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Robust multimodal pedestrian understanding algorithm for pedestrian safety in autonomous driving

CORDIS · observation · Publication date unknown

ne understanding for better contextual awareness; and developing defense mechanisms against adversarial attacks that could compromise system reliability. The proposed solution integrates cutting-edge deep learning techniques, including transformer-based architectures for efficient feature extraction, multi-task learning paradigms for simultaneous detection and prediction tasks, and adversarial training methods to enhance system robustness. The project's interdisciplinary approach, combining computer vision, machine learning, and transportation safety expertise, positions it to make significant contributions to both academic research and industrial applications in the emerging field of intelligent transportation systems. The expected results will significantly enhance the safety and reliability of autonomous vehicles in pedestrian-rich environments, contributing to the broader development of safer autonomous driving technologies. These advancements will support ongoing EU efforts to promote autonomous driving and build public trust in these systems. autonomous driving; robust pedest

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recordType

award

status

SIGNED

region

EU

value

276187.92

unit

EUR