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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 151–200 of 514 papers

TitleStatusHype
DME-Driver: Integrating Human Decision Logic and 3D Scene Perception in Autonomous Driving—0
Nav-Q: Quantum Deep Reinforcement Learning for Collision-Free Navigation of Self-Driving CarsCode0
Traffic Sign Recognition Using Local Vision Transformer—0
LaksNet: an end-to-end deep learning model for self-driving cars in Udacity simulator—0
Pre-Training LiDAR-Based 3D Object Detectors Through ColorizationCode0
F^2AT: Feature-Focusing Adversarial Training via Disentanglement of Natural and Perturbed Patterns—0
Towards Long-Range 3D Object Detection for Autonomous Vehicles—0
Extract-Transform-Load for Video StreamsCode0
CPIPS: Learning to Preserve Perceptual Distances in End-to-End Image Compression—0
Gray-box Adversarial Attack of Deep Reinforcement Learning-based Trading Agents—0
Detecting and Mitigating System-Level Anomalies of Vision-Based ControllersCode0
Unsupervised Domain Adaptation for Self-Driving from Past Traversal FeaturesCode0
DAD++: Improved Data-free Test Time Adversarial DefenseCode0
R-C-P Method: An Autonomous Volume Calculation Method Using Image Processing and Machine Vision—0
Balancing Transparency and Risk: The Security and Privacy Risks of Open-Source Machine Learning Models—0
Improving Realistic Worst-Case Performance of NVCiM DNN Accelerators through Training with Right-Censored Gaussian Noise—0
Small, but important: Traffic light proposals for detecting small traffic lights and beyond—0
An Estimator for the Sensitivity to Perturbations of Deep Neural Networks—0
Introducing Risk Shadowing For Decisive and Comfortable Behavior Planning—0
Human Trajectory Forecasting with Explainable Behavioral Uncertainty—0
Benchmarking Robustness of AI-Enabled Multi-sensor Fusion Systems: Challenges and Opportunities—0
Introduction to Latent Variable Energy-Based Models: A Path Towards Autonomous Machine Intelligence—0
AutoExp: A multidisciplinary, multi-sensor framework to evaluate human activities in self-driving cars—0
Pedestrian detection with high-resolution event camera—0
Improving Defensive Distillation using Teacher Assistant—0
Evil from Within: Machine Learning Backdoors through Hardware Trojans—0
Self-Supervised Learning based Depth Estimation from Monocular ImagesCode0
Detection of Uncertainty in Exceedance of Threshold (DUET): An Adversarial Patch Localizer—0
Importance Filtering with Risk Models for Complex Driving Situations—0
Do we need entire training data for adversarial training?—0
Reinforced Labels: Multi-Agent Deep Reinforcement Learning for Point-Feature Label Placement—0
S-NeRF: Neural Radiance Fields for Street Views—0
Towards Measuring Ethicality of an Intelligent Assistive System—0
TrainSim: A Railway Simulation Framework for LiDAR and Camera Dataset Generation—0
Run-Time Monitors Design for Adaptive Radar Systems: A Practical Framework—0
OTFS -- A Mathematical Foundation for Communication and Radar Sensing in the Delay-Doppler Domain—0
Referential communication in heterogeneous communities of pre-trained visual deep networksCode0
A sliced-Wasserstein distance-based approach for out-of-class-distribution detection—0
Methods and Tools for Monitoring Driver's Behavior—0
Towards Reconciling Usability and Usefulness of Explainable AI Methodologies—0
Seamless Multimodal Biometrics for Continuous Personalised Wellbeing Monitoring—0
Deep Depth Estimation From Thermal ImageCode0
AmbieGen: A Search-based Framework for Autonomous Systems TestingCode0
Hybrid Deep Reinforcement Learning and Planning for Safe and Comfortable Automated Driving—0
Benchmark for Uncertainty & Robustness in Self-Supervised LearningCode0
Monocular 3D Object Detection using Multi-Stage Approaches with Attention and Slicing aided hyper inference—0
Lightweight Monocular Depth Estimation—0
Cognitive Level-k Meta-Learning for Safe and Pedestrian-Aware Autonomous Driving—0
It is not "accuracy vs. explainability" -- we need both for trustworthy AI systems—0
AI Model Utilization Measurements For Finding Class Encoding Patterns—0
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