Empowering autonomous driving with large language models: A safety perspective

Y Wang, R Jiao, C Lang, SS Zhan, C Huang… - arXiv preprint arXiv …, 2023 - arxiv.org
Autonomous Driving (AD) faces crucial hurdles for commercial launch, notably in the form of
diminished public trust and safety concerns from long-tail unforeseen driving scenarios. This …

Semi-supervised semantics-guided adversarial training for robust trajectory prediction

R Jiao, X Liu, T Sato, QA Chen… - Proceedings of the IEEE …, 2023 - openaccess.thecvf.com
Predicting the trajectories of surrounding objects is a critical task for self-driving vehicles and
many other autonomous systems. Recent works demonstrate that adversarial attacks on …

Kinematics-aware trajectory generation and prediction with latent stochastic differential modeling

R Jiao, Y Wang, X Liu, C Huang, Q Zhu - arXiv preprint arXiv:2309.09317, 2023 - arxiv.org
Trajectory generation and trajectory prediction are two critical tasks for autonomous
vehicles, which generate various trajectories during development and predict the trajectories …

Cloud and Edge Computing for Connected and Automated Vehicles

Q Zhu, B Yu, Z Wang, J Tang, QA Chen… - … and Trends® in …, 2023 - nowpublishers.com
The recent development of cloud computing and edge computing shows great promise for
the Connected and Automated Vehicle (CAV), by enabling CAVs to offload their massive on …

Verification and Design of Robust and Safe Neural Network-enabled Autonomous Systems

Q Zhu, W Li, C Huang, X Chen, W Zhou… - 2023 59th Annual …, 2023 - ieeexplore.ieee.org
Neural networks are being applied to a wide range of tasks in autonomous systems, such as
perception, prediction, planning, control, and general decision making. While they may …

Safe and secure design of connected and autonomous vehicles

X Liu - 2023 - search.proquest.com
Abstract Machine learning-based techniques have shown great promises in perception,
prediction, planning, and general decision-making for improving task performance of …