Work

Hybrid Resilient Framework for Self-Driving Car

Year: 2025

Type: article

Abstract: Self-driving cars are expected to become a convenient source of transportation in near future. However, achieving a self-driving car at Level 5 still poses challenges. The challenges could be attributed to maintaining desirable functionality under uncertainty while accomplishing a task. This paper proposes a hybrid N-redundancy-based resilient framework for the perception module that integrates abstract sensor fusion and a probabilistic deep learning model to ensure resilient performance. Experimental results using the CARLA simulator demonstrate that our model consistently outperforms existing approaches in runtime efficiency while maintaining functional integrity under attack. Time-based performance evaluations reveal that our hybrid model achieves faster and more reliable outputs, validating its effectiveness in resilient autonomous perception.

Source: IEEE Access

DOI: 10.1109/access.2025.3597990

Authors: Junaid M. Qurashi, Kamal Jambi, Fathy E. Eassa, Maher Khemakhem, Fawaz Alsolami, Reem Abdulaziz Alnanih

Institution: King Abdulaziz University

Language: English

Topic: Autonomous Vehicle Technology and Safety

Subfield: Automotive Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,075