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 1751–1775 of 6092 papers

TitleStatusHype
Towards Accurate Ego-lane Identification with Early Time Series Classification—0
Can We Trust Embodied Agents? Exploring Backdoor Attacks against Embodied LLM-based Decision-Making Systems—0
SCaRL- A Synthetic Multi-Modal Dataset for Autonomous Driving—0
Benchmarking and Improving Bird's Eye View Perception Robustness in Autonomous DrivingCode3
DINO-SD: Champion Solution for ICRA 2024 RoboDepth Challenge—0
Collective Perception Datasets for Autonomous Driving: A Comprehensive Review—0
A re-calibration method for object detection with multi-modal alignment bias in autonomous driving—0
MultiOOD: Scaling Out-of-Distribution Detection for Multiple ModalitiesCode2
BehaviorGPT: Smart Agent Simulation for Autonomous Driving with Next-Patch Prediction—0
Vista: A Generalizable Driving World Model with High Fidelity and Versatile ControllabilityCode7
A Comparative Study on Multi-task Uncertainty Quantification in Semantic Segmentation and Monocular Depth Estimation—0
GaussianFormer: Scene as Gaussians for Vision-Based 3D Semantic Occupancy PredictionCode4
Hardness-Aware Scene Synthesis for Semi-Supervised 3D Object DetectionCode0
DiffuBox: Refining 3D Object Detection with Point DiffusionCode1
Improving 3D Occupancy Prediction through Class-balancing Loss and Multi-scale Representation—0
Automatic parking planning control method based on improved A* algorithm—0
Automated Parking Planning with Vision-Based BEV Approach—0
Talk to Parallel LiDARs: A Human-LiDAR Interaction Method Based on 3D Visual Grounding—0
3D Annotation-Free Learning by Distilling 2D Open-Vocabulary Segmentation Models for Autonomous DrivingCode1
SMART: Scalable Multi-agent Real-time Motion Generation via Next-token PredictionCode3
Label-efficient Semantic Scene Completion with Scribble AnnotationsCode1
Continuously Learning, Adapting, and Improving: A Dual-Process Approach to Autonomous DrivingCode2
NeuroGauss4D-PCI: 4D Neural Fields and Gaussian Deformation Fields for Point Cloud InterpolationCode1
An Empirical Study of Training State-of-the-Art LiDAR Segmentation Models—0
MagicDrive3D: Controllable 3D Generation for Any-View Rendering in Street Scenes—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