A road toward safe artificial intelligence in mobility

Recent progresses in machine learning in general ad deep learning in particular make it possible to include this technology in more and more autonomous vehicles. However, before this possible future becomes reality and our roads are made safer with algorithms replacing human drivers, it is necessary to know how to prove the quality of the decisions made. This Chair project "A road towards Safe Artificial Intelligence for Mobility" is a research proposal aimed at strengthening local research dynamics about safety issues associated with the use of artificial intelligence in mobility. To achieve this goal, he will endeavor to formalize the problem, to propose algorithms to solve it and to demonstrate its feasibility on real autonomous vehicles under real driving conditions. To establish safety certificates, a first idea is to develop the associated theory by formalizing this requirement as a multi-objective/multi-level optimization problem aimed at both learning and guaranteeing the quality of the IA model learning. However this optimization problem, a mixed binary program, is very complex and does not scale. The challenge is to work on formalization, relaxations and resolution algorithms. The goal is to build and train, in a reasonable time, deep neural networks which can be proven robust, possibly associated with the explicability and the interpretability of these black box models. The second research direction of the grant proposal aims at ensuring the safety of deep neural networks in the framework of mobility by monitoring its decision processes. This implies research on redundant multimodal perception including audio and video data processing acquired through different modalities (such as polarimetry) and related fusion issues in the context of deep learning. Another important aspect is the safe monitoring of the decision-making processes by including novelty and out-of-distribution detection mechanisms and self assessment. A way to achieve this goal is the formalization of the problem in the context of robust statistical hypothesis testing on multi-modal inputs based on optimal transport theory. The third part of the project regards the implementation of the proposed solutions. It aims at testing the investigated solutions in real conditions with real autonomous vehicles. To this end, our chair project is articulated with the Rouen Autonomous Lab which already has four autonomous vehicles on site and the program PIA3 TIGA "Rouen Normandie Intelligent Mobility for All - for an integrated system of multimodal and carbon-free mobility". The chair will intervene, together with the INSA and the University of Rouen Normandy as part of the Normandy University, in all levels of engineering training and in training through research programs. To this end, the chair will be deeply involved in the Normandy University Research Institute (EUR) project MINMACS in the field of safe AI for mobility. To reach these ambitious goals and to make the Madrillet Campus in Normandy an international reference in the field of AI for mobility, the Chair will benefit from: (i) a team of three professors combining the relevant scientific skills, (ii) a financial support from INSA and the University of Rouen Normandy, (iii) the scientific collaboration with local and national research laboratories with research in AI domain, (iv) facilities of the CRIANN (the regional on-site calculation center), and (v) the support of Rouen Autonomous Lab and its four autonomous vehicles operating on Madrillet Campus Our ultimate goal is to contribute to make learning systems safe for mobility and beneficial to society.