SOTAVerified

Autonomous Vehicles

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 )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 326–350 of 2605 papers

TitleStatusHype
ROAD: The ROad event Awareness Dataset for Autonomous DrivingCode1
Robust Lane Detection via Expanded Self AttentionCode1
Driving Style Representation in Convolutional Recurrent Neural Network Model of Driver IdentificationCode1
Object Tracking by Detection with Visual and Motion CuesCode1
Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road ScenesCode1
Non-linear State-space Model Identification from Video Data using Deep EncodersCode1
Video Deblurring by Fitting to Test DataCode1
Leading Cruise Control in Mixed Traffic Flow: System Modeling, Controllability, and String StabilityCode1
Understanding Bird's-Eye View of Road Semantics using an Onboard CameraCode1
Detecting 32 Pedestrian Attributes for Autonomous VehiclesCode1
Temporal Pyramid Network for Pedestrian Trajectory Prediction with Multi-SupervisionCode1
SS-SFDA : Self-Supervised Source-Free Domain Adaptation for Road Segmentation in Hazardous EnvironmentsCode1
Polarization-driven Semantic Segmentation via Efficient Attention-bridged FusionCode1
Emergent Road Rules In Multi-Agent Driving EnvironmentsCode1
Robust super-resolution depth imaging via a multi-feature fusion deep networkCode1
DeepSeqSLAM: A Trainable CNN+RNN for Joint Global Description and Sequence-based Place RecognitionCode1
GndNet: Fast Ground Plane Estimation and Point Cloud Segmentation for Autonomous VehiclesCode1
CenterFusion: Center-based Radar and Camera Fusion for 3D Object DetectionCode1
End-to-end Lane Shape Prediction with TransformersCode1
Trajectory Planning for Autonomous Vehicles Using Hierarchical Reinforcement LearningCode1
Optimizing Mixed Autonomy Traffic Flow With Decentralized Autonomous Vehicles and Multi-Agent RLCode1
Keep your Eyes on the Lane: Real-time Attention-guided Lane DetectionCode1
Pedestrian Intention Prediction: A Multi-task PerspectiveCode1
RONELD: Robust Neural Network Output Enhancement for Active Lane DetectionCode1
Neural circuit policies enabling auditable autonomyCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1BAAMA3DP22.85—Unverified
2GSNetA3DP20.21—Unverified