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 5576–5600 of 6092 papers

TitleStatusHype
Bin-wise Temperature Scaling (BTS): Improvement in Confidence Calibration Performance through Simple Scaling Techniques—0
Temporal Coherence for Active Learning in Videos—0
DeepBbox: Accelerating Precise Ground Truth Generation for Autonomous Driving Datasets—0
Texture Underfitting for Domain Adaptation—0
Class-balanced Grouping and Sampling for Point Cloud 3D Object DetectionCode0
Generic Tracking and Probabilistic Prediction Framework and Its Application in Autonomous Driving—0
Design Space of Behaviour Planning for Autonomous Driving—0
Efficient Deep Neural Networks—0
Cross-Enhancement Transform Two-Stream 3D ConvNets for Action Recognition—0
How far should self-driving cars see? Effect of observation range on vehicle self-localization—0
Fine-Grained Segmentation Networks: Self-Supervised Segmentation for Improved Long-Term Visual LocalizationCode0
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes—0
Adversarial shape perturbations on 3D point cloudsCode0
A Multimodal Vision Sensor for Autonomous Driving—0
To complete or to estimate, that is the question: A Multi-Task Approach to Depth Completion and Monocular Depth Estimation—0
Bypass Enhancement RGB Stream Model for Pedestrian Action Recognition of Autonomous Vehicles—0
Benchmarking the Robustness of Semantic Segmentation Models—0
Decision making in dynamic and interactive environments based on cognitive hierarchy theory, Bayesian inference, and predictive control—0
Incremental Reinforcement Learning --- a New Continuous Reinforcement Learning Frame Based on Stochastic Differential Equation methods—0
Learning Guided Convolutional Network for Depth CompletionCode0
Scalable Place Recognition Under Appearance Change for Autonomous Driving—0
Video Stitching for Linear Camera Arrays—0
Orientation-aware Semantic Segmentation on Icosahedron SpheresCode0
Single Level Feature-to-Feature Forecasting with Deformable Convolutions—0
Object as Distribution—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