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

Showing 676700 of 6092 papers

TitleStatusHype
InverseMatrixVT3D: An Efficient Projection Matrix-Based Approach for 3D Occupancy PredictionCode1
Enhancing System-Level Safety in Mixed-Autonomy Platoon via Safe Reinforcement LearningCode1
RSUD20K: A Dataset for Road Scene Understanding In Autonomous DrivingCode1
Synthetic Data Generation Framework, Dataset, and Efficient Deep Model for Pedestrian Intention PredictionCode1
HAIM-DRL: Enhanced Human-in-the-loop Reinforcement Learning for Safe and Efficient Autonomous DrivingCode1
Context-Aware Interaction Network for RGB-T Semantic SegmentationCode1
A Survey on Autonomous Driving Datasets: Statistics, Annotation Quality, and a Future OutlookCode1
Off-Road LiDAR Intensity Based Semantic SegmentationCode1
Bezier Everywhere All at Once: Learning Drivable Lanes as Bezier GraphsCode1
Towards Robust 3D Object Detection with LiDAR and 4D Radar Fusion in Various Weather ConditionsCode1
STIGCN: spatial–temporal interaction‑aware graph convolution network for pedestrian trajectory predictionCode1
FENet: Focusing Enhanced Network for Lane DetectionCode1
Autonomous Driving using Residual Sensor Fusion and Deep Reinforcement LearningCode1
A Unified Generative Framework for Realistic Lidar Simulation in Autonomous Driving SystemsCode1
MonoLSS: Learnable Sample Selection For Monocular 3D DetectionCode1
Parameterized Decision-making with Multi-modal Perception for Autonomous DrivingCode1
TAO-Amodal: A Benchmark for Tracking Any Object AmodallyCode1
M-BEV: Masked BEV Perception for Robust Autonomous DrivingCode1
Object-Aware Domain Generalization for Object DetectionCode1
Regulating Intermediate 3D Features for Vision-Centric Autonomous DrivingCode1
EDA: Evolving and Distinct Anchors for Multimodal Motion PredictionCode1
Personalized Autonomous Driving with Large Language Models: Field ExperimentsCode1
DriveMLM: Aligning Multi-Modal Large Language Models with Behavioral Planning States for Autonomous DrivingCode1
Semi-Supervised Class-Agnostic Motion Prediction with Pseudo Label Regeneration and BEVMixCode1
How Well Does GPT-4V(ision) Adapt to Distribution Shifts? A Preliminary InvestigationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ReasonNetDriving Score79.95Unverified
2InterFuserDriving Score76.18Unverified
3TCPDriving Score75.14Unverified
4TF++ WPDriving Score66.32Unverified
5Learning From All Vehicles (LAV)Driving Score61.85Unverified
6TransFuserDriving Score61.18Unverified
7TransFuser (Reproduced)Driving Score55.04Unverified
8TCP (Reproduced)Driving Score47.91Unverified
9Latent TransFuserDriving Score45.2Unverified
10GRIADDriving Score36.79Unverified
#ModelMetricClaimedVerifiedStatus
1Geometric FusionRC69.17Unverified
2TransFuserRC56.36Unverified
#ModelMetricClaimedVerifiedStatus
1Geometric FusionRC86.91Unverified
2TransFuserRC78.41Unverified