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 1951–1975 of 6092 papers

TitleStatusHype
Better Monocular 3D Detectors with LiDAR from the PastCode1
UniMix: Towards Domain Adaptive and Generalizable LiDAR Semantic Segmentation in Adverse Weather—0
MOSE: Boosting Vision-based Roadside 3D Object Detection with Scene Cues—0
Human Detection from 4D Radar Data in Low-Visibility Field Conditions—0
Detecting Every Object from EventsCode1
Residual Chain Prediction for Autonomous Driving Path Planning—0
Light the Night: A Multi-Condition Diffusion Framework for Unpaired Low-Light Enhancement in Autonomous Driving—0
MonoTAKD: Teaching Assistant Knowledge Distillation for Monocular 3D Object DetectionCode1
Prompting Multi-Modal Tokens to Enhance End-to-End Autonomous Driving Imitation Learning with LLMs—0
HawkDrive: A Transformer-driven Visual Perception System for Autonomous Driving in Night SceneCode0
Co-Occ: Coupling Explicit Feature Fusion with Volume Rendering Regularization for Multi-Modal 3D Semantic Occupancy Prediction—0
Automated Lane Change Behavior Prediction and Environmental Perception Based on SLAM Technology—0
Exploring Probabilistic Models for Semi-supervised Learning—0
Scaling Motion Forecasting Models with Ensemble Distillation—0
Language-Guided Instance-Aware Domain-Adaptive Panoptic Segmentation—0
Dendrites endow artificial neural networks with accurate, robust and parameter-efficient learning—0
A Methodology to Study the Impact of Spiking Neural Network Parameters considering Event-Based Automotive Data—0
Is CLIP the main roadblock for fine-grained open-world perception?Code2
CORP: A Multi-Modal Dataset for Campus-Oriented Roadside Perception Tasks—0
LiDAR4D: Dynamic Neural Fields for Novel Space-time View LiDAR SynthesisCode3
HENet: Hybrid Encoding for End-to-end Multi-task 3D Perception from Multi-view CamerasCode2
LidarDM: Generative LiDAR Simulation in a Generated WorldCode2
Ego-Motion Aware Target Prediction Module for Robust Multi-Object TrackingCode0
AD4RL: Autonomous Driving Benchmarks for Offline Reinforcement Learning with Value-based Dataset—0
TCLC-GS: Tightly Coupled LiDAR-Camera Gaussian Splatting for 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