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

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Showing 151–160 of 2605 papers

TitleStatusHype
CMDFusion: Bidirectional Fusion Network with Cross-modality Knowledge Distillation for LIDAR Semantic SegmentationCode1
COOPERNAUT: End-to-End Driving with Cooperative Perception for Networked VehiclesCode1
aiMotive Dataset: A Multimodal Dataset for Robust Autonomous Driving with Long-Range PerceptionCode1
CoTV: Cooperative Control for Traffic Light Signals and Connected Autonomous Vehicles using Deep Reinforcement LearningCode1
CRAT-Pred: Vehicle Trajectory Prediction with Crystal Graph Convolutional Neural Networks and Multi-Head Self-AttentionCode1
AirSim: High-Fidelity Visual and Physical Simulation for Autonomous VehiclesCode1
Adaptive-Mask Fusion Network for Segmentation of Drivable Road and Negative Obstacle With Untrustworthy FeaturesCode1
CSFlow: Learning Optical Flow via Cross Strip Correlation for Autonomous DrivingCode1
CityRefer: Geography-aware 3D Visual Grounding Dataset on City-scale Point Cloud DataCode1
CoMAL: Collaborative Multi-Agent Large Language Models for Mixed-Autonomy TrafficCode1
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Benchmark Results

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