104 citations · 160 across the 11 of their papers we have counts for
6 papers · 1 filter
Pixel-wise Smoothing for Certified Robustness against Camera Motion Perturbations
Hanjiang Hu, Zuxin Liu, Linyi Li +2
Deep learning-based visual perception models lack robustness when faced with camera motion perturbations in practice. The current certification process for assessing robustness is…
Learning Shared Safety Constraints from Multi-task Demonstrations
Konwoo Kim, Gokul Swamy, Zuxin Liu +3
Regardless of the particular task we want them to perform in an environment, there are often shared safety constraints we want our agents to respect. For example, regardless of whe…
Understanding V2V Driving Scenarios through Traffic Primitives
Wenshuo Wang, Weiyang Zhang, Ding Zhao
Semantically understanding complex drivers' encountering behavior, wherein two or multiple vehicles are spatially close to each other, does potentially benefit autonomous car's dec…
Cluster Naturalistic Driving Encounters Using Deep Unsupervised Learning
Sisi Li, Wenshuo Wang, Zhaobin Mo +1
Learning knowledge from driving encounters could help self-driving cars make appropriate decisions when driving in complex settings with nearby vehicles engaged. This paper develop…
Learning and Inferring a Driver's Braking Action in Car-Following Scenarios
Wenshuo Wang, Junqiang Xi, Ding Zhao
Accurately predicting and inferring a driver's decision to brake is critical for designing warning systems and avoiding collisions. In this paper we focus on predicting a driver's…
How Much Data is Enough? A Statistical Approach with Case Study on Longitudinal Driving Behavior
Wenshuo Wang, Chang Liu, Ding Zhao
Big data has shown its uniquely powerful ability to reveal, model, and understand driver behaviors. The amount of data affects the experiment cost and conclusions in the analysis.…