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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 476–500 of 514 papers

TitleStatusHype
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
Brain Inspired Cognitive Model with Attention for Self-Driving Cars—0
Hardware for Machine Learning: Challenges and OpportunitiesCode0
Detecting Unexpected Obstacles for Self-Driving Cars: Fusing Deep Learning and Geometric Modeling—0
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