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 1551–1575 of 6092 papers

TitleStatusHype
The Meeseeks Mesh: Spatially Consistent 3D Adversarial Objects for BEV Detector—0
DriveRX: A Vision-Language Reasoning Model for Cross-Task Autonomous Driving—0
OccLE: Label-Efficient 3D Semantic Occupancy Prediction—0
GaussianFusion: Gaussian-Based Multi-Sensor Fusion for End-to-End Autonomous DrivingCode0
Active-O3: Empowering Multimodal Large Language Models with Active Perception via GRPO—0
Towards Human-Like Trajectory Prediction for Autonomous Driving: A Behavior-Centric Approach—0
CogAD: Cognitive-Hierarchy Guided End-to-End Autonomous Driving—0
CCL-LGS: Contrastive Codebook Learning for 3D Language Gaussian Splatting—0
DiffVLA: Vision-Language Guided Diffusion Planning for Autonomous Driving—0
Zero-Shot Pseudo Labels Generation Using SAM and CLIP for Semi-Supervised Semantic Segmentation—0
Echo Planning for Autonomous Driving: From Current Observations to Future Trajectories and Back—0
DriveX: Omni Scene Modeling for Learning Generalizable World Knowledge in Autonomous Driving—0
ProphetDWM: A Driving World Model for Rolling Out Future Actions and Videos—0
FutureSightDrive: Thinking Visually with Spatio-Temporal CoT for Autonomous Driving—0
RQR3D: Reparametrizing the regression targets for BEV-based 3D object detection—0
Integrating Counterfactual Simulations with Language Models for Explaining Multi-Agent Behaviour—0
InstructPart: Task-Oriented Part Segmentation with Instruction Reasoning—0
CrashAgent: Crash Scenario Generation via Multi-modal Reasoning—0
Plan-R1: Safe and Feasible Trajectory Planning as Language Modeling—0
Distance Estimation in Outdoor Driving Environments Using Phase-only Correlation Method with Event Cameras—0
Clip4Retrofit: Enabling Real-Time Image Labeling on Edge Devices via Cross-Architecture CLIP Distillation—0
SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Driving—0
BadDepth: Backdoor Attacks Against Monocular Depth Estimation in the Physical World—0
Raw2Drive: Reinforcement Learning with Aligned World Models for End-to-End Autonomous Driving (in CARLA v2)—0
DriveMoE: Mixture-of-Experts for Vision-Language-Action Model in End-to-End Autonomous 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