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 926950 of 6092 papers

TitleStatusHype
CAE-LO: LiDAR Odometry Leveraging Fully Unsupervised Convolutional Auto-Encoder for Interest Point Detection and Feature DescriptionCode1
CAFE-AD: Cross-Scenario Adaptive Feature Enhancement for Trajectory Planning in Autonomous DrivingCode1
CaFNet: A Confidence-Driven Framework for Radar Camera Depth EstimationCode1
Curricular Subgoals for Inverse Reinforcement LearningCode1
Learning Geometry-Guided Depth via Projective Modeling for Monocular 3D Object DetectionCode1
CSFlow: Learning Optical Flow via Cross Strip Correlation for Autonomous DrivingCode1
High-level camera-LiDAR fusion for 3D object detection with machine learningCode1
A Survey on Autonomous Driving Datasets: Statistics, Annotation Quality, and a Future OutlookCode1
HiLoTs: High-Low Temporal Sensitive Representation Learning for Semi-Supervised LiDAR Segmentation in Autonomous DrivingCode1
Ctrl-V: Higher Fidelity Video Generation with Bounding-Box Controlled Object MotionCode1
Learning hierarchical behavior and motion planning for autonomous drivingCode1
Cal or No Cal? -- Real-Time Miscalibration Detection of LiDAR and Camera SensorsCode1
Crowdsourced 3D Mapping: A Combined Multi-View Geometry and Self-Supervised Learning ApproachCode1
Adversarial Training of Self-supervised Monocular Depth Estimation against Physical-World AttacksCode1
Camera-based 3D Semantic Scene Completion with Sparse Guidance NetworkCode1
Homography Loss for Monocular 3D Object DetectionCode1
A Survey of World Models for Autonomous DrivingCode1
3D-MiniNet: Learning a 2D Representation from Point Clouds for Fast and Efficient 3D LIDAR Semantic SegmentationCode1
Cross-modal Learning for Domain Adaptation in 3D Semantic SegmentationCode1
Learning Interpretable, High-Performing Policies for Autonomous DrivingCode1
HS3-Bench: A Benchmark and Strong Baseline for Hyperspectral Semantic Segmentation in Driving ScenariosCode1
CRN: Camera Radar Net for Accurate, Robust, Efficient 3D PerceptionCode1
CROON: Automatic Multi-LiDAR Calibration and Refinement Method in Road SceneCode1
Learning Across Domains and Devices: Style-Driven Source-Free Domain Adaptation in Clustered Federated LearningCode1
CR3DT: Camera-RADAR Fusion for 3D Detection and TrackingCode1
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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