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 59015925 of 6092 papers

TitleStatusHype
Conditional Affordance Learning for Driving in Urban EnvironmentsCode0
G2D: from GTA to DataCode0
Real-time Lane Marker Detection Using Template Matching with RGB-D Camera0
Deep Continuous Conditional Random Fields with Asymmetric Inter-object Constraints for Online Multi-object Tracking0
Conditional Generative Adversarial Network for Structured Domain Adaptation0
3D-RCNN: Instance-Level 3D Object Reconstruction via Render-and-Compare0
DenseASPP for Semantic Segmentation in Street ScenesCode0
LiDAR-Video Driving Dataset: Learning Driving Policies Effectively0
PeerNets: Exploiting Peer Wisdom Against Adversarial AttacksCode0
Foresee: Attentive Future Projections of Chaotic Road Environments with Online Training0
Novel Video Prediction for Large-scale Scene using Optical Flow0
Propagating Confidences through CNNs for Sparse Data RegressionCode0
Semantic Road Layout Understanding by Generative Adversarial Inpainting0
What Face and Body Shapes Can Tell About Height0
A Data-Driven Approach for Autonomous Motion Planning and Control in Off-Road Driving Scenarios0
VisualBackProp for learning using privileged information with CNNs0
End-to-end driving simulation via angle branched network0
Scene Understanding Networks for Autonomous Driving based on Around View Monitoring System0
Counterexample-Guided Data AugmentationCode0
Task Agnostic Robust Learning on Corrupt Outputs by Correlation-Guided Mixture Density NetworksCode0
Hu-Fu: Hardware and Software Collaborative Attack Framework against Neural Networks0
LMNet: Real-time Multiclass Object Detection on CPU using 3D LiDARCode0
DeLS-3D: Deep Localization and Segmentation with a 3D Semantic MapCode0
Improving Predictive Uncertainty Estimation using Dropout -- Hamiltonian Monte Carlo0
Loss-Calibrated Approximate Inference in Bayesian Neural NetworksCode0
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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