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 5851–5875 of 6092 papers

TitleStatusHype
Focal Loss in 3D Object DetectionCode0
Road Detection Technique Using Filters with Application to Autonomous Driving System—0
On Offline Evaluation of Vision-based Driving ModelsCode0
Bio-LSTM: A Biomechanically Inspired Recurrent Neural Network for 3D Pedestrian Pose and Gait Prediction—0
Adaptive Behavior Generation for Autonomous Driving using Deep Reinforcement Learning with Compact Semantic States—0
Generic Probabilistic Interactive Situation Recognition and Prediction: From Virtual to Real—0
A Block Coordinate Ascent Algorithm for Mean-Variance Optimization—0
Conditional Transfer with Dense Residual Attention: Synthesizing traffic signs from street-view imagery—0
Out-of-Distribution Detection Using an Ensemble of Self Supervised Leave-out ClassifiersCode0
DeepHunter: Hunting Deep Neural Network Defects via Coverage-Guided Fuzzing—0
Developing a Purely Visual Based Obstacle Detection using Inverse Perspective Mapping—0
VSO: Visual Semantic Odometry—0
Baidu Apollo Auto-Calibration System - An Industry-Level Data-Driven and Learning based Vehicle Longitude Dynamic Calibrating AlgorithmCode0
Learning End-to-end Autonomous Driving using Guided Auxiliary SupervisionCode0
Deep Lidar CNN to Understand the Dynamics of Moving Vehicles—0
Sparsity in Deep Neural Networks - An Empirical Investigation with TensorQuantCode0
HMS-Net: Hierarchical Multi-scale Sparsity-invariant Network for Sparse Depth Completion—0
Autonomous Driving without a Burden: View from Outside with Elevated LiDAR—0
A Brief Survey and an Application of Semantic Image Segmentation for Autonomous Driving—0
Guiding Deep Learning System Testing using Surprise AdequacyCode1
Predicting Action Tubes—0
Deconvolutional Networks for Point-Cloud Vehicle Detection and Tracking in Driving Scenarios—0
Learning Monocular Depth by Distilling Cross-domain Stereo NetworksCode0
Uncertainty-aware Short-term Motion Prediction of Traffic Actors for Autonomous Driving—0
Multispectral Pedestrian Detection via Simultaneous Detection and SegmentationCode0
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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