SOTAVerified

Autonomous Driving

Autonomous driving is the task of driving a vehicle 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: Exploring the Limitations of Behavior Cloning for Autonomous Driving)

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 4626–4650 of 6092 papers

TitleStatusHype
Model Guided Road Intersection Classification—0
On the Role of Sensor Fusion for Object Detection in Future Vehicular Networks—0
Optimal Cost Design for Model Predictive ControlCode1
Learnable Online Graph Representations for 3D Multi-Object Tracking—0
Efficient LiDAR Odometry for Autonomous Driving—0
FCOS3D: Fully Convolutional One-Stage Monocular 3D Object DetectionCode1
DANNet: A One-Stage Domain Adaptation Network for Unsupervised Nighttime Semantic SegmentationCode1
Patch Shortcuts: Interpretable Proxy Models Efficiently Find Black-Box Vulnerabilities—0
VM-MODNet: Vehicle Motion aware Moving Object Detection for Autonomous Driving—0
Imagining The Road Ahead: Multi-Agent Trajectory Prediction via Differentiable SimulationCode1
Tackling Variabilities in Autonomous Driving—0
BEVDetNet: Bird's Eye View LiDAR Point Cloud based Real-time 3D Object Detection for Autonomous Driving—0
Multi-task Learning with Attention for End-to-end Autonomous Driving—0
FIERY: Future Instance Prediction in Bird's-Eye View from Surround Monocular CamerasCode1
Robust Sensor Fusion Algorithms Against Voice Command Attacks in Autonomous VehiclesCode0
Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Dataset—0
Multi-Modal Fusion Transformer for End-to-End Autonomous DrivingCode2
Plants Don't Walk on the Street: Common-Sense Reasoning for Reliable Semantic Segmentation—0
Learning on Hardware: A Tutorial on Neural Network Accelerators and Co-Processors—0
Learning Interpretable End-to-End Vision-Based Motion Planning for Autonomous Driving with Optical Flow Distillation—0
End-to-End Interactive Prediction and Planning with Optical Flow Distillation for Autonomous Driving—0
Self-Supervised Pillar Motion Learning for Autonomous DrivingCode1
Motion Prediction Performance Analysis for Autonomous Driving Systems and the Effects of Tracking Noise—0
Temporally smooth online action detection using cycle-consistent future anticipationCode0
Spatiotemporal Deformable Scene Graphs for Complex Activity Detection—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ReasonNetDriving Score79.95—Unverified
2InterFuserDriving Score76.18—Unverified
3TCPDriving Score75.14—Unverified
4TF++ WPDriving Score66.32—Unverified
5Learning From All Vehicles (LAV)Driving Score61.85—Unverified
6TransFuserDriving Score61.18—Unverified
7TransFuser (Reproduced)Driving Score55.04—Unverified
8TCP (Reproduced)Driving Score47.91—Unverified
9Latent TransFuserDriving Score45.2—Unverified
10GRIADDriving Score36.79—Unverified
#ModelMetricClaimedVerifiedStatus
1Geometric FusionRC69.17—Unverified
2TransFuserRC56.36—Unverified
#ModelMetricClaimedVerifiedStatus
1Geometric FusionRC86.91—Unverified
2TransFuserRC78.41—Unverified