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 4976–5000 of 6092 papers

TitleStatusHype
Vehicular Visible Light Positioning for Collision Avoidance and Platooning: A SurveyCode0
SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous DrivingCode2
Continual Unsupervised Domain Adaptation for Semantic SegmentationCode0
The efficacy of Neural Planning Metrics: A meta-analysis of PKL on nuScenesCode1
RADIATE: A Radar Dataset for Automotive Perception in Bad WeatherCode1
Multiple Future Prediction Leveraging Synthetic Trajectories—0
Tracklets Predicting Based Adaptive Graph Tracking—0
Finding Physical Adversarial Examples for Autonomous Driving with Fast and Differentiable Image CompositingCode0
Value Alignment Verification—0
HPERL: 3D Human Pose Estimation from RGB and LiDARCode0
A Closed-Loop System for Improving Annotation Quality and Efficiency—0
DynaSLAM II: Tightly-Coupled Multi-Object Tracking and SLAM—0
Automotive Radar Interference Mitigation with Unfolded Robust PCA based on Residual Overcomplete Auto-Encoder Blocks—0
Development of Open Informal Dataset Affecting Autonomous Driving—0
Auto-calibration Method Using Stop Signs for Urban Autonomous Driving Applications—0
Resource Allocation Complexity Analysis for Relay-based Multihop V2V Communication—0
Continuous Safety Verification of Neural Networks—0
Hierarchical Attention Learning of Scene Flow in 3D Point Clouds—0
Pedestrian Trajectory Prediction with Convolutional Neural Networks—0
Increasing the Robustness of Semantic Segmentation Models with Painting-by-Numbers—0
Investigating the Robustness of Artificial Intelligent Algorithms with Mixture Experiments—0
CurbScan: Curb Detection and Tracking Using Multi-Sensor Fusion—0
Understanding Local Robustness of Deep Neural Networks under Natural VariationsCode0
Double Targeted Universal Adversarial PerturbationsCode0
Motion Prediction Using Temporal Inception ModuleCode1
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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