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 5751–5775 of 6092 papers

TitleStatusHype
Deep Reinforcement Learning framework for Autonomous DrivingCode0
Sky-GVIO: an enhanced GNSS/INS/Vision navigation with FCN-based sky-segmentation in urban canyonCode0
KAN-RCBEVDepth: A multi-modal fusion algorithm in object detection for autonomous drivingCode0
Deep Reinforcement Learning for Autonomous DrivingCode0
Finding Physical Adversarial Examples for Autonomous Driving with Fast and Differentiable Image CompositingCode0
Slice-based Learning: A Programming Model for Residual Learning in Critical Data SlicesCode0
A Generative Framework for Low-Cost Result Validation of Machine Learning-as-a-Service InferenceCode0
What Matters in Autonomous Driving Anomaly Detection: A Weakly Supervised HorizonCode0
FedRAV: Hierarchically Federated Region-Learning for Traffic Object Classification of Autonomous VehiclesCode0
C2F-TP: A Coarse-to-Fine Denoising Framework for Uncertainty-Aware Trajectory PredictionCode0
Building Lane-Level Maps from Aerial ImagesCode0
Deep recurrent Gaussian process with variational Sparse Spectrum approximationCode0
Variational Voxel Pseudo Image TrackingCode0
SMARLA: A Safety Monitoring Approach for Deep Reinforcement Learning AgentsCode0
FedPAW: Federated Learning with Personalized Aggregation Weights for Urban Vehicle Speed PredictionCode0
BTSeg: Barlow Twins Regularization for Domain Adaptation in Semantic SegmentationCode0
An efficient solution for semantic segmentation: ShuffleNet V2 with atrous separable convolutionsCode0
Marginally calibrated response distributions for end-to-end learning in autonomous drivingCode0
Automotive Speed Estimation: Sensor Types and Error Characteristics from OBD-II to ADASCode0
Uncertainty-Aware AB3DMOT by Variational 3D Object DetectionCode0
AutoGraph: Predicting Lane Graphs from Traffic ObservationsCode0
Mamba-VA: A Mamba-based Approach for Continuous Emotion Recognition in Valence-Arousal SpaceCode0
Federated Learning with Heterogeneous Data Handling for Robust Vehicular Object DetectionCode0
Federated Learning for Privacy-Preserving Feedforward Control in Multi-Agent SystemsCode0
MambaOcc: Visual State Space Model for BEV-based Occupancy Prediction with Local Adaptive ReorderingCode0
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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