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 2326–2350 of 6092 papers

TitleStatusHype
CamNet: Coarse-to-Fine Retrieval for Camera Re-Localization—0
An Online Spatial-Temporal Graph Trajectory Planner for Autonomous Vehicles—0
End-to-End Deep Learning of Lane Detection and Path Prediction for Real-Time Autonomous Driving—0
PillarFlow: End-to-end Birds-eye-view Flow Estimation for Autonomous Driving—0
CAML: Collaborative Auxiliary Modality Learning for Multi-Agent Systems—0
End-to-end Autonomous Driving using Deep Learning: A Systematic Review—0
Anomaly Detection in Radar Data Using PointNets—0
End-to-End 3D-PointCloud Semantic Segmentation for Autonomous Driving—0
Camera Calibration with Pose Guidance—0
Adaptive Stress Testing: Finding Likely Failure Events with Reinforcement Learning—0
A 2.5D Vehicle Odometry Estimation for Vision Applications—0
3D-FCT: Simultaneous 3D Object Detection and Tracking Using Feature Correlation—0
Self-learned Intelligence for Integrated Decision and Control of Automated Vehicles at Signalized Intersections—0
Enabling Robots to Communicate their Objectives—0
Camera-based vehicle velocity estimation from monocular video—0
Enabling Next-Generation V2X Perception: Wireless Rigid Body Localization and Tracking—0
Enabling Level-4 Autonomous Driving on a Single $1k Off-the-Shelf Card—0
Camera-Based Localization and Enhanced Normalized Mutual Information—0
Anomaly-Aware Semantic Segmentation via Style-Aligned OoD Augmentation—0
Enabling Efficient Deep Convolutional Neural Network-based Sensor Fusion for Autonomous Driving—0
Enable an Open Software Defined Mobility Ecosystem through VEC-OF—0
Empowering Autonomous Driving with Large Language Models: A Safety Perspective—0
Camera Agnostic Two-Head Network for Ego-Lane Inference—0
Anomaly-Aware Semantic Segmentation by Leveraging Synthetic-Unknown Data—0
Adaptive Stream Processing on Edge Devices through Active Inference—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