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 251–260 of 2605 papers

TitleStatusHype
PanoFlow: Learning 360° Optical Flow for Surrounding Temporal UnderstandingCode1
Adversarial Attack and Defense of YOLO Detectors in Autonomous Driving ScenariosCode1
CRAT-Pred: Vehicle Trajectory Prediction with Crystal Graph Convolutional Neural Networks and Multi-Head Self-AttentionCode1
Learning Interpretable, High-Performing Policies for Autonomous DrivingCode1
ObjectSeeker: Certifiably Robust Object Detection against Patch Hiding Attacks via Patch-agnostic MaskingCode1
CSFlow: Learning Optical Flow via Cross Strip Correlation for Autonomous DrivingCode1
Multi-Agent Trajectory Prediction With Heterogeneous Edge-Enhanced Graph Attention NetworkCode1
CoTV: Cooperative Control for Traffic Light Signals and Connected Autonomous Vehicles using Deep Reinforcement LearningCode1
Graph Convolution-Based Deep Reinforcement Learning for Multi-Agent Decision-Making in Mixed Traffic EnvironmentsCode1
HydraFusion: Context-Aware Selective Sensor Fusion for Robust and Efficient Autonomous Vehicle PerceptionCode1
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Benchmark Results

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