered land vehicles, carrying mobile mapping systems (MMS) and collecting geodata in a massive, continuous way. Although we will develop generalpurpose geodata acquisition and processing techniques, in GAMMS we focus on the rapidly growing market of the High Definition (HD) maps for the autonomous vehicles (AVs), a.k.a. self-driving cars. Because of the enormous task of mapping the world roads for AVs we will develop highly automated software to produce HD maps from the MMS remote sensing data. Because of the safety requirements of AVs, we will also develop map certification methods and quasi real-time, online techniques to continuously update the HD maps. The building blocks of GAMMS are: an electrically-powered AV, a MMS, a GNSS/Galileo receiver, multi-sensor trajectory determination software, multispectral laser scanners, vehicle dynamic models, automated mapping software and mission risk analysis methods. A keystone of GAMMS –which encompasses the extension of the Galileo receiver and the development of ultra-safe, ubiquitous navigation methods at the 5 cm error level– is the use of Galileo features (e.g. E5 AltBOC signal) and new services: navigation message authentication (NMA), high-accuracy serive (HAS) and signal authentication. Galileo and our trajectory determination methods enable the GAMMS concept. Our market value proposition is the production of high-accuracy high-reliable maps at a fraction of today’s cost. In a first fielding of the AMMS technology we will focus on the skyrocketing market of HD maps and, for this particular application, our value proposition includes the quasi real-time, continuous online

Driving is a challenging task. In our everyday life as drivers, we are facing unexpected situations we need to handle in a safe and efficient way. The same is valid for Connected and Automated Vehicles (CAVs), which also need to handle these situations, to a certain extent, depending on their automation level. The higher the automation level is, the higher the expectations for the system to cope with these situations are. In the context of this project, these unexpected situations where the normal operation of the CAV is close to be disrupted (e.g. ODD limit is reached due to traffic changes, harsh weather/light conditions, imperfect data, sensor/communication failures, etc.), are called “events”. EVENTS is also the acronym of this project. Today, CAVs are facing several challenges (e.g. perception in complex urban environments, Vulnerable Road Users (VRUs) detection, perception in adverse weather and low visibility conditions) that should be overcome in order to be able to drive through these events in a safe and reliable way. Within our scope, and in order to cover a wide area of scenarios, these kinds of events are clustered under three main use cases: a) Interaction with VRUs, b) Non-Standard and Unstructured Road Conditions and c) Low Visibility and Adverse Weather Conditions. Our vision in EVENTS is to create a robust and self-resilient perception and decision-making system for AVs to manage different kind of “events” on the horizon. These events result in reaching the AV ODD limitations due to the dynamic changing road environment (VRUs, obstacles) and/or due to imperfect data (e.g. sensor and communication failures). The AV should continue and operate safely no matter what. When the system cannot handle the situation, an improved minimum risk manoeuvre should be put in place.

Transport related emissions and urbanisation are creating an unparalleled demand for less polluting and efficient means of moving. Tackling the challenge is imperative and it calls for comprehensive understanding of the landscape, its every aspect and innovative mindset. It is a well-known fact that electric vehicles are a big part of the solution (combined with renewable energy production). We aim at developing and demonstrating an innovative, modular vehicle concept that is just perfect for the urban needs: zero emission, compact, safe and rightsized for the mission. Furthermore, we aim at intensifying the utilisation of the vehicles through versatile designing to promote, e.g., multipurpose usage and shared concepts. The key technical innovations of our RECONFIGURABLE LIGHT ELECTRIC VEHICLE, REFLECTIVE, vehicle are: 1) modular, scalable, electric powertrain and reconfigurable interiors fit from L7 quadricycles to M1/A vehicles; 2) supreme structural and active safety proven in Euro NCAP crash test and real life experiments of our L7 demonstrator vehicles; 3) added usability and comfortability through adaptable charging solution combining conductive and wireless charging and limited automated features. To conclude, we aim at introducing a L7 demonstration vehicle that meets the highest quality and safety standards with an affordable price making it an irresistible choice for any urban environment and use case. No such solution exists at the market and our primary aim is to bridge this gap.

Complex environment and traffic conditions have major impact on the safety and operations of Connected and Automated Vehicles (CAVs). Weather affects not only the vehicle performance but also the roadway infrastructure, thereby increases the risk of collision and traffic scenarios variations. So far, most automated vehicles have been primarily trained and tested under optimal weather and road conditions with clear visibility. However, the systems will have to prove that they are equally reliable and accurate under any weather and road condition before they can see widespread acceptance and adoption. ROADVIEW integrates a complex in-vehicle system-of-systems able to perform advanced environment and traffic recognition and prediction and determine the appropriate course of action of a CAV in a real-world environment, including harsh weather conditions. ROADVIEW develops an embedded in-vehicle perception and decision-making system based on enhanced sensing, localisation, and improved object/person classification (including vulnerable road users). ROADVIEW ground-breaking innovations are grounded on a cost-effective multisensory setup, sensor noise modelling and filtering, collaborative perception, testing by simulation-assisted methods and integration and demonstration under different scenarios and weather conditions, reaching TRL 7 by the end of the project. ROADVIEW implements the co-programmed European Partnership “Connected, Cooperative and Automated Mobility” (CCAM) partnership by contributing to the development of a more powerful, fail-safe, resilient and weather-aware technologies. The consortium is a perfect combination of leading universities in the field and research institutes, high-tech SMEs, and strong industry leaders. Beyond their research excellence, the consortium members bring a unique portfolio of testing sites and testing infrastructure, ranging from hardware-testing facilities and rain and wind tunnels to test tracks north of the Arctic Circle.

During the past few years many projects and initiatives were undertaken deploying and testing Automated Vehicles (AVs) for public transportation and logistics. However in spite of their ambition, all of these projects stayed on the level of elaborated experimentation and never reached the level of a large-scale commercial deployment of transport services. The reasons for this are many, the most important being the lack of economically viable and commercially realistic models, the lack of scalability of the business and operating models, and the lack of user oriented services required for large end-user adoption of the solutions. The ULTIMO project will create the very first economically feasible and sustainable integration of AVs for MaaS public transportation and LaaS urban goods transportation. ULTIMO aims to deploy in three sites in Europe 15 or more multi-vendor SAE L4 AVs per site. A user centric holistic approach, applied throughout the project, will ensure that all elements in a cross-sector business environment are incorporated to deliver large-scale on-demand, door-to-door, well-accepted, shared, seamless-integrated and economically viable CCAM services. We target the operation without safety driver on-board, in a fully automated and mission management mode with the support of innovative user centric passenger services. ULTIMO’s innovative transportation models are designed for a long-term sustainable impact on automated transportation in Europe, around the globe and on society. The composition of the consortium ensures the interoperability between multiple stakeholders by making adoption of new technology at minimum costs and maximum safety. The integration of the ongoing experiments of previous AV-demonstrator projects ensures highest possible technical and societal impacts from the very beginning of the project, as well as during the project lifetime and even long after its completion.