September 12, 2025Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering

Intelligent collision avoidance system for urban scenarios using deep learning

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The system achieved a precision rate of 98.79% when detecting pedestrian collisions, indicating high effectiveness.
Extensive validation on benchmarks like City Persons and KITTI showed mAP@0.5 rates of 96.21% and 94.83%, respectively.
This automated system analyzes factors such as pedestrian exposure and intersection density to enhance safety.
The proposed model's real-time inference capabilities suggest its feasibility for practical applications in urban traffic management.

Authors

WFWajdi FarhatUniversity of Monastir MGMohsen GuizaniMohamed bin Zayed University of Artificial Intelligence OROlfa Ben RhaiemNorthern Border University

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Implication

This study demonstrates a collision avoidance system for pedestrian safety in urban environments, highlighting its accuracy and real-time capabilities.

Cite This Study

Farhat et al. (2025) studied this question.

synapsesocial.com/papers/68d418e1713b0b5dfea64ef3 https://doi.org/10.1177/09544070251370923

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Implication

This study demonstrates a collision avoidance system for pedestrian safety in urban environments, highlighting its accuracy and real-time capabilities.

Intelligent collision avoidance system for urban scenarios using deep learning

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Intelligent collision avoidance system for urban scenarios using deep learning

Key Points

Discussion

Implication

Cite This Study