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Self-Driving Cars

Self-driving cars : the task of making a car that can drive itself without human guidance.

( Image credit: Learning a Driving Simulator )

Papers

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Showing 351–400 of 514 papers

TitleStatusHype
Adaptive Compression of Word Embeddings—0
Towards Robust LiDAR-based Perception in Autonomous Driving: General Black-box Adversarial Sensor Attack and Countermeasures—0
Semantic Segmentation With Multi Scale Spatial Attention For Self Driving Cars—0
Application of Neuroevolution in Autonomous Cars—0
AutoCP: Automated Pipelines for Accurate Prediction Intervals—0
To Explain or Not to Explain: A Study on the Necessity of Explanations for Autonomous Vehicles—0
Anomalous Motion Detection on Highway Using Deep LearningCode0
Pixel Invisibility: Detecting Objects Invisible in Color Images—0
Formal Verification of End-to-End Learning in Cyber-Physical Systems: Progress and Challenges—0
Feudal Steering: Hierarchical Learning for Steering Angle Prediction—0
Probabilistic Semantic Mapping for Urban Autonomous Driving Applications—0
Light-in-the-loop: using a photonics co-processor for scalable training of neural networks—0
A Survey on 3D LiDAR Localization for Autonomous Vehicles—0
Monocular Depth Estimators: Vulnerabilities and Attacks—0
Adversarial Attacks and Defense on Texts: A Survey—0
Synthetic Observational Health Data with GANs: from slow adoption to a boom in medical research and ultimately digital twins?—0
Monitoring and Diagnosability of Perception Systems—0
Satellite Navigation for the Age of Autonomy—0
OctSqueeze: Octree-Structured Entropy Model for LiDAR CompressionCode0
Online Monitoring for Neural Network Based Monocular Pedestrian Pose Estimation—0
RUHSNet: 3D Object Detection Using Lidar Data in Real TimeCode0
Attentional Bottleneck: Towards an Interpretable Deep Driving Network—0
Explainable Goal-Driven Agents and Robots -- A Comprehensive Review—0
EMPIR: Ensembles of Mixed Precision Deep Networks for Increased Robustness against Adversarial AttacksCode0
Traffic Lane Detection using FCN—0
Federated Machine Learning for Intelligent IoT via Reconfigurable Intelligent Surface—0
Detecting Adversarial Examples in Learning-Enabled Cyber-Physical Systems using Variational Autoencoder for Regression—0
Uncertainty depth estimation with gated images for 3D reconstruction—0
Image-based OoD-Detector Principles on Graph-based Input Data in Human Action Recognition—0
"Who is Driving around Me?" Unique Vehicle Instance Classification using Deep Neural Features—0
AdvMS: A Multi-source Multi-cost Defense Against Adversarial Attacks—0
FRSign: A Large-Scale Traffic Light Dataset for Autonomous Trains—0
A Novel Graph based Trajectory Predictor with Pseudo Oracle—0
Real-time Out-of-distribution Detection in Learning-Enabled Cyber-Physical Systems—0
Stochastic Finite State Control of POMDPs with LTL Specifications—0
An Abstraction Model for Semantic Segmentation Algorithms—0
High Resolution Millimeter Wave Imaging For Self-Driving Cars—0
Quantifying Urban Canopy Cover with Deep Convolutional Neural NetworksCode0
Automatic Testing With Reusable Adversarial Agents—0
Attacking Optical FlowCode0
Depth-wise Decomposition for Accelerating Separable Convolutions in Efficient Convolutional Neural Networks—0
Teaching Vehicles to Anticipate: A Systematic Study on Probabilistic Behavior Prediction Using Large Data Sets—0
Conditional Driving from Natural Language Instructions—0
A Hybrid Compact Neural Architecture for Visual Place RecognitionCode0
Semantic Understanding of Foggy Scenes with Purely Synthetic DataCode0
GLADAS: Gesture Learning for Advanced Driver Assistance Systems—0
Learning with Protection: Rejection of Suspicious Samples under Adversarial Environment—0
A*3D Dataset: Towards Autonomous Driving in Challenging EnvironmentsCode0
Towards Assume-Guarantee Profiles for Autonomous Vehicles—0
Context Aware Road-user Importance Estimation (iCARE)—0
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