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 2051–2100 of 6092 papers

TitleStatusHype
Does Physical Adversarial Example Really Matter to Autonomous Driving? Towards System-Level Effect of Adversarial Object Evasion Attack—0
Benchmarking the Robustness of Semantic Segmentation Models—0
A Multi-Layered Approach for Measuring the Simulation-to-Reality Gap of Radar Perception for Autonomous Driving—0
Active-O3: Empowering Multimodal Large Language Models with Active Perception via GRPO—0
DOEPatch: Dynamically Optimized Ensemble Model for Adversarial Patches Generation—0
AMP: Autoregressive Motion Prediction Revisited with Next Token Prediction for Autonomous Driving—0
Do-AIQ: A Design-of-Experiment Approach to Quality Evaluation of AI Mislabel Detection Algorithm—0
Active Legibility in Multiagent Reinforcement Learning—0
DMNR: Unsupervised De-noising of Point Clouds Corrupted by Airborne Particles—0
DMLO: Deep Matching LiDAR Odometry—0
DME-Driver: Integrating Human Decision Logic and 3D Scene Perception in Autonomous Driving—0
DM3D: Distortion-Minimized Weight Pruning for Lossless 3D Object Detection—0
Amortized Q-learning with Model-based Action Proposals for Autonomous Driving on Highways—0
3D Vision-Language Gaussian Splatting—0
FSF-Net: Enhance 4D Occupancy Forecasting with Coarse BEV Scene Flow for Autonomous Driving—0
DL-SLOT: Dynamic Lidar SLAM and Object Tracking Based On Graph Optimization—0
Active Learning from Scene Embeddings for End-to-End Autonomous Driving—0
LAM3D: Leveraging Attention for Monocular 3D Object Detection—0
DLA: Dense-Layer-Analysis for Adversarial Example Detection—0
Divide and Merge: Motion and Semantic Learning in End-to-End Autonomous Driving—0
From Simulation to Real World Maneuver Execution using Deep Reinforcement Learning—0
DiversityGAN: Diversity-Aware Vehicle Motion Prediction via Latent Semantic Sampling—0
Diverse Auto-Curriculum is Critical for Successful Real-World Multiagent Learning Systems—0
Benchmarking Lane-changing Decision-making for Deep Reinforcement Learning—0
AmodalSynthDrive: A Synthetic Amodal Perception Dataset for Autonomous Driving—0
DiVE: DiT-based Video Generation with Enhanced Control—0
Disturbing Reinforcement Learning Agents with Corrupted Rewards—0
Benchmarking Image Sensors Under Adverse Weather Conditions for Autonomous Driving—0
Distribution-aware Goal Prediction and Conformant Model-based Planning for Safe Autonomous Driving—0
A Methodology to Study the Impact of Spiking Neural Network Parameters considering Event-Based Automotive Data—0
Active Learning for Lane Detection: A Knowledge Distillation Approach—0
Distributional Soft Actor-Critic with Harmonic Gradient for Safe and Efficient Autonomous Driving in Multi-lane Scenarios—0
Enabling 3D Object Detection with a Low-Resolution LiDAR—0
FSNet: Redesign Self-Supervised MonoDepth for Full-Scale Depth Prediction for Autonomous Driving—0
Distributionally Robust Trajectory Optimization Under Uncertain Dynamics via Relative Entropy Trust-Regions—0
Distributed Radiance Fields for Edge Video Compression and Metaverse Integration in Autonomous Driving—0
Distributed Learning for Vehicular Dynamic Spectrum Access in Autonomous Driving—0
Distributed Learning and Inference with Compressed Images—0
A Methodology to Identify Cognition Gaps in Visual Recognition Applications Based on Convolutional Neural Networks—0
Active Altruism Learning and Information Sufficiency for Autonomous Driving—0
Distributed Coordination for Multi-Vehicle Systems in the Presence of Misbehaving Vehicles—0
Distortion-Aware Network Pruning and Feature Reuse for Real-time Video Segmentation—0
DistillNeRF: Perceiving 3D Scenes from Single-Glance Images by Distilling Neural Fields and Foundation Model Features—0
Distilling Multi-modal Large Language Models for Autonomous Driving—0
A Methodological Review of Visual Road Recognition Procedures for Autonomous Driving Applications—0
3D StreetUnveiler with Semantic-Aware 2DGS—0
Distilling Monocular Foundation Model for Fine-grained Depth Completion—0
Bench2Drive-R: Turning Real World Data into Reactive Closed-Loop Autonomous Driving Benchmark by Generative Model—0
Behavioural gap assessment of human-vehicle interaction in real and virtual reality-based scenarios in autonomous driving—0
Distance-Normalized Unified Representation for Monocular 3D Object Detection—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