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 5626–5650 of 6092 papers

TitleStatusHype
HCRMP: A LLM-Hinted Contextual Reinforcement Learning Framework for Autonomous Driving—0
HDMapGen: A Hierarchical Graph Generative Model of High Definition Maps—0
HeAL3D: Heuristical-enhanced Active Learning for 3D Object Detection—0
Heatmap-based Vanishing Point boosts Lane Detection—0
HeatNet: Bridging the Day-Night Domain Gap in Semantic Segmentation with Thermal Images—0
HE-Drive: Human-Like End-to-End Driving with Vision Language Models—0
HeightFormer: A Multilevel Interaction and Image-adaptive Classification-regression Network for Monocular Height Estimation with Aerial Images—0
HeightFormer: A Semantic Alignment Monocular 3D Object Detection Method from Roadside Perspective—0
HeightFormer: Explicit Height Modeling without Extra Data for Camera-only 3D Object Detection in Bird's Eye View—0
Heterogeneous Graph-based Trajectory Prediction using Local Map Context and Social Interactions—0
Heuristic Optimization of Amplifier Reconfiguration Process for Autonomous Driving Optical Networks—0
HGCN-GJS: Hierarchical Graph Convolutional Network with Groupwise Joint Sampling for Trajectory Prediction—0
HGNET: A Hierarchical Feature Guided Network for Occupancy Flow Field Prediction—0
Hi-ALPS -- An Experimental Robustness Quantification of Six LiDAR-based Object Detection Systems for Autonomous Driving—0
Hidden Backdoor Attack against Semantic Segmentation Models—0
Hidden Footprints: Learning Contextual Walkability from 3D Human Trails—0
Hierarchical and Decoupled BEV Perception Learning Framework for Autonomous Driving—0
Hierarchical Attention Learning of Scene Flow in 3D Point Clouds—0
Hierarchical End-to-End Autonomous Driving: Integrating BEV Perception with Deep Reinforcement Learning—0
Hierarchical Game-Theoretic Planning for Autonomous Vehicles—0
Hierarchical Insights: Exploiting Structural Similarities for Reliable 3D Semantic Segmentation—0
Hierarchical Instance Mixing across Domains in Aerial Segmentation—0
Developing Driving Strategies Efficiently: A Skill-Based Hierarchical Reinforcement Learning Approach—0
Hierarchical Model-Based Imitation Learning for Planning in Autonomous Driving—0
Hierarchical Program-Triggered Reinforcement Learning Agents For Automated 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