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
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
A Hierarchical Architecture for Sequential Decision-Making in Autonomous Driving using Deep Reinforcement LearningCode0
FitAct: Error Resilient Deep Neural Networks via Fine-Grained Post-Trainable Activation FunctionsCode0
VisualBackProp: efficient visualization of CNNsCode0
Semantic Segmentation for Autonomous Driving: Model Evaluation, Dataset Generation, Perspective Comparison, and Real-Time CapabilityCode0
Finding Physical Adversarial Examples for Autonomous Driving with Fast and Differentiable Image CompositingCode0
Translated Skip Connections -- Expanding the Receptive Fields of Fully Convolutional Neural NetworksCode0
Failing to Learn: Autonomously Identifying Perception Failures for Self-driving CarsCode0
Policy Learning with a Language BottleneckCode0
Predicting Physical World Destinations for Commands Given to Self-Driving CarsCode0
Semantic Understanding of Foggy Scenes with Purely Synthetic DataCode0
Pre-Training LiDAR-Based 3D Object Detectors Through ColorizationCode0
Deep convolutional neural networks for pedestrian detectionCode0
Extract-Transform-Load for Video StreamsCode0
DAD++: Improved Data-free Test Time Adversarial DefenseCode0
Total Recall: Understanding Traffic Signs using Deep Hierarchical Convolutional Neural NetworksCode0
Sequential Decision Making with Expert Demonstrations under Unobserved HeterogeneityCode0
Context-Aware Full Body Anonymization using Text-to-Image Diffusion ModelsCode0
Visual Global Localization with a Hybrid WNN-CNN ApproachCode0
Quantifying Urban Canopy Cover with Deep Convolutional Neural NetworksCode0
A0C: Alpha Zero in Continuous Action SpaceCode0
Towards better data discovery and collection with flow-based programmingCode0
Explaining How a Deep Neural Network Trained with End-to-End Learning Steers a CarCode0
End-to-end Multi-Modal Multi-Task Vehicle Control for Self-Driving Cars with Visual PerceptionCode0
Classification of Time-Series Data Using Boosted Decision TreesCode0
CARL-D: A vision benchmark suite and large scale dataset for vehicle detection and scene segmentationCode0
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