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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 251–300 of 514 papers

TitleStatusHype
Why AI is Harder Than We ThinkCode1
Imagining The Road Ahead: Multi-Agent Trajectory Prediction via Differentiable SimulationCode1
Scaling Beyond Bandwidth Limitations: Wireless Control With Stability Guarantees Under Overload—0
Detection of Dataset Shifts in Learning-Enabled Cyber-Physical Systems using Variational Autoencoder for Regression—0
Trajectory Prediction with Latent Belief Energy-Based ModelCode1
An NCAP-like Safety Indicator for Self-Driving Cars—0
Sparse Auxiliary Networks for Unified Monocular Depth Prediction and CompletionCode2
Exploiting Playbacks in Unsupervised Domain Adaptation for 3D Object Detection—0
Automated and Autonomous Experiment in Electron and Scanning Probe Microscopy—0
Fixes That Fail: Self-Defeating Improvements in Machine-Learning SystemsCode0
Cross-Dataset Collaborative Learning for Semantic Segmentation in Autonomous Driving—0
Self-Supervised Steering Angle Prediction for Vehicle Control Using Visual Odometry—0
Sparse LiDAR and Stereo Fusion (SLS-Fusion) for Depth Estimationand 3D Object Detection—0
Dynamic Fusion Module Evolves Drivable Area and Road Anomaly Detection: A Benchmark and AlgorithmsCode1
OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal AssociationCode2
Sequential Place Learning: Heuristic-Free High-Performance Long-Term Place RecognitionCode1
AutoPreview: A Framework for Autopilot Behavior Understanding—0
Imitation Learning with Human Eye Gaze via Multi-Objective PredictionCode0
4D Panoptic LiDAR SegmentationCode1
Accelerating Recursive Partition-Based Causal Structure Learning—0
Towards AIOps in Edge Computing Environments—0
(AF)2-S3Net: Attentive Feature Fusion with Adaptive Feature Selection for Sparse Semantic Segmentation Network—0
Autonomous Navigation through intersections with Graph ConvolutionalNetworks and Conditional Imitation Learning for Self-driving Cars—0
Towards Universal Physical Attacks On Cascaded Camera-Lidar 3D Object Detection Models—0
Object Tracking by Detection with Visual and Motion CuesCode1
Accurate and Robust Scale Recovery for Monocular Visual Odometry Based on Plane Geometry—0
Adversarial Attack Attribution: Discovering Attributable Signals in Adversarial ML Attacks—0
A Hybrid Learner for Simultaneous Localization and Mapping—0
Dataset Curation Beyond Accuracy—0
Robust Multi-view Representation Learning—0
Driving through the Lens: Improving Generalization of Learning-based Steering using Simulated Adversarial Examples—0
GINN: Fast GPU-TEE Based Integrity for Neural Network Training—0
An Efficient Generation Method based on Dynamic Curvature of the Reference Curve for Robust Trajectory Planning—0
Achieving Real-Time LiDAR 3D Object Detection on a Mobile Device—0
Pit30M: A Benchmark for Global Localization in the Age of Self-Driving CarsCode1
Convolutional Recurrent Network for Road Boundary Extraction—0
Computer Vision based Accident Detection for Autonomous Vehicles—0
Learning to Localize Using a LiDAR Intensity Map—0
Quantum Optical Convolutional Neural Network: A Novel Image Recognition Framework for Quantum Computing—0
Sampling Training Data for Continual Learning Between Robots and the Cloud—0
An Empirical Review of Adversarial DefensesCode0
Predictive Collision Management for Time and Risk Dependent Path Planning—0
DeepSeqSLAM: A Trainable CNN+RNN for Joint Global Description and Sequence-based Place RecognitionCode1
A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges—0
Deep Learning for Flight Demand Forecasting—0
Motion Prediction on Self-driving Cars: A Review—0
Attribution Preservation in Network Compression for Reliable Network InterpretationCode1
Importance-Aware Semantic Segmentation in Self-Driving with Discrete Wasserstein Training—0
Finding Physical Adversarial Examples for Autonomous Driving with Fast and Differentiable Image CompositingCode0
GOAT: GPU Outsourcing of Deep Learning Training With Asynchronous Probabilistic Integrity Verification Inside Trusted Execution Environment—0
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