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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 276–300 of 514 papers

TitleStatusHype
Accurate and Robust Scale Recovery for Monocular Visual Odometry Based on Plane Geometry—0
Adversarial Attack Attribution: Discovering Attributable Signals in Adversarial ML Attacks—0
A Hybrid Learner for Simultaneous Localization and Mapping—0
Dataset Curation Beyond Accuracy—0
Robust Multi-view Representation Learning—0
Driving through the Lens: Improving Generalization of Learning-based Steering using Simulated Adversarial Examples—0
GINN: Fast GPU-TEE Based Integrity for Neural Network Training—0
An Efficient Generation Method based on Dynamic Curvature of the Reference Curve for Robust Trajectory Planning—0
Achieving Real-Time LiDAR 3D Object Detection on a Mobile Device—0
Pit30M: A Benchmark for Global Localization in the Age of Self-Driving CarsCode1
Convolutional Recurrent Network for Road Boundary Extraction—0
Computer Vision based Accident Detection for Autonomous Vehicles—0
Learning to Localize Using a LiDAR Intensity Map—0
Quantum Optical Convolutional Neural Network: A Novel Image Recognition Framework for Quantum Computing—0
Sampling Training Data for Continual Learning Between Robots and the Cloud—0
An Empirical Review of Adversarial DefensesCode0
Predictive Collision Management for Time and Risk Dependent Path Planning—0
DeepSeqSLAM: A Trainable CNN+RNN for Joint Global Description and Sequence-based Place RecognitionCode1
A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges—0
Deep Learning for Flight Demand Forecasting—0
Motion Prediction on Self-driving Cars: A Review—0
Attribution Preservation in Network Compression for Reliable Network InterpretationCode1
Importance-Aware Semantic Segmentation in Self-Driving with Discrete Wasserstein Training—0
Finding Physical Adversarial Examples for Autonomous Driving with Fast and Differentiable Image CompositingCode0
GOAT: GPU Outsourcing of Deep Learning Training With Asynchronous Probabilistic Integrity Verification Inside Trusted Execution Environment—0
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