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 826–850 of 6092 papers

TitleStatusHype
TiGDistill-BEV: Multi-view BEV 3D Object Detection via Target Inner-Geometry Learning DistillationCode1
MR-Occ: Efficient Camera-LiDAR 3D Semantic Occupancy Prediction Using Hierarchical Multi-Resolution Voxel Representation—0
Leveraging Edge Intelligence and LLMs to Advance 6G-Enabled Internet of Automated Defense Vehicles—0
Multi-Modality Driven LoRA for Adverse Condition Depth Estimation—0
Leveraging Large Language Models for Enhancing Autonomous Vehicle Perception—0
DepthMamba with Adaptive Fusion—0
Zero-shot Hazard Identification in Autonomous Driving: A Case Study on the COOOL Benchmark—0
DrivingWorld: Constructing World Model for Autonomous Driving via Video GPTCode3
MLLM-SUL: Multimodal Large Language Model for Semantic Scene Understanding and Localization in Traffic ScenariosCode0
DriveEditor: A Unified 3D Information-Guided Framework for Controllable Object Editing in Driving ScenesCode1
HV-BEV: Decoupling Horizontal and Vertical Feature Sampling for Multi-View 3D Object DetectionCode0
TSceneJAL: Joint Active Learning of Traffic Scenes for 3D Object DetectionCode0
Evaluating the Adversarial Robustness of Detection Transformers—0
Parallel Neural Computing for Scene Understanding from LiDAR Perception in Autonomous RacingCode0
GDM4MMIMO: Generative Diffusion Models for Massive MIMO Communications—0
Generating Traffic Scenarios via In-Context Learning to Learn Better Motion PlannerCode1
AA-SGAN: Adversarially Augmented Social GAN with Synthetic DataCode0
Causal Composition Diffusion Model for Closed-loop Traffic Generation—0
OLiDM: Object-aware LiDAR Diffusion Models for Autonomous Driving—0
Feature Based Methods in Domain Adaptation for Object Detection: A Review Paper—0
DeepMF: Deep Motion Factorization for Closed-Loop Safety-Critical Driving Scenario Simulation—0
NumbOD: A Spatial-Frequency Fusion Attack Against Object DetectorsCode0
Lightweight Design and Optimization methods for DCNNs: Progress and Futures—0
Application of Multimodal Large Language Models in Autonomous Driving—0
Towards Selection and Transition Between Behavior-Based Neural Networks for Automated Driving—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