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 3801–3825 of 6092 papers

TitleStatusHype
Distance Estimation in Outdoor Driving Environments Using Phase-only Correlation Method with Event Cameras—0
Distance-Normalized Unified Representation for Monocular 3D Object Detection—0
Distilling Monocular Foundation Model for Fine-grained Depth Completion—0
Distilling Multi-modal Large Language Models for Autonomous Driving—0
DistillNeRF: Perceiving 3D Scenes from Single-Glance Images by Distilling Neural Fields and Foundation Model Features—0
Distortion-Aware Network Pruning and Feature Reuse for Real-time Video Segmentation—0
Distributed Coordination for Multi-Vehicle Systems in the Presence of Misbehaving Vehicles—0
Distributed Learning and Inference with Compressed Images—0
Distributed Learning for Vehicular Dynamic Spectrum Access in Autonomous Driving—0
Distributed Radiance Fields for Edge Video Compression and Metaverse Integration in Autonomous Driving—0
Distributionally Robust Trajectory Optimization Under Uncertain Dynamics via Relative Entropy Trust-Regions—0
Distributional Soft Actor-Critic with Harmonic Gradient for Safe and Efficient Autonomous Driving in Multi-lane Scenarios—0
Distribution-aware Goal Prediction and Conformant Model-based Planning for Safe Autonomous Driving—0
Disturbing Reinforcement Learning Agents with Corrupted Rewards—0
DiVE: DiT-based Video Generation with Enhanced Control—0
Diverse Auto-Curriculum is Critical for Successful Real-World Multiagent Learning Systems—0
DiversityGAN: Diversity-Aware Vehicle Motion Prediction via Latent Semantic Sampling—0
Divide and Merge: Motion and Semantic Learning in End-to-End Autonomous Driving—0
DLA: Dense-Layer-Analysis for Adversarial Example Detection—0
DL-SLOT: Dynamic Lidar SLAM and Object Tracking Based On Graph Optimization—0
DM3D: Distortion-Minimized Weight Pruning for Lossless 3D Object Detection—0
DME-Driver: Integrating Human Decision Logic and 3D Scene Perception in Autonomous Driving—0
DMLO: Deep Matching LiDAR Odometry—0
DMNR: Unsupervised De-noising of Point Clouds Corrupted by Airborne Particles—0
Do-AIQ: A Design-of-Experiment Approach to Quality Evaluation of AI Mislabel Detection Algorithm—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