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 51–75 of 514 papers

TitleStatusHype
DiffSF: Diffusion Models for Scene Flow EstimationCode1
Multi-Object Tracking with Camera-LiDAR Fusion for Autonomous Driving—0
Improved LiDAR Odometry and Mapping using Deep Semantic Segmentation and Novel Outliers Detection—0
Reinforcement Learning Based Oscillation Dampening: Scaling up Single-Agent RL algorithms to a 100 AV highway field operational test—0
Distilling Adversarial Robustness Using Heterogeneous Teachers—0
Multi-class Temporal Logic Neural Networks—0
Enhanced Deep Q-Learning for 2D Self-Driving Cars: Implementation and Evaluation on a Custom Track Environment—0
Spiking CenterNet: A Distillation-boosted Spiking Neural Network for Object Detection—0
Cross-Layer Performance Evaluation of C-V2X—0
Enhancing Surveillance Camera FOV Quality via Semantic Line Detection and Classification with Deep Hough Transform—0
DME-Driver: Integrating Human Decision Logic and 3D Scene Perception in Autonomous Driving—0
Autonomous Driving using Residual Sensor Fusion and Deep Reinforcement LearningCode1
Nav-Q: Quantum Deep Reinforcement Learning for Collision-Free Navigation of Self-Driving CarsCode0
Tactics2D: A Highly Modular and Extensible Simulator for Driving Decision-makingCode2
Deep Perspective Transformation Based Vehicle Localization on Bird's Eye ViewCode1
Traffic Sign Recognition Using Local Vision Transformer—0
Dialogue-based generation of self-driving simulation scenarios using Large Language ModelsCode1
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
Extract-Transform-Load for Video StreamsCode0
Towards Long-Range 3D Object Detection for Autonomous Vehicles—0
CPIPS: Learning to Preserve Perceptual Distances in End-to-End Image Compression—0
Maximum diffusion reinforcement learningCode1
Gray-box Adversarial Attack of Deep Reinforcement Learning-based Trading Agents—0
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