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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 351–375 of 514 papers

TitleStatusHype
Adaptive Compression of Word Embeddings—0
Towards Robust LiDAR-based Perception in Autonomous Driving: General Black-box Adversarial Sensor Attack and Countermeasures—0
Semantic Segmentation With Multi Scale Spatial Attention For Self Driving Cars—0
Application of Neuroevolution in Autonomous Cars—0
AutoCP: Automated Pipelines for Accurate Prediction Intervals—0
To Explain or Not to Explain: A Study on the Necessity of Explanations for Autonomous Vehicles—0
Anomalous Motion Detection on Highway Using Deep LearningCode0
Pixel Invisibility: Detecting Objects Invisible in Color Images—0
Formal Verification of End-to-End Learning in Cyber-Physical Systems: Progress and Challenges—0
Feudal Steering: Hierarchical Learning for Steering Angle Prediction—0
Probabilistic Semantic Mapping for Urban Autonomous Driving Applications—0
Light-in-the-loop: using a photonics co-processor for scalable training of neural networks—0
A Survey on 3D LiDAR Localization for Autonomous Vehicles—0
Monocular Depth Estimators: Vulnerabilities and Attacks—0
Adversarial Attacks and Defense on Texts: A Survey—0
Synthetic Observational Health Data with GANs: from slow adoption to a boom in medical research and ultimately digital twins?—0
Monitoring and Diagnosability of Perception Systems—0
Satellite Navigation for the Age of Autonomy—0
OctSqueeze: Octree-Structured Entropy Model for LiDAR CompressionCode0
Online Monitoring for Neural Network Based Monocular Pedestrian Pose Estimation—0
RUHSNet: 3D Object Detection Using Lidar Data in Real TimeCode0
Attentional Bottleneck: Towards an Interpretable Deep Driving Network—0
Explainable Goal-Driven Agents and Robots -- A Comprehensive Review—0
EMPIR: Ensembles of Mixed Precision Deep Networks for Increased Robustness against Adversarial AttacksCode0
Traffic Lane Detection using FCN—0
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