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 701725 of 6092 papers

TitleStatusHype
How Well Does GPT-4V(ision) Adapt to Distribution Shifts? A Preliminary InvestigationCode1
BAT: Behavior-Aware Human-Like Trajectory Prediction for Autonomous DrivingCode1
NuScenes-MQA: Integrated Evaluation of Captions and QA for Autonomous Driving Datasets using Markup AnnotationsCode1
M3SOT: Multi-frame, Multi-field, Multi-space 3D Single Object TrackingCode1
Camera-based 3D Semantic Scene Completion with Sparse Guidance NetworkCode1
Bootstrapping Autonomous Driving Radars with Self-Supervised LearningCode1
LaMPilot: An Open Benchmark Dataset for Autonomous Driving with Language Model ProgramsCode1
GPT-4 Enhanced Multimodal Grounding for Autonomous Driving: Leveraging Cross-Modal Attention with Large Language ModelsCode1
Reason2Drive: Towards Interpretable and Chain-based Reasoning for Autonomous DrivingCode1
WoVoGen: World Volume-aware Diffusion for Controllable Multi-camera Driving Scene GenerationCode1
COTR: Compact Occupancy TRansformer for Vision-based 3D Occupancy PredictionCode1
MUVO: A Multimodal World Model with Spatial Representations for Autonomous DrivingCode1
Human-Centric Autonomous Systems With LLMs for User Command ReasoningCode1
PPAD: Iterative Interactions of Prediction and Planning for End-to-end Autonomous DrivingCode1
Quantized Distillation: Optimizing Driver Activity Recognition Models for Resource-Constrained EnvironmentsCode1
Lidar Annotation Is All You NeedCode1
Exploring Event-based Human Pose Estimation with 3D Event RepresentationsCode1
Augmenting Lane Perception and Topology Understanding with Standard Definition Navigation MapsCode1
Towards Calibrated Robust Fine-Tuning of Vision-Language ModelsCode1
Flow-Based Feature Fusion for Vehicle-Infrastructure Cooperative 3D Object DetectionCode1
Learning Cooperative Trajectory Representations for Motion ForecastingCode1
HEDNet: A Hierarchical Encoder-Decoder Network for 3D Object Detection in Point CloudsCode1
Three Pillars improving Vision Foundation Model Distillation for LidarCode1
Navigating Data Heterogeneity in Federated Learning A Semi-Supervised Federated Object DetectionCode1
BM2CP: Efficient Collaborative Perception with LiDAR-Camera ModalitiesCode1
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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