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Found 9 papers, 1 papers with code
3D Reasoning for Unsupervised Anomaly Detection in Pediatric WbMRI
Modern deep unsupervised learning methods have shown great promise for detecting diseases across a variety of medical imaging modalities.
Exploring Adversarial Robustness of Multi-Sensor Perception Systems in Self Driving
Yet, there have been limited studies on the adversarial robustness of multi-modal models that fuse LiDAR features with image features.
Adversarial Attacks On Multi-Agent Communication
Growing at a fast pace, modern autonomous systems will soon be deployed at scale, opening up the possibility for cooperative multi-agent systems.
AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehicles
Importantly, by simulating directly from sensor data, we obtain adversarial scenarios that are safety-critical for the full autonomy stack.
Diverse Complexity Measures for Dataset Curation in Self-driving
Our experiments on a wide range of tasks and models show that the proposed curation pipeline is able to select datasets that lead to better generalization and higher performance.
StrObe: Streaming Object Detection from LiDAR Packets
In this paper we propose StrObe, a novel approach that minimizes latency by ingesting LiDAR packets and emitting a stream of detections without waiting for the full sweep to be built.
Learning to Communicate and Correct Pose Errors
Learned communication makes multi-agent systems more effective by aggregating distributed information.
V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Prediction
In this paper, we explore the use of vehicle-to-vehicle (V2V) communication to improve the perception and motion forecasting performance of self-driving vehicles.
Ranked #1 on
3D Object Detection
on OPV2V
Physically Realizable Adversarial Examples for LiDAR Object Detection
Modern autonomous driving systems rely heavily on deep learning models to process point cloud sensory data; meanwhile, deep models have been shown to be susceptible to adversarial attacks with visually imperceptible perturbations.
