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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 401–450 of 514 papers

TitleStatusHype
Conditional Driving from Natural Language Instructions—0
Context Aware Road-user Importance Estimation (iCARE)—0
Self-Supervised Deep Learning on Point Clouds by Reconstructing Space—0
Continual Hyperbolic Learning of Instances and Classes—0
CPIPS: Learning to Preserve Perceptual Distances in End-to-End Image Compression—0
Cross-Dataset Collaborative Learning for Semantic Segmentation in Autonomous Driving—0
Cross-Layer Performance Evaluation of C-V2X—0
Cross-Modal Analysis of Human Detection for Robotics: An Industrial Case Study—0
CrowdDriven: A New Challenging Dataset for Outdoor Visual Localization—0
Dataset Curation Beyond Accuracy—0
Deep Bayesian Estimation for Dynamic Treatment Regimes with a Long Follow-up Time—0
Deep Generative Models for Vehicle Speed Trajectories—0
Deep Learning for Flight Demand Forecasting—0
Deep Learning in the Automotive Industry: Recent Advances and Application Examples—0
Learning Tractable Probabilistic Models for Moral Responsibility and Blame—0
Leveraging RGB-D Data with Cross-Modal Context Mining for Glass Surface Detection—0
Depth-wise Decomposition for Accelerating Separable Convolutions in Efficient Convolutional Neural Networks—0
Detecting Adversarial Examples in Learning-Enabled Cyber-Physical Systems using Variational Autoencoder for Regression—0
Detecting Adversarial Samples Using Density Ratio Estimates—0
Detecting Drivable Area for Self-driving Cars: An Unsupervised Approach—0
Detecting Unexpected Obstacles for Self-Driving Cars: Fusing Deep Learning and Geometric Modeling—0
Detection of Dataset Shifts in Learning-Enabled Cyber-Physical Systems using Variational Autoencoder for Regression—0
Detection of Uncertainty in Exceedance of Threshold (DUET): An Adversarial Patch Localizer—0
Development of Open Informal Dataset Affecting Autonomous Driving—0
Diff-Net: Image Feature Difference based High-Definition Map Change Detection for Autonomous Driving—0
DISC: Dataset for Analyzing Driving Styles In Simulated Crashes for Mixed Autonomy—0
Discrete Control in Real-World Driving Environments using Deep Reinforcement Learning—0
Distilling Adversarial Robustness Using Heterogeneous Teachers—0
DME-Driver: Integrating Human Decision Logic and 3D Scene Perception in Autonomous Driving—0
Do we need entire training data for adversarial training?—0
Drive Video Analysis for the Detection of Traffic Near-Miss Incidents—0
Driving Experience Transfer Method for End-to-End Control of Self-Driving Cars—0
Driving through the Lens: Improving Generalization of Learning-based Steering using Simulated Adversarial Examples—0
Efficient Image Evidence Analysis of CNN Classification Results—0
Efficient Outdoor 3D Point Cloud Semantic Segmentation for Critical Road Objects and Distributed Contexts—0
Efficient Perception, Planning, and Control Algorithm for Vision-Based Automated Vehicles—0
Ego-Lane Analysis System (ELAS): Dataset and Algorithms—0
Embedded Development Boards for Edge-AI: A Comprehensive Report—0
End-to-end sensor modeling for LiDAR Point Cloud—0
ENet-21: An Optimized light CNN Structure for Lane Detection—0
Enhanced Behavioral Cloning Based self-driving Car Using Transfer Learning—0
Enhanced Deep Q-Learning for 2D Self-Driving Cars: Implementation and Evaluation on a Custom Track Environment—0
Enhanced Neuromorphic Semantic Segmentation Latency through Stream Event—0
Enhancing Apparent Personality Trait Analysis with Cross-Modal Embeddings—0
Enhancing Autonomous Driving Safety with Collision Scenario Integration—0
Enhancing Surveillance Camera FOV Quality via Semantic Line Detection and Classification with Deep Hough Transform—0
Ensemble Methods as a Defense to Adversarial Perturbations Against Deep Neural Networks—0
EnSyth: A Pruning Approach to Synthesis of Deep Learning Ensembles—0
GAS: Generating Fast and Accurate Surrogate Models for Autonomous Vehicle Systems—0
Evaluating Uncertainty Quantification in End-to-End Autonomous Driving Control—0
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