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 4826–4850 of 6092 papers

TitleStatusHype
YOLOv11 for Vehicle Detection: Advancements, Performance, and Applications in Intelligent Transportation Systems—0
YOLO-Vehicle-Pro: A Cloud-Edge Collaborative Framework for Object Detection in Autonomous Driving under Adverse Weather Conditions—0
You can have your ensemble and run it too -- Deep Ensembles Spread Over Time—0
"Trust me on this" Explaining Agent Behavior to a Human Terminator—0
You Only Click Once: Single Point Weakly Supervised 3D Instance Segmentation for Autonomous Driving—0
You Only Crash Once: Improved Object Detection for Real-Time, Sim-to-Real Hazardous Terrain Detection and Classification for Autonomous Planetary Landings—0
You Only Look Bottom-Up for Monocular 3D Object Detection—0
YUVMultiNet: Real-time YUV multi-task CNN for autonomous driving—0
Zero-Day Botnet Attack Detection in IoV: A Modular Approach Using Isolation Forests and Particle Swarm Optimization—0
Zero-shot Deep Reinforcement Learning Driving Policy Transfer for Autonomous Vehicles based on Robust Control—0
Zero-shot Hazard Identification in Autonomous Driving: A Case Study on the COOOL Benchmark—0
Zero-Shot Pseudo Labels Generation Using SAM and CLIP for Semi-Supervised Semantic Segmentation—0
Zero-Shot Reinforcement Learning with Deep Attention Convolutional Neural Networks—0
ZeroVO: Visual Odometry with Minimal Assumptions—0
ZFusion: An Effective Fuser of Camera and 4D Radar for 3D Object Perception in Autonomous Driving—0
Learning 2D to 3D Lifting for Object Detection in 3D for Autonomous Vehicles—0
Physics-Inspired Interpretability Of Machine Learning Models—0
PhysNav-DG: A Novel Adaptive Framework for Robust VLM-Sensor Fusion in Navigation Applications—0
SPADE: Sparse Pillar-based 3D Object Detection Accelerator for Autonomous Driving—0
Information Entropy Guided Height-aware Histogram for Quantization-friendly Pillar Feature Encoder—0
PillarHist: A Quantization-aware Pillar Feature Encoder based on Height-aware Histogram—0
*: Improving the 3D detector by introducing Voxel2Pillar feature encoding and extracting multi-scale features—0
PILOT: Efficient Planning by Imitation Learning and Optimisation for Safe Autonomous Driving—0
Pilot-guided Multimodal Semantic Communication for Audio-Visual Event Localization—0
PiShield: A PyTorch Package for Learning with Requirements—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