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Found 12 papers, 1 papers with code
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.
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
Learning to Communicate and Correct Pose Errors
Learned communication makes multi-agent systems more effective by aggregating distributed information.
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.
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.
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.
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.
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.
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.
MixSim: A Hierarchical Framework for Mixed Reality Traffic Simulation
Towards this goal, we propose to leverage the wealth of interesting scenarios captured in the real world and make them reactive and controllable to enable closed-loop SDV evaluation in what-if situations.
Adv3D: Generating Safety-Critical 3D Objects through Closed-Loop Simulation
In this paper, we propose a framework, Adv3D, that takes real world scenarios and performs closed-loop sensor simulation to evaluate autonomy performance, and finds vehicle shapes that make the scenario more challenging, resulting in autonomy failures and uncomfortable SDV maneuvers.
Learning Realistic Traffic Agents in Closed-loop
Our experiments show that RTR learns more realistic and generalizable traffic simulation policies, achieving significantly better tradeoffs between human-like driving and traffic compliance in both nominal and long-tail scenarios.
