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Found 7 papers, 5 papers with code
Benchmarking and Improving Bird's Eye View Perception Robustness in Autonomous Driving
In this study, we present RoboBEV, an extensive benchmark suite designed to evaluate the resilience of BEV algorithms.
Exploring Backdoor Attacks against Large Language Model-based Decision Making
Specifically, we propose three attack mechanisms and corresponding backdoor optimization methods to attack different components in the LLM-based decision-making pipeline: word injection, scenario manipulation, and knowledge injection.
The RoboDrive Challenge: Drive Anytime Anywhere in Any Condition
In the realm of autonomous driving, robust perception under out-of-distribution conditions is paramount for the safe deployment of vehicles.
RoboDepth: Robust Out-of-Distribution Depth Estimation under Corruptions
Depth estimation from monocular images is pivotal for real-world visual perception systems.
The RoboDepth Challenge: Methods and Advancements Towards Robust Depth Estimation
In this paper, we summarize the winning solutions from the RoboDepth Challenge -- an academic competition designed to facilitate and advance robust OoD depth estimation.
RoboBEV: Towards Robust Bird's Eye View Perception under Corruptions
Our experiments further demonstrate that pre-training and depth-free BEV transformation has the potential to enhance out-of-distribution robustness.
On the Adversarial Robustness of Camera-based 3D Object Detection
In recent years, camera-based 3D object detection has gained widespread attention for its ability to achieve high performance with low computational cost.
