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 5976–6000 of 6092 papers

TitleStatusHype
Automating Vehicles by Deep Reinforcement Learning using Task Separation with Hill Climbing—0
Long-Term On-Board Prediction of People in Traffic Scenes under Uncertainty—0
Predicting Driver Attention in Critical SituationsCode1
Fast Recurrent Fully Convolutional Networks for Direct Perception in Autonomous Driving—0
Fusing Bird View LIDAR Point Cloud and Front View Camera Image for Deep Object Detection—0
Minimizing Supervision for Free-space SegmentationCode0
Robust and Precise Vehicle Localization based on Multi-sensor Fusion in Diverse City Scenes—0
Squeeze-SegNet: A new fast Deep Convolutional Neural Network for Semantic SegmentationCode0
CARLA: An Open Urban Driving SimulatorCode0
A General Neural Network Hardware Architecture on FPGA—0
A Joint 3D-2D based Method for Free Space Detection on Roads—0
Composing Meta-Policies for Autonomous Driving Using Hierarchical Deep Reinforcement Learning—0
DDD17: End-To-End DAVIS Driving DatasetCode1
Accountability of AI Under the Law: The Role of Explanation—0
Deep Recurrent Gaussian Process with Variational Sparse Spectrum ApproximationCode0
HyKo: A Spectral Dataset for Scene Understanding—0
SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point CloudCode0
Gradient-free Policy Architecture Search and Adaptation—0
Burn-In Demonstrations for Multi-Modal Imitation Learning—0
Arguing Machines: Human Supervision of Black Box AI Systems That Make Life-Critical DecisionsCode0
DeepRoadMapper: Extracting Road Topology From Aerial Images—0
Bounding Boxes, Segmentations and Object Coordinates: How Important Is Recognition for 3D Scene Flow Estimation in Autonomous Driving Scenarios?—0
A Multimodal, Full-Surround Vehicular Testbed for Naturalistic Studies and Benchmarking: Design, Calibration and Deployment—0
MultiNet: Multi-Modal Multi-Task Learning for Autonomous Driving—0
MODNet: Moving Object Detection Network with Motion and Appearance for Autonomous Driving—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