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 251–275 of 6092 papers

TitleStatusHype
Lightning NeRF: Efficient Hybrid Scene Representation for Autonomous DrivingCode2
PointCore: Efficient Unsupervised Point Cloud Anomaly Detector Using Local-Global FeaturesCode2
OccFusion: Multi-Sensor Fusion Framework for 3D Semantic Occupancy PredictionCode2
On the Road to Portability: Compressing End-to-End Motion Planner for Autonomous DrivingCode2
Deep learning for 3D human pose estimation and mesh recovery: A surveyCode2
A Cognitive-Based Trajectory Prediction Approach for Autonomous DrivingCode2
ICP-Flow: LiDAR Scene Flow Estimation with ICPCode2
EMIFF: Enhanced Multi-scale Image Feature Fusion for Vehicle-Infrastructure Cooperative 3D Object DetectionCode2
PCA-Bench: Evaluating Multimodal Large Language Models in Perception-Cognition-Action ChainCode2
RAG-Driver: Generalisable Driving Explanations with Retrieval-Augmented In-Context Learning in Multi-Modal Large Language ModelCode2
PC-NeRF: Parent-Child Neural Radiance Fields Using Sparse LiDAR Frames in Autonomous Driving EnvironmentsCode2
Diffusion-ES: Gradient-free Planning with Diffusion for Autonomous Driving and Zero-Shot Instruction FollowingCode2
LaneGraph2Seq: Lane Topology Extraction with Language Model via Vertex-Edge Encoding and Connectivity EnhancementCode2
MF-MOS: A Motion-Focused Model for Moving Object SegmentationCode2
ADMap: Anti-disturbance framework for reconstructing online vectorized HD mapCode2
Self-supervised Learning of LiDAR 3D Point Clouds via 2D-3D Neural CalibrationCode2
Data-Centric Evolution in Autonomous Driving: A Comprehensive Survey of Big Data System, Data Mining, and Closed-Loop TechnologiesCode2
LangProp: A code optimization framework using Large Language Models applied to drivingCode2
WidthFormer: Toward Efficient Transformer-based BEV View TransformationCode2
RoboFusion: Towards Robust Multi-Modal 3D Object Detection via SAMCode2
Holistic Autonomous Driving Understanding by Bird's-Eye-View Injected Multi-Modal Large ModelsCode2
MAPLM: A Real-World Large-Scale Vision-Language Benchmark for Map and Traffic Scene UnderstandingCode2
Visual Point Cloud Forecasting enables Scalable Autonomous DrivingCode2
Fully Sparse 3D Occupancy PredictionCode2
LaneSegNet: Map Learning with Lane Segment Perception for Autonomous DrivingCode2
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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