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 4051–4075 of 6092 papers

TitleStatusHype
Lane Detection with Versatile AtrousFormer and Local Semantic Guidance—0
CROON: Automatic Multi-LiDAR Calibration and Refinement Method in Road SceneCode1
An Unsupervised Domain Adaptive Approach for Multimodal 2D Object Detection in Adverse Weather Conditions—0
Important Object Identification with Semi-Supervised Learning for Autonomous Driving—0
Reconfigurable Intelligent Surface (RIS)-aided Vehicular Networks: Their Protocols, Resource Allocation, and Performance—0
A Quality Index Metric and Method for Online Self-Assessment of Autonomous Vehicles Sensory Perception—0
Pedestrian Stop and Go Forecasting with Hybrid Feature FusionCode1
Pseudo-Stereo for Monocular 3D Object Detection in Autonomous DrivingCode1
A Versatile Multi-View Framework for LiDAR-based 3D Object Detection with Guidance from Panoptic Segmentation—0
Differentiable Control Barrier Functions for Vision-based End-to-End Autonomous Driving—0
Evaluating Object (mis)Detection from a Safety and Reliability Perspective: Discussion and MeasuresCode0
Optical Flow Based Motion Detection for Autonomous DrivingCode1
Bilateral Deep Reinforcement Learning Approach for Better-than-human Car Following Model—0
Intrinsically-Motivated Reinforcement Learning: A Brief Introduction—0
Beyond 3D Siamese Tracking: A Motion-Centric Paradigm for 3D Single Object Tracking in Point CloudsCode2
LatentFormer: Multi-Agent Transformer-Based Interaction Modeling and Trajectory Prediction—0
Spatio-Temporal Gating-Adjacency GCN for Human Motion Prediction—0
Continual BatchNorm Adaptation (CBNA) for Semantic SegmentationCode0
MUAD: Multiple Uncertainties for Autonomous Driving, a benchmark for multiple uncertainty types and tasksCode1
Physical Backdoor Attacks to Lane Detection Systems in Autonomous Driving—0
Improving Lidar-Based Semantic Segmentation of Top-View Grid Maps by Learning Features in Complementary Representations—0
Vision-based Large-scale 3D Semantic Mapping for Autonomous Driving Applications—0
A Unified Query-based Paradigm for Point Cloud UnderstandingCode1
MMFN: Multi-Modal Fusion Net for End-to-End Autonomous DrivingCode1
Adversarial samples for deep monocular 6D object pose estimationCode0
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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