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 4526–4550 of 6092 papers

TitleStatusHype
DenseTNT: Waymo Open Dataset Motion Prediction Challenge 1st Place SolutionCode1
OffRoadTranSeg: Semi-Supervised Segmentation using Transformers on OffRoad environments—0
Image Classification with CondenseNeXt for ARM-Based Computing PlatformsCode0
RSN: Range Sparse Net for Efficient, Accurate LiDAR 3D Object Detection—0
Autonomous Driving Strategies at Intersections: Scenarios, State-of-the-Art, and Future Outlooks—0
Multi-Modal 3D Object Detection in Autonomous Driving: a Survey—0
Transformer Meets Convolution: A Bilateral Awareness Network for Semantic Segmentation of Very Fine Resolution Urban Scene ImagesCode2
FusionPainting: Multimodal Fusion with Adaptive Attention for 3D Object DetectionCode1
Lossless Point Cloud Attribute Compression with Normal-based Intra Prediction—0
Imitation Learning: Progress, Taxonomies and Challenges—0
Uncertainty-Aware Model-Based Reinforcement Learning with Application to Autonomous Driving—0
Arbitrary Angle of Arrival in Radar Target Simulation—0
NuPlan: A closed-loop ML-based planning benchmark for autonomous vehiclesCode1
SeqNetVLAD vs PointNetVLAD: Image Sequence vs 3D Point Clouds for Day-Night Place RecognitionCode1
MEAL: Manifold Embedding-based Active Learning—0
EPMF: Efficient Perception-aware Multi-sensor Fusion for 3D Semantic SegmentationCode1
Spatio-Temporal Multi-Task Learning Transformer for Joint Moving Object Detection and Segmentation—0
MODETR: Moving Object Detection with Transformers—0
3D Object Detection for Autonomous Driving: A SurveyCode1
One Million Scenes for Autonomous Driving: ONCE DatasetCode1
SODA10M: A Large-Scale 2D Self/Semi-Supervised Object Detection Dataset for Autonomous Driving—0
RangeIoUDet: Range Image Based Real-Time 3D Object Detector Optimized by Intersection Over Union—0
Projecting Your View Attentively: Monocular Road Scene Layout Estimation via Cross-View TransformationCode1
Safe Local Motion Planning With Self-Supervised Freespace ForecastingCode1
PointAugmenting: Cross-Modal Augmentation for 3D Object Detection—0
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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