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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 451–475 of 514 papers

TitleStatusHype
OctSqueeze: Octree-Structured Entropy Model for LiDAR CompressionCode0
On a Formal Model of Safe and Scalable Self-driving CarsCode0
On Learning Informative Trajectory Embeddings for Imitation, Classification and RegressionCode0
A Feedback Linearized Model Predictive Control Strategy for Input-Constrained Self-Driving CarsCode0
TraffSign: Multilingual Traffic Signboard Text Detection and Recognition for Urdu and EnglishCode0
On Single Source Robustness in Deep Fusion ModelsCode0
On the Importance of Regularisation & Auxiliary Information in OOD DetectionCode0
On the Interaction between Autonomous Mobility on Demand Systems and Power Distribution Networks -- An Optimal Power Flow ApproachCode0
DeepXplore: Automated Whitebox Testing of Deep Learning SystemsCode0
An Empirical Review of Adversarial DefensesCode0
Input Prioritization for Testing Neural NetworksCode0
Hardware for Machine Learning: Challenges and OpportunitiesCode0
A Minimal Closed-Form Solution for Multi-Perspective Pose Estimation using Points and LinesCode0
SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR SequencesCode0
SpecRepair: Counter-Example Guided Safety Repair of Deep Neural NetworksCode0
Giving Commands to a Self-Driving Car: How to Deal with Uncertain Situations?Code0
Imitation Learning with Human Eye Gaze via Multi-Objective PredictionCode0
Deep Depth Estimation From Thermal ImageCode0
PaDPaF: Partial Disentanglement with Partially-Federated GANsCode0
Self-Supervised Learning based Depth Estimation from Monocular ImagesCode0
Unsupervised Domain Adaptation for Self-Driving from Past Traversal FeaturesCode0
AmbieGen: A Search-based Framework for Autonomous Systems TestingCode0
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A SurveyCode0
PifPaf: Composite Fields for Human Pose EstimationCode0
Fixes That Fail: Self-Defeating Improvements in Machine-Learning SystemsCode0
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