AI4CCAM | Trustworthy AI for CCAM

Summary
Considering Artificial Intelligence (AI) capabilities and potential risks, and taking into account its limitations, AI4CCAM will develop an open environment for integrating trustworthy-by-design AI models of vulnerable road user behaviour anticipation in urban traffic conditions, and accounting for improved road safety and user acceptance. Leveraging the Trustworthy AI guidelines for general intelligent software systems and the ethics recommendations for connected automated vehicles, AI4CCAM will support AI-based scenarios management in which pedestrian/cyclist behaviour anticipation models will integrate visual gaze estimation and where explainable ego car trajectory prediction models are simulated with ethical dilemmas and multiplied with generative adversarial networks and metamorphic testing techniques. The AI4CCAM open environment will include an interoperable digital framework for managing and generating AI-based urban-traffic scenarios in which trustworthy-by-design AI models can be tested and an online participatory space to foster acceptance of AI in automated driving, determine AI risks and identify biases in datasets and cyber-threats. Simulation scenarios of road users interacting with automated vehicles will be developed and evaluated in three complementary use cases covering the whole sense-plan-act paradigm and user acceptance. As such, the project will advance knowledge in building trustworthy-by-design AI-based solutions for CCAM applications.
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More information & hyperlinks
Web resources: https://cordis.europa.eu/project/id/101076911
Start date: 01-01-2023
End date: 31-12-2025
Total budget - Public funding: 5 965 630,00 Euro - 5 965 630,00 Euro
Cordis data

Original description

Considering Artificial Intelligence (AI) capabilities and potential risks, and taking into account its limitations, AI4CCAM will develop an open environment for integrating trustworthy-by-design AI models of vulnerable road user behaviour anticipation in urban traffic conditions, and accounting for improved road safety and user acceptance. Leveraging the Trustworthy AI guidelines for general intelligent software systems and the ethics recommendations for connected automated vehicles, AI4CCAM will support AI-based scenarios management in which pedestrian/cyclist behaviour anticipation models will integrate visual gaze estimation and where explainable ego car trajectory prediction models are simulated with ethical dilemmas and multiplied with generative adversarial networks and metamorphic testing techniques. The AI4CCAM open environment will include an interoperable digital framework for managing and generating AI-based urban-traffic scenarios in which trustworthy-by-design AI models can be tested and an online participatory space to foster acceptance of AI in automated driving, determine AI risks and identify biases in datasets and cyber-threats. Simulation scenarios of road users interacting with automated vehicles will be developed and evaluated in three complementary use cases covering the whole sense-plan-act paradigm and user acceptance. As such, the project will advance knowledge in building trustworthy-by-design AI-based solutions for CCAM applications.

Status

SIGNED

Call topic

HORIZON-CL5-2022-D6-01-05

Update Date

09-02-2023
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Cooperative, connected and automated mobility Partnership (CCAM)
CCAM Partnership Call 2022
HORIZON-CL5-2022-D6-01-05 Artificial Intelligence (AI): Explainable and trustworthy concepts, techniques and models for CCAM (CCAM Partnership)
Horizon Europe
HORIZON.2 Global Challenges and European Industrial Competitiveness
HORIZON.2.5 Climate, Energy and Mobility
HORIZON.2.5.7 Clean, Safe and Accessible Transport and Mobility
HORIZON-CL5-2022-D6-01
HORIZON-CL5-2022-D6-01-05 Artificial Intelligence (AI): Explainable and trustworthy concepts, techniques and models for CCAM (CCAM Partnership)
HORIZON.2.5.8 Smart Mobility
HORIZON-CL5-2022-D6-01
HORIZON-CL5-2022-D6-01-05 Artificial Intelligence (AI): Explainable and trustworthy concepts, techniques and models for CCAM (CCAM Partnership)