Deep learning adversarial attacks and defenses in autonomous vehicles: A systematic literature review from a safety perspective
Abstract The integration of Deep Learning (DL) algorithms in Autonomous Vehicles (AVs)
has revolutionized their precision in navigating various driving scenarios, ranging from anti …
has revolutionized their precision in navigating various driving scenarios, ranging from anti …
A Comprehensive Literature Review on Artificial Dataset Generation for Repositioning Challenges in Shared Electric Automated and Connected Mobility
In the near future, the incorporation of shared electric automated and connected mobility
(SEACM) technologies will significantly transform the landscape of transportation into a …
(SEACM) technologies will significantly transform the landscape of transportation into a …
SBFT Tool Competition 2024-Cyber-Physical Systems Track
This report summarizes the results of the fourth edition of the 2024 Cyber-Physical Systems
tool competition, held as part of the SBFT'24 workshop. Three tools (AmbieGenVAE, CRAG …
tool competition, held as part of the SBFT'24 workshop. Three tools (AmbieGenVAE, CRAG …
Sovar: Build generalizable scenarios from accident reports for autonomous driving testing
Autonomous driving systems (ADSs) have undergone remarkable development and are
increasingly employed in safety-critical applications. However, recently reported data on …
increasingly employed in safety-critical applications. However, recently reported data on …
Dance of the ads: Orchestrating failures through historically-informed scenario fuzzing
T Wang, T Gu, H Deng, H Li, X Kuang… - Proceedings of the 33rd …, 2024 - dl.acm.org
As autonomous driving systems (ADS) advance towards higher levels of autonomy,
orchestrating their safety verification becomes increasingly intricate. This paper unveils …
orchestrating their safety verification becomes increasingly intricate. This paper unveils …
Boundary state generation for testing and improvement of autonomous driving systems
Recent advances in Deep Neural Networks (DNNs) and sensor technologies are enabling
autonomous driving systems (ADSs) with an ever-increasing level of autonomy. However …
autonomous driving systems (ADSs) with an ever-increasing level of autonomy. However …
Domain knowledge distillation from large language model: An empirical study in the autonomous driving domain
Engineering knowledge-based (or expert) systems require extensive manual effort and
domain knowledge. As Large Language Models (LLMs) are trained using an enormous …
domain knowledge. As Large Language Models (LLMs) are trained using an enormous …
Legend: A top-down approach to scenario generation of autonomous driving systems assisted by large language models
Autonomous driving systems (ADS) are safety-critical and require comprehensive testing
before their deployment on public roads. While existing testing approaches primarily aim at …
before their deployment on public roads. While existing testing approaches primarily aim at …
Security analysis and adaptive false data injection against multi-sensor fusion localization for autonomous driving
Multi-sensor Fusion (MSF) algorithms are critical components in modern autonomous
driving systems, particularly in localization and AI-powered perception modules, which play …
driving systems, particularly in localization and AI-powered perception modules, which play …
How does simulation-based testing for self-driving cars match human perception?
Software metrics such as coverage or mutation scores have been investigated for the
automated quality assessment of test suites. While traditional tools rely on software metrics …
automated quality assessment of test suites. While traditional tools rely on software metrics …