Self-Driving Cars
166 papers with code • 0 benchmarks • 15 datasets
Self-driving cars : the task of making a car that can drive itself without human guidance.
( Image credit: Learning a Driving Simulator )
Benchmarks
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Libraries
Use these libraries to find Self-Driving Cars models and implementationsLatest papers
DiffSF: Diffusion Models for Scene Flow Estimation
Aiming at improving accuracy while additionally providing an estimate for uncertainty, we propose DiffSF that combines transformer-based scene flow estimation with denoising diffusion models.
Autonomous Driving using Residual Sensor Fusion and Deep Reinforcement Learning
This paper proposes a novel approach by integrating sensor fusion with deep reinforcement learning, specifically the Soft Actor-Critic (SAC) algorithm, to develop an optimal control policy for self-driving cars.
Nav-Q: Quantum Deep Reinforcement Learning for Collision-Free Navigation of Self-Driving Cars
In this work, we propose Nav-Q, the first quantum-supported DRL algorithm for CFN of self-driving cars, that leverages quantum computation for improving the training performance without the requirement for onboard quantum hardware.
Tactics2D: A Reinforcement Learning Environment Library with Generative Scenarios for Driving Decision-making
For access to the source code and participation in discussions, visit the official GitHub page for Tactcis2D at https://github. com/WoodOxen/Tactics2D.
Deep Perspective Transformation Based Vehicle Localization on Bird's Eye View
An accurate understanding of a self-driving vehicle's surrounding environment is crucial for its navigation system.
Dialogue-based generation of self-driving simulation scenarios using Large Language Models
Simulation is an invaluable tool for developing and evaluating controllers for self-driving cars.
Pre-Training LiDAR-Based 3D Object Detectors Through Colorization
Accurate 3D object detection and understanding for self-driving cars heavily relies on LiDAR point clouds, necessitating large amounts of labeled data to train.
Extract-Transform-Load for Video Streams
We find that no current system sufficiently fulfills both needs and therefore propose Skyscraper, a system tailored to V-ETL.
Maximum diffusion reinforcement learning
The assumption that data are independent and identically distributed underpins all machine learning.
PanopticNeRF-360: Panoramic 3D-to-2D Label Transfer in Urban Scenes
Moreover, PanopticNeRF-360 enables omnidirectional rendering of high-fidelity, multi-view and spatiotemporally consistent appearance, semantic and instance labels.