How generative adversarial networks promote the development of intelligent transportation systems: A survey

H Lin, Y Liu, S Li, X Qu - IEEE/CAA journal of automatica sinica, 2023 - ieeexplore.ieee.org
In current years, the improvement of deep learning has brought about tremendous changes:
As a type of unsupervised deep learning algorithm, generative adversarial networks (GANs) …

A survey of robustness and safety of 2d and 3d deep learning models against adversarial attacks

Y Li, B **e, S Guo, Y Yang, B **ao - ACM Computing Surveys, 2024 - dl.acm.org
Benefiting from the rapid development of deep learning, 2D and 3D computer vision
applications are deployed in many safe-critical systems, such as autopilot and identity …

A survey on safety-critical driving scenario generation—A methodological perspective

W Ding, C Xu, M Arief, H Lin, B Li… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Autonomous driving systems have witnessed significant development during the past years
thanks to the advance in machine learning-enabled sensing and decision-making …

Advdo: Realistic adversarial attacks for trajectory prediction

Y Cao, C **ao, A Anandkumar, D Xu… - European Conference on …, 2022 - Springer
Trajectory prediction is essential for autonomous vehicles (AVs) to plan correct and safe
driving behaviors. While many prior works aim to achieve higher prediction accuracy, few …

Proxyformer: Proxy alignment assisted point cloud completion with missing part sensitive transformer

S Li, P Gao, X Tan, M Wei - … of the IEEE/CVF conference on …, 2023 - openaccess.thecvf.com
Problems such as equipment defects or limited viewpoints will lead the captured point
clouds to be incomplete. Therefore, recovering the complete point clouds from the partial …

Cat: Closed-loop adversarial training for safe end-to-end driving

L Zhang, Z Peng, Q Li, B Zhou - Conference on Robot …, 2023 - proceedings.mlr.press
Driving safety is a top priority for autonomous vehicles. Orthogonal to prior work handling
accident-prone traffic events by algorithm designs at the policy level, we investigate a …

Uncovering the missing pattern: Unified framework towards trajectory imputation and prediction

Y Xu, A Bazarjani, H Chi, C Choi… - Proceedings of the IEEE …, 2023 - openaccess.thecvf.com
Trajectory prediction is a crucial undertaking in understanding entity movement or human
behavior from observed sequences. However, current methods often assume that the …

Synthetic datasets for autonomous driving: A survey

Z Song, Z He, X Li, Q Ma, R Ming, Z Mao… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
Autonomous driving techniques have been flourishing in recent years while thirsting for
huge amounts of high-quality data. However, it is difficult for real-world datasets to keep up …

Mixsim: A hierarchical framework for mixed reality traffic simulation

S Suo, K Wong, J Xu, J Tu, A Cui… - Proceedings of the …, 2023 - openaccess.thecvf.com
The prevailing way to test a self-driving vehicle (SDV) in simulation involves non-reactive
open-loop replay of real world scenarios. However, in order to safely deploy SDVs to the …

Safebench: A benchmarking platform for safety evaluation of autonomous vehicles

C Xu, W Ding, W Lyu, Z Liu, S Wang… - Advances in …, 2022 - proceedings.neurips.cc
As shown by recent studies, machine intelligence-enabled systems are vulnerable to test
cases resulting from either adversarial manipulation or natural distribution shifts. This has …