SOTAVerified

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 451–500 of 514 papers

TitleStatusHype
Driving Experience Transfer Method for End-to-End Control of Self-Driving Cars—0
Evaluation of Neural Networks for Image Recognition Applications: Designing a 0-1 MILP Model of a CNN to create adversarials—0
JuncNet: A Deep Neural Network for Road Junction Disambiguation for Autonomous Vehicles—0
Total Recall: Understanding Traffic Signs using Deep Hierarchical Convolutional Neural NetworksCode0
YouTube AV 50K: An Annotated Corpus for Comments in Autonomous VehiclesCode0
A Minimal Closed-Form Solution for Multi-Perspective Pose Estimation using Points and LinesCode0
Hybrid Scene Compression for Visual Localization—0
A Game-Based Approximate Verification of Deep Neural Networks with Provable GuaranteesCode0
Semantic Instance Meets Salient Object: Study on Video Semantic Salient Instance Segmentation—0
Modular Vehicle Control for Transferring Semantic Information Between Weather Conditions Using GANsCode0
Ego-Lane Analysis System (ELAS): Dataset and Algorithms—0
Motion Planning NetworksCode0
PointFlowNet: Learning Representations for Rigid Motion Estimation from Point Clouds—0
Performance Evaluation of Deep Learning Networks for Semantic Segmentation of Traffic Stereo-Pair Images—0
SoPhie: An Attentive GAN for Predicting Paths Compliant to Social and Physical ConstraintsCode0
Sequential Attacks on Agents for Long-Term Adversarial Goals—0
A0C: Alpha Zero in Continuous Action SpaceCode0
Trusted Neural Networks for Safety-Constrained Autonomous Control—0
Towards Autonomous Reinforcement Learning: Automatic Setting of Hyper-parameters using Bayesian Optimization—0
Visual Global Localization with a Hybrid WNN-CNN ApproachCode0
Semantic Adversarial Deep Learning—0
Vehicle Communication Strategies for Simulated Highway Driving—0
Drive Video Analysis for the Detection of Traffic Near-Miss Incidents—0
Analyzing Self-Driving Cars on Twitter—0
Event-based Vision meets Deep Learning on Steering Prediction for Self-driving Cars—0
Cluster Naturalistic Driving Encounters Using Deep Unsupervised Learning—0
TSViz: Demystification of Deep Learning Models for Time-Series AnalysisCode0
End-to-end Multi-Modal Multi-Task Vehicle Control for Self-Driving Cars with Visual PerceptionCode0
Efficient Image Evidence Analysis of CNN Classification Results—0
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A SurveyCode0
Artificial Intelligence and Statistics—0
Beyond Grand Theft Auto V for Training, Testing and Enhancing Deep Learning in Self Driving Cars—0
Feature-Guided Black-Box Safety Testing of Deep Neural Networks—0
Modeling the Resource Requirements of Convolutional Neural Networks on Mobile Devices—0
Pseudo-labels for Supervised Learning on Dynamic Vision Sensor Data, Applied to Object Detection under Ego-motion—0
Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges—0
Mitigating Evasion Attacks to Deep Neural Networks via Region-based Classification—0
Ensemble Methods as a Defense to Adversarial Perturbations Against Deep Neural Networks—0
Playing with Embeddings : Evaluating embeddings for Robot Language Learning through MUD Games—0
3D Visual Perception for Self-Driving Cars using a Multi-Camera System: Calibration, Mapping, Localization, and Obstacle DetectionCode0
On a Formal Model of Safe and Scalable Self-driving CarsCode0
Fast, Accurate Thin-Structure Obstacle Detection for Autonomous Mobile Robots—0
Failing to Learn: Autonomously Identifying Perception Failures for Self-driving CarsCode0
DeepXplore: Automated Whitebox Testing of Deep Learning SystemsCode0
Detecting Adversarial Samples Using Density Ratio Estimates—0
Detecting Drivable Area for Self-driving Cars: An Unsupervised Approach—0
Explaining How a Deep Neural Network Trained with End-to-End Learning Steers a CarCode0
Interpretable Learning for Self-Driving Cars by Visualizing Causal Attention—0
Towards Closing the Energy Gap Between HOG and CNN Features for Embedded Vision—0
CHAOS: A Parallelization Scheme for Training Convolutional Neural Networks on Intel Xeon Phi—0
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