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 3976–4000 of 6092 papers

TitleStatusHype
EMDFNet: Efficient Multi-scale and Diverse Feature Network for Traffic Sign Detection—0
Emergency Vehicles Audio Detection and Localization in Autonomous Driving—0
Emergent Communication in Multi-Agent Reinforcement Learning for Future Wireless Networks—0
Emerging Threats in Deep Learning-Based Autonomous Driving: A Comprehensive Survey—0
EMF: Event Meta Formers for Event-based Real-time Traffic Object Detection—0
EMIE-MAP: Large-Scale Road Surface Reconstruction Based on Explicit Mesh and Implicit Encoding—0
EMMA: End-to-End Multimodal Model for Autonomous Driving—0
Empowering Autonomous Driving with Large Language Models: A Safety Perspective—0
Enable an Open Software Defined Mobility Ecosystem through VEC-OF—0
Enabling Efficient Deep Convolutional Neural Network-based Sensor Fusion for Autonomous Driving—0
Enabling Level-4 Autonomous Driving on a Single $1k Off-the-Shelf Card—0
Enabling Next-Generation V2X Perception: Wireless Rigid Body Localization and Tracking—0
Enabling Robots to Communicate their Objectives—0
Self-learned Intelligence for Integrated Decision and Control of Automated Vehicles at Signalized Intersections—0
End-to-End 3D-PointCloud Semantic Segmentation for Autonomous Driving—0
End-to-end Autonomous Driving using Deep Learning: A Systematic Review—0
PillarFlow: End-to-end Birds-eye-view Flow Estimation for Autonomous Driving—0
End-to-End Deep Learning of Lane Detection and Path Prediction for Real-Time Autonomous Driving—0
End-to-end Deep Object Tracking with Circular Loss Function for Rotated Bounding Box—0
End-to-End Deep Reinforcement Learning for Lane Keeping Assist—0
End-to-end Driving in High-Interaction Traffic Scenarios with Reinforcement Learning—0
End-to-end driving simulation via angle branched network—0
End-to-End Driving via Self-Supervised Imitation Learning Using Camera and LiDAR Data—0
End-to-End Interactive Prediction and Planning with Optical Flow Distillation for Autonomous Driving—0
Multi-task UNet architecture for end-to-end 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