Reference number : 172-2023-2024-Q4-1016395, amendment number : , agreement type : G, recipient legal name : University of Waterloo, recipient country : CA, recipient province : ON, recipient city : Waterloo|Waterloo, recipient postal code : N2L 3G1, program name english : Collaborative Science, Technology and Innovation Program - Collaborative R&D Initiatives, program name french : Programme de collaboration en science, en technologie et en innovation - Initiatives de collaboration en R-D, program purpose english : Collaborate on multiparty research and development programs to catalyze transformative, high-risk, high-reward research with the potential for game-changing scientific discoveries and technological breakthroughs in priority areas., program purpose french : Collaborer à des programmes multipartites de recherche-développement dans le but de profiter de la synergie entre la recherche transformatrice à haut risque et à haut rendement et le potentiel de découvertes scientifiques et de percées technologiques révolutionnaires dans des secteurs d’intervention prioritaires., agreement title english : A High-Fidelity Virtual Platform to Assess Certified Robustness of Deep Learning and Deep Reinforcement Learning Algorithms for Autonomous Driving, agreement title french : Une plateforme virtuelle à haute fidélité pour évaluer la robustesse déclarée des algorithmes d’apprentissage profond et d’apprentissage profond à renforcement destinés à la conduite autonome., agreement number : 1016395, agreement value : 121220.0, agreement start date : 2024-03-22, agreement end date : 2026-03-31, description english : In the realm of Autonomous Driving (AD), the application of Deep Learning (DL) and Deep Reinforcement Learning (DRL) has been pivotal. However, ensuring the robustness of these technologies against cyber threats remains a critical challenge. This project proposes the development of a high-fidelity virtual platform to assess the certified robustness of DL and DRL algorithms in AD, focusing on cybersecurity vulnerabilities. The proposed framework leverages the capabilities of the Carla open-source simulator to create a realistic virtual environment that mimics real-world cyberattack scenarios on AD systems.  This project will have three phases: the first phase will involve identifying and training baseline DL/DRL algorithms for AD and selecting the state-of-the-art methods suitable for various AD scenarios. In the second phase, the focus will be on developing a virtual platform prototype capable of accurately simulating diverse cyberattack scenarios, thereby providing a testbed for evaluating the resilience of DL/DRL-based AVs. The final phase involves training certified defense algorithms designed explicitly for DL/DRLbased AV systems, emphasizing their optimization for AD requirements and testing against a spectrum of cyber threats., description french : Une plateforme virtuelle à haute fidélité pour évaluer la robustesse déclarée des algorithmes d’apprentissage profond et d’apprentissage profond à renforcement destinés à la conduite autonome., expected results english : In the short term, anticipated outcomes will be strengthened collaborations across industry, academia, and government to support research excellence. In the medium term, anticipated outcomes will be the development of new and potentially disruptive technologies with collaborators. In the long term, find collaborative solutions to public policy challenges and create stronger innovation systems., expected results french : À court terme, les résultats prévus seront des collaborations renforcées entre l'industrie, le milieu universitaire et le gouvernement pour soutenir l'excellence en recherche. À moyen terme, les résultats attendus seront le développement de nouvelles technologies potentiellement perturbatrices avec des collaborateurs. À long terme, trouver des solutions collaboratives aux défis des politiques publiques et créer des systèmes d'innovation plus solides., additional information english : nan, additional information french : nan, owner organization : nrc-cnrc, owner organization title : National Research Council Canada | Conseil national de recherches Canada