AI4CCAM has a whole Work Package (WP4) dedicated to defining a validation process (including KPIs and methodological aspects from WP1 and WP3) that embraces the variety of CCAM Use Cases involving explainability and Trustworthy AI concepts.
Under this WP the AI4CCAM digital framework and AI models (from WP1 and WP2) will be evaluated in dedicated Use Cases representing simulated CCAM scenarios and collect all necessary evidence for validation. An impact assessment in line with the use case scenarios will be performed as well.
Three complementary use cases also covering user acceptance will be implemented:
- Human-vehicle AI-based interactions and ethical dilemmas
- Advanced AI models for CCAM situation awareness and VRUs behavior anticipation
- Car trajectory prediction.
Skoda is elaborating its crosswalk scenario, which will be used to challenge and validate the Trustworthy AI.
AI4CCAM partners met on 25 January in Athens for the third General Assembly of the project, hosted by Inlecom.
A crowdy room to discuss project developments and upcoming achievements, especially considering that AI4CCAM is now one year old: the perfect moment for summing up progresses made so far.
The main focus of the meeting was on the three AI4CCAM Use Cases (UCs), discussing actions and on complementary of UCs, as well as common methodology, and user acceptance in UCs.
Of course, other relevant technical aspects of the project were discussed as well, such as
Ethical AI issues in autonomous driving, models and datasets, technical architecture. However, AI4CCAM is also looking into CCAM Partnership and internationalization activities that were discussed too, along with dissemination and communication activities.
Missed our insightful webinar on 29 November on building trust in AI for automated vehicles? The replay is now available!
This webinar aimed to delve into the challenges and opportunities of AI in the context of road safety, congestion reduction, and environmental impact. It primarily focused on what it takes to build trust and the conditions for adoption of AI-driven solutions. It gathered 58 attendees, notably from the industry, and also some researchers (from the CEA for instance), beyond of course AI4CCAM partners.
The webinar also aimed to shed light on the psychological and emotional factors that influence the adoption of AI in automated vehicles. By understanding these dynamics, we can contribute to an informed and well-rounded perspective on AI-driven mobility.
In this 1-hour webinar, join Marc Eynaud, Arnaud Gotlieb, Lucie Regereau, and Isabelle Vallet as they unveil user acceptance nuances, emotions, and barriers in automated mobility.
- Discover key insights from the European project AI4CCAM (AI for Connected Cooperative and Automated Mobility). Arnaud Gotlieb, the project coordinator, emphasizes the importance of trustworthy AI for autonomous driving.
- Explore Lucie Regereau’s methodology, delving into the emotions of daily car users in Paris, Berlin, and Warsaw. Understand why user acceptance is crucial for automated driving’s future.
- Isabelle Vallet shares fascinating results on traffic regulations, attitudes, and representations’ impact on automated vehicle adoption. See how these factors shape perceptions and trust.
AI4CCAM interviewed Nafsica Papakosta, Project Manager and Communications Specialist at INLECOM, leader of the WP5, which involves Communication, Dissemination and Exploitation for the AI and CCAM ecosystems.
In this interview, Nafsica addresses the methodology employed to identify and assess the outcomes of the AI4CCAM project with the greatest exploitation and innovation potential.
What is the purpose of the AI4CCAM’s Innovation & Exploitation Methodology?
As AI4CCAM’s Innovation and Exploitation Manager, INLECOM employs the Innovation & Exploitation Methodology (IEM), a well-established and validated framework to efficiently address the AI4CCAM project’s objectives in the areas of Exploitation, Innovation Registry, and Patent Filing. This methodology enables the documentation and evaluation of all project outcomes based on the owners’ input, with the purpose of producing a collection of outcomes and assessing their potential for innovation and exploitation. The AI4CCAM IEM is comprised of 3 phases and is being used to account for both exploitable and innovative project outcomes.
How does the Outcomes Registry phase of the IEM methodology work?
The initial phase of the IEM focuses on the completion of the Outcomes Registry via a web form that consists of 11 to 15 questions which help us pinpoint AI4CCAM’s innovative outcome(s) and key exploitative results filled out by project partners The Outcomes Registry is essentially a list of significant expected project outcomes together with preliminary descriptions of their characteristics. Partners submit their expected outcomes to the registry and assist in categorizing outcomes according to a certain exploitation path. The first phase of AI4CCAM’s IEM Methodology identified 11 expected Key Exploitable Results (KERs).
A feedback procedure will then be established to assist partners in better addressing difficulties, exploitation queries, and concerns. Based on the material collected, the initial Innovation & Exploitation assessment is performed, based on seven criteria: Solution Readiness, Anticipated market Interest, Anticipated size of the market in question, Level of innovation, Competitive landscape, Market readiness, and Solution reproducibility and re-usability.
What is the purpose of the AI4CCAM Innovation Registry and how will the decision on which patents to file be made?
On the innovation, IP, and IPR management front, INLECOM will use its experience as an Innovation Manager to help establish the AI4CCAM Innovation Registry, which will include novel AI-driven models used in safety-critical CCAM applications.
The methodology for cataloguing and supporting AI4CCAM outputs with high innovation potential will be provided in the third phase of the IEM. The AI4CCAM consortium partners will be able to fully explore and comprehend the innovation potential of their IP assets as a direct result of the innovation management initiatives guided by INLECOM, which will evaluate the outcomes’ innovation and commercial ambitions. A preliminary assessment of the outcomes’ innovative dimension will take place using the criteria of utility, novelty, and non-obviousness.
We will then be collaboratively evaluating, scoring, and prioritizing the areas where we will focus the IP related actions that have a planned commercial trajectory and/or strategic interest to AI4CCAM partners.
INLECOM intends to formally protect the AI4CCAM outputs through the submission and issuance of two patents. Our work will be guided by the EU’s legislative framework and the patent filing procedural requirements, as well as significant experience from past projects.
The CCAM Partnership Multicluster Meeting which will take place on 21 and 22 November in Brussels (for members of the CCAM Association only).
The CCAM Partnership Multicluster Meeting will discuss achievements and ongoing initiatives. The CCAM Partnership was born on 2021 to create a more user-centered and inclusive mobility system, increasing road safety while reducing congestion and environmental footprint; develop a more collaborative research, testing and demonstration projects in order to accelerate the innovation pace and implementation of automated mobility; work together at European level to help remove barriers and contribute to the acceptance and efficient rollout of automation technologies and services.
AI4CCAM, represented by the project coordinator Arnaud Gotlieb, Simula Research, will be attending the event as an auditor. This will be an important opportunity for the project to network and establish a dialogue with sister initiatives.
AI4CCAM continues its webinars focused on the different technical aspect of the project, exploring AI’s impact on connected and automated vehicles.
On 29 November, jointly organised with BVA, the webinar “On the Roads of Tomorrow: Securing Trust in AI for Automated Vehicles” will be held!
As a global leader in insights, data & consulting powered by behavioral science, The BVA Family is eager to delve into the challenges and opportunities of AI in road safety, congestion reduction, and environmental impact. This webinar aims to share valuable insights on building trust and conditions for adopting AI-driven solutions. We’ll also discuss the psychological and emotional factors influencing AI adoption in automated vehicles.
The webinar will also be the perfect opportunity for an open discussion, engaging in a dialogue with experts in the field and fellow participants to explore the future of AI in CAVs.
Curious about AI and connected/automated vehicles (CAVs)? Register (for free) here!
AI4CCAM has just released its public deliverable on Methodology for trustworthy AI (Artificial Intelligence) in the scope of Connected, Cooperative and Automated Mobility (CCAM).
The methodology relies on current European guidelines, namely the report Trustworthy Autonomous Vehicles produced by the Joint Research Center of the European commission in 2021, a first instantiation in the autonomous vehicles scope of previous initiatives including the AI Act , (European Commission, 2021) and the ethics guidelines for Trustworthy AI (Expert Group on Artificial Intelligence, 2019). It is also based on the developments of the confiance.ai program, a multi-sector research program tackling trustworthiness of AI in critical systems.
In this document, the proposed methodology is based in a macro decomposition of phases in a pipeline to ensure trustworthiness when developing a given AI-based system for CCAM, inspired from the confiance.ai program. Within such pipeline, specific activities in the project are circumscribed at a high- level and trustworthiness properties are targeted for each one of these phases. These trustworthiness attributes are based on the current developments at a European level, namely those published by the Joint Research Centre report on autonomous vehicles in 2021. All properties identified in the confiance.ai program are provided as support to complete the identified trustworthiness attributes depending on the studied use case.
Application of AI developments will be developed and applied in the use cases in future months.
Within the context of the AI4CCAM project the methodology should be instantiated in 3 uses cases addressing complementary views on AI use and perception. The methodology is instantiated in only one of the use cases of the project for first preliminary guidelines, this is: in AI-enhanced ADAS for trajectory perception. Subsequent activities in the project should see its application to other use cases. In the same logic, one scenario of many to come has been modeled for this specific use case.
AI4CCAM interviewed Pavan Vasishta, Akkodis, leader of the project WP4 working on “Use Case Implementation and Validation”.
Pavan is a Senior Research Scientist in Akkodis, and in this interview he tells us more what validation and impact mean when dealing with Artificial Intelligence (AI) for Autonomous Vehicles.
As leader of the WP4 of the project, what kind of work you did to define a validation process able to include a variety of CCAM use cases?
Our work in WP4 of the project deals mainly with validating the various AI models that will come out of this project in perception and trajectory prediction. Along with other project partners, we are developing guidelines on what validation means in terms of AI for Autonomous Vehicles.
For this, we are working on creating a Digital Twin – a recreation of the real world in simulation – that will act as a playground for all these models. Within this microcosm, we will be able to simulate a variety of behaviours, weather conditions and test out many different scenarios. Each use case and scenario will be studied in depth and simulated within the Digital Twin and compared against ethical and technological criteria for Vulnerable Road User acceptance of Connected and Autonomous Mobility.
What is the impact and the role of AI in the use cases you are working on within the project?
Explainable AI is at the heart of the use cases we are working on within the project. A major problem in the acceptance of AI today is its perceived “black box”-ness. One does not know what goes on within an AI model after inputting certain data. We aim to keep explainability at the heart of our work, especially when it comes to perception and trajectory prediction of VRUs.
While we are working on improving and validating Advanced Driver Assistance Systems and the robustness of AI-based perception systems for CAVs, we are also actively contributing to the development of trustworthy AIs in safe trajectory prediction. We have managed to get some very good results in predicting pedestrian pedestrian behaviour in urban scenarios.
How can AI4CCAM impact the user acceptance of CCAM let us say, in a 5-year horizon?
Autonomous Vehicles can be a game changer in human behaviour in the long run, providing autonomy, independence and safety to many, many people around the world. One of the main issues plaguing user acceptance is the opacity of vehicle behaviour and manoeuvres on open roads and in the presence of other road users. With all the work that we are putting into the explainabilty of the vehicles’ intentions in a variety of scenarios, within the ambit of AI4CCAM, it is my hope that more and more people feel comfortable around AVs so that we can unleash the full potential of Connected Mobility.
The Covenant of Mayors has recently released the publication “Policy options to reduce emissions from the mobility sector: inspiring examples and learning opportunities.”
The Covenant of Mayors is a European initiative that solicits voluntary commitments by local governments to implement EU climate and energy objectives. With transport as one of its key sectors, the Covenant plays a significant role in climate mitigation. Transport accounts for approximately 16% of actions submitted by Covenant signatories and contributes to 26-28% of total emissions, according to the Joint Research Committee’s Baseline Emission Inventories (BEI, Covenant of Mayors 2019 Assessment). The Covenant also tackles transport in its climate adaptation pillar by using transport-related indicators such as the vulnerability of transport infrastructure to extreme weather events.
In 2022, the Covenant of Mayors further expanded its focus by introducing an Energy Poverty Pillar, which includes indicators related to transport poverty. These metrics assess the accessibility and availability of public transport services, giving insights into how mobility influences social inclusion.
In the publication, AI4CCAM is included among the inspiring projects for improving public transport.
According to the International Transport Forum, public transport buses and trains can release up to a fifth of CO2 emissions per passenger-Km than ride-hailing and about a third of a private vehicle. A strong and well-integrated public transport network can also help provide equal access to jobs, education, services and other economic opportunities, particularly to those without private vehicles. Investing in public transport is one of the most effective measures to reduce transport emissions and bring cities closer to reaching their climate targets. It can increase equity and foster economic development. Therefore, ensuring that public transportation is accessible, affordable, and inclusive is of paramount importance to reach wider climate and societal goals set by cities.
Download the publication and find AI4CCAM at page 7!
