Autonomous Vehicles
536 papers with code • 1 benchmarks • 27 datasets
Autonomous vehicles is the task of making a vehicle that can guide itself without human conduction.
Many of the state-of-the-art results can be found at more general task pages such as 3D Object Detection and Semantic Segmentation.
( Image credit: GSNet: Joint Vehicle Pose and Shape Reconstruction with Geometrical and Scene-aware Supervision )
Benchmarks
These leaderboards are used to track progress in Autonomous Vehicles
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Libraries
Use these libraries to find Autonomous Vehicles models and implementationsDatasets
CARLA
AirSim
INTERACTION Dataset
Talk2Car
RadarScenes
ROAD
Drive&Act
ApolloCar3D
Argoverse 2 Motion Forecasting
Subtasks
Most implemented papers
AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles
Microsoft/AirSim
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Developing and testing algorithms for autonomous vehicles in real world is an expensive and time consuming process.
Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges
Mobile devices such as smartphones and autonomous vehicles increasingly rely on deep neural networks (DNNs) to execute complex inference tasks such as image classification and speech recognition, among others.
Flow: A Modular Learning Framework for Mixed Autonomy Traffic
Furthermore, in single-lane traffic, a small neural network control law with only local observation is found to eliminate stop-and-go traffic - surpassing all known model-based controllers to achieve near-optimal performance - and generalize to out-of-distribution traffic densities.
nuScenes: A multimodal dataset for autonomous driving
Most autonomous vehicles, however, carry a combination of cameras and range sensors such as lidar and radar.
Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes
ydhongHIT/DDRNet
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The proposed deep dual-resolution networks (DDRNets) are composed of two deep branches between which multiple bilateral fusions are performed.
LEAF: A Benchmark for Federated Settings
Modern federated networks, such as those comprised of wearable devices, mobile phones, or autonomous vehicles, generate massive amounts of data each day.
Counterfactual Multi-Agent Policy Gradients
COMA uses a centralised critic to estimate the Q-function and decentralised actors to optimise the agents' policies.
DynaSLAM: Tracking, Mapping and Inpainting in Dynamic Scenes
And it also estimates a map of the static parts of the scene, which is a must for long-term applications in real-world environments.
LiDAR-Camera Calibration using 3D-3D Point correspondences
With the advent of autonomous vehicles, LiDAR and cameras have become an indispensable combination of sensors.
Joint 3D Proposal Generation and Object Detection from View Aggregation
kujason/avod
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We present AVOD, an Aggregate View Object Detection network for autonomous driving scenarios.
