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 451475 of 6092 papers

TitleStatusHype
FBSNet: A Fast Bilateral Symmetrical Network for Real-Time Semantic SegmentationCode1
FEND: A Future Enhanced Distribution-Aware Contrastive Learning Framework for Long-tail Trajectory PredictionCode1
Flow4D: Leveraging 4D Voxel Network for LiDAR Scene Flow EstimationCode1
Exploring the Devil in Graph Spectral Domain for 3D Point Cloud AttacksCode1
Extending Large Vision-Language Model for Diverse Interactive Tasks in Autonomous DrivingCode1
Exploring Simple 3D Multi-Object Tracking for Autonomous DrivingCode1
Exploring Data Aggregation in Policy Learning for Vision-Based Urban Autonomous DrivingCode1
A proposal for Multimodal Emotion Recognition using aural transformers and Action Units on RAVDESS datasetCode1
Exploring Map-based Features for Efficient Attention-based Vehicle Motion PredictionCode1
Explaining Autonomous Driving Actions with Visual Question AnsweringCode1
An LSTM-Based Autonomous Driving Model Using Waymo Open DatasetCode1
Experimental Comparison of Global Motion Planning Algorithms for Wheeled Mobile RobotsCode1
Exploiting the Complementarity of 2D and 3D Networks to Address Domain-Shift in 3D Semantic SegmentationCode1
Evaluation of Differentially Constrained Motion Models for Graph-Based Trajectory PredictionCode1
Evaluating the Robustness of Semantic Segmentation for Autonomous Driving against Real-World Adversarial Patch AttacksCode1
Event-aided Semantic Scene CompletionCode1
Ev-3DOD: Pushing the Temporal Boundaries of 3D Object Detection with Event CamerasCode1
Annealed Winner-Takes-All for Motion ForecastingCode1
Explainability of Point Cloud Neural Networks Using SMILE: Statistical Model-Agnostic Interpretability with Local ExplanationsCode1
Explainable Object-induced Action Decision for Autonomous VehiclesCode1
Estimating the Magnitude and Phase of Automotive Radar Signals under Multiple Interference Sources with Fully Convolutional NetworksCode1
Evaluating Adversarial Attacks on Driving Safety in Vision-Based Autonomous VehiclesCode1
Exploring Navigation Maps for Learning-Based Motion PredictionCode1
Exploring Point-BEV Fusion for 3D Point Cloud Object Tracking with TransformerCode1
Asynchronous Blob Tracker for Event CamerasCode1
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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