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Found 13 papers, 0 papers with code
Controllable Safety-Critical Closed-loop Traffic Simulation via Guided Diffusion
These findings affirm that guided diffusion models provide a robust and versatile foundation for safety-critical, interactive traffic simulation, extending their utility across the broader landscape of autonomous driving.
LLM-Assist: Enhancing Closed-Loop Planning with Language-Based Reasoning
Although planning is a crucial component of the autonomous driving stack, researchers have yet to develop robust planning algorithms that are capable of safely handling the diverse range of possible driving scenarios.
OpEnCam: Lensless Optical Encryption Camera
OpEnCam encrypts the incoming light before capturing it using the modulating ability of optical masks.
LDP-Feat: Image Features with Local Differential Privacy
Modern computer vision services often require users to share raw feature descriptors with an untrusted server.
Learning Phase Mask for Privacy-Preserving Passive Depth Estimation
With over a billion sold each year, cameras are not only becoming ubiquitous and omnipresent, but are driving progress in a wide range of applications such as augmented/virtual reality, robotics, surveillance, security, autonomous navigation and many others.
Divide-and-Conquer for Lane-Aware Diverse Trajectory Prediction
Our second contribution is a novel trajectory prediction framework called ALAN that uses existing lane centerlines as anchors to provide trajectories constrained to the input lanes.
Voting-based Approaches For Differentially Private Federated Learning
Differentially Private Federated Learning (DPFL) is an emerging field with many applications.
SMART: Simultaneous Multi-Agent Recurrent Trajectory Prediction
Our second contribution is a novel method that generates diverse predictions while accounting for scene semantics and multi-agent interactions, with constant-time inference independent of the number of agents.
Towards a MEMS-based Adaptive LIDAR
We present a proof-of-concept LIDAR design that allows adaptive real-time measurements according to dynamically specified measurement patterns.
Revealing Scenes by Inverting Structure from Motion Reconstructions
We present a privacy attack that reconstructs color images of the scene from the point cloud.
Privacy-Preserving Action Recognition using Coded Aperture Videos
We propose a lens-free coded aperture camera system for human action recognition that is privacy-preserving.
Learning Privacy Preserving Encodings through Adversarial Training
We present a framework to learn privacy-preserving encodings of images that inhibit inference of chosen private attributes, while allowing recovery of other desirable information.
Privacy Preserving Optics for Miniature Vision Sensors
Most privacy preserving algorithms for computer vision are applied after image/video data has been captured.
