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 1751–1775 of 6092 papers

TitleStatusHype
BEV-SAN: Accurate BEV 3D Object Detection via Slice Attention Networks—0
AdaOcc: Adaptive-Resolution Occupancy Prediction—0
DUDES: Deep Uncertainty Distillation using Ensembles for Semantic Segmentation—0
DuEqNet: Dual-Equivariance Network in Outdoor 3D Object Detection for Autonomous Driving—0
3DArticCyclists: Generating Synthetic Articulated 8D Pose-Controllable Cyclist Data for Computer Vision Applications—0
BEVPose: Unveiling Scene Semantics through Pose-Guided Multi-Modal BEV Alignment—0
DriveGPT: Scaling Autoregressive Behavior Models for Driving—0
DriveGPT4-V2: Harnessing Large Language Model Capabilities for Enhanced Closed-Loop Autonomous Driving—0
BEV-MODNet: Monocular Camera based Bird's Eye View Moving Object Detection for Autonomous Driving—0
Analytical Safety Bounds for Trajectory Following Controllers in Autonomous Vehicles—0
DriveGPT4: Interpretable End-to-end Autonomous Driving via Large Language Model—0
DriveGenVLM: Real-world Video Generation for Vision Language Model based Autonomous Driving—0
BLOS-BEV: Navigation Map Enhanced Lane Segmentation Network, Beyond Line of Sight—0
Dynamically Local-Enhancement Planner for Large-Scale Autonomous Driving—0
An Efficient Generation Method based on Dynamic Curvature of the Reference Curve for Robust Trajectory Planning—0
Dynamic Compression Ratio Selection for Edge Inference Systems with Hard Deadlines—0
DriveGen: Towards Infinite Diverse Traffic Scenarios with Large Models—0
Analysis of Truncated Singular Value Decomposition for Koopman Operator-Based Lane Change Model—0
Ada3Diff: Defending against 3D Adversarial Point Clouds via Adaptive Diffusion—0
Dynamic Interaction-Aware Scene Understanding for Reinforcement Learning in Autonomous Driving—0
Dynamic Point Cloud Denoising via Gradient Fields—0
Dynamic Prioritization of Emergency Vehicles For Self-Organizing Traffic using VTL+EV *—0
Dynamic Scenario Representation Learning for Motion Forecasting with Heterogeneous Graph Convolutional Recurrent Networks—0
Dynamic Task Weighting Methods for Multi-task Networks in Autonomous Driving Systems—0
Rearchitecting Classification Frameworks For Increased Robustness—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