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 6026–6050 of 6092 papers

TitleStatusHype
A Review on Deep Learning Techniques Applied to Semantic SegmentationCode0
Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art—0
Virtual to Real Reinforcement Learning for Autonomous DrivingCode0
Deep Reinforcement Learning framework for Autonomous DrivingCode0
Predicting Deeper into the Future of Semantic SegmentationCode0
Fraternal Twins: Unifying Attacks on Machine Learning and Digital Watermarking—0
Addressing Appearance Change in Outdoor Robotics with Adversarial Domain Adaptation—0
Non-line-of-sight tracking of people at long range—0
VidLoc: A Deep Spatio-Temporal Model for 6-DoF Video-Clip Relocalization—0
Brain Inspired Cognitive Model with Attention for Self-Driving Cars—0
Enabling Robots to Communicate their Objectives—0
SafeDrive: A Robust Lane Tracking System for Autonomous and Assisted Driving Under Limited Visibility—0
Dirty Pixels: Towards End-to-End Image Processing and PerceptionCode0
MultiNet: Real-time Joint Semantic Reasoning for Autonomous DrivingCode1
Cloud Dictionary: Sparse Coding and Modeling for Point CloudsCode0
End-to-End Deep Reinforcement Learning for Lane Keeping Assist—0
Learning to Drive using Inverse Reinforcement Learning and Deep Q-Networks—0
SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous DrivingCode0
SAD-GAN: Synthetic Autonomous Driving using Generative Adversarial Networks—0
Learning from Maps: Visual Common Sense for Autonomous Driving—0
Full-Resolution Residual Networks for Semantic Segmentation in Street ScenesCode0
3D Fully Convolutional Network for Vehicle Detection in Point CloudCode0
Multi-View 3D Object Detection Network for Autonomous DrivingCode0
GPU-based Pedestrian Detection for Autonomous Driving—0
Learning Scalable Deep Kernels with Recurrent StructureCode0
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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