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

Showing 826850 of 6092 papers

TitleStatusHype
DVI: Depth Guided Video Inpainting for Autonomous DrivingCode1
Dyna-DM: Dynamic Object-aware Self-supervised Monocular Depth MapsCode1
DUSA: Decoupled Unsupervised Sim2Real Adaptation for Vehicle-to-Everything Collaborative PerceptionCode1
AFDet: Anchor Free One Stage 3D Object DetectionCode1
Dynamic Conditional Imitation Learning for Autonomous DrivingCode1
Dual Radar: A Multi-modal Dataset with Dual 4D Radar for Autonomous DrivingCode1
DualDiff: Dual-branch Diffusion Model for Autonomous Driving with Semantic FusionCode1
Dur360BEV: A Real-world 360-degree Single Camera Dataset and Benchmark for Bird-Eye View Mapping in Autonomous DrivingCode1
DSTIGCN: Deformable Spatial-Temporal Interaction Graph Convolution Network for Pedestrian Trajectory PredictionCode1
Learning Geometry-Guided Depth via Projective Modeling for Monocular 3D Object DetectionCode1
Learning hierarchical behavior and motion planning for autonomous drivingCode1
A Technical Survey and Evaluation of Traditional Point Cloud Clustering Methods for LiDAR Panoptic SegmentationCode1
Learning Multiple Initial Solutions to Optimization ProblemsCode1
DualAD: Dual-Layer Planning for Reasoning in Autonomous DrivingCode1
BEV-MAE: Bird's Eye View Masked Autoencoders for Point Cloud Pre-training in Autonomous Driving ScenariosCode1
Learning to drive from a world on railsCode1
Learning to Fuse Monocular and Multi-view Cues for Multi-frame Depth Estimation in Dynamic ScenesCode1
Dynamic Environment Prediction in Urban Scenes using Recurrent Representation LearningCode1
LEO: Boosting Mixture of Vision Encoders for Multimodal Large Language ModelsCode1
LeTFuser: Light-weight End-to-end Transformer-Based Sensor Fusion for Autonomous Driving with Multi-Task LearningCode1
ATDN vSLAM: An all-through Deep Learning-Based Solution for Visual Simultaneous Localization and MappingCode1
DRL-Based Trajectory Tracking for Motion-Related Modules in Autonomous DrivingCode1
DSEC: A Stereo Event Camera Dataset for Driving ScenariosCode1
BiTraP: Bi-directional Pedestrian Trajectory Prediction with Multi-modal Goal EstimationCode1
Asymmetrical Bi-RNN for pedestrian trajectory encodingCode1
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Benchmark Results

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