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 276–300 of 6092 papers

TitleStatusHype
LingoQA: Visual Question Answering for Autonomous DrivingCode2
OccNeRF: Advancing 3D Occupancy Prediction in LiDAR-Free EnvironmentsCode2
DrivingGaussian: Composite Gaussian Splatting for Surrounding Dynamic Autonomous Driving ScenesCode2
LMDrive: Closed-Loop End-to-End Driving with Large Language ModelsCode2
Exploring Radar Data Representations in Autonomous Driving: A Comprehensive ReviewCode2
Towards Knowledge-driven Autonomous DrivingCode2
FRNet: Frustum-Range Networks for Scalable LiDAR SegmentationCode2
Open-sourced Data Ecosystem in Autonomous Driving: the Present and FutureCode2
Is Ego Status All You Need for Open-Loop End-to-End Autonomous Driving?Code2
PaSCo: Urban 3D Panoptic Scene Completion with Uncertainty AwarenessCode2
Driving into the Future: Multiview Visual Forecasting and Planning with World Model for Autonomous DrivingCode2
Cam4DOcc: Benchmark for Camera-Only 4D Occupancy Forecasting in Autonomous Driving ApplicationsCode2
Panacea: Panoramic and Controllable Video Generation for Autonomous DrivingCode2
OccWorld: Learning a 3D Occupancy World Model for Autonomous DrivingCode2
NeuRAD: Neural Rendering for Autonomous DrivingCode2
SelfOcc: Self-Supervised Vision-Based 3D Occupancy PredictionCode2
A Survey on Multimodal Large Language Models for Autonomous DrivingCode2
Sparse4D v3: Advancing End-to-End 3D Detection and TrackingCode2
FlashOcc: Fast and Memory-Efficient Occupancy Prediction via Channel-to-Height PluginCode2
Tactics2D: A Highly Modular and Extensible Simulator for Driving Decision-makingCode2
On the Road with GPT-4V(ision): Early Explorations of Visual-Language Model on Autonomous DrivingCode2
Large Trajectory Models are Scalable Motion Predictors and PlannersCode2
RoboDepth: Robust Out-of-Distribution Depth Estimation under CorruptionsCode2
Vision Language Models in Autonomous Driving: A Survey and OutlookCode2
UniPAD: A Universal Pre-training Paradigm 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