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 3626–3650 of 6092 papers

TitleStatusHype
Data Smells in Public Datasets—0
D-Aug: Enhancing Data Augmentation for Dynamic LiDAR Scenes—0
DAWN: Vehicle Detection in Adverse Weather Nature Dataset—0
DB3D-L: Depth-aware BEV Feature Transformation for Accurate 3D Lane Detection—0
DDK: A Deep Koopman Approach for Dynamics Modeling and Trajectory Tracking of Autonomous Vehicles—0
Modified DDPG car-following model with a real-world human driving experience with CARLA simulator—0
DDU-Net: Dual-Decoder-U-Net for Road Extraction Using High-Resolution Remote Sensing Images—0
Deceiving Image-to-Image Translation Networks for Autonomous Driving with Adversarial Perturbations—0
Decentralized multi-agent reinforcement learning algorithm using a cluster-synchronized laser network—0
Decision Making for Autonomous Driving in Interactive Merge Scenarios via Learning-based Prediction—0
Decision making in dynamic and interactive environments based on cognitive hierarchy theory, Bayesian inference, and predictive control—0
Decision-making Strategy on Highway for Autonomous Vehicles using Deep Reinforcement Learning—0
Decoder Fusion RNN: Context and Interaction Aware Decoders for Trajectory Prediction—0
Decoding Interpretable Logic Rules from Neural Networks—0
Deconvolutional Networks for Point-Cloud Vehicle Detection and Tracking in Driving Scenarios—0
DecoratingFusion: A LiDAR-Camera Fusion Network with the Combination of Point-level and Feature-level Fusion—0
Decoupled Diffusion Sparks Adaptive Scene Generation—0
DecoupledGaussian: Object-Scene Decoupling for Physics-Based Interaction—0
Decoupling of neural network calibration measures—0
DeepAccident: A Motion and Accident Prediction Benchmark for V2X Autonomous Driving—0
Deep Active Learning for Efficient Training of a LiDAR 3D Object Detector—0
Deep auxiliary learning for visual localization using colorization task—0
DeepAVO: Efficient Pose Refining with Feature Distilling for Deep Visual Odometry—0
DeepBbox: Accelerating Precise Ground Truth Generation for Autonomous Driving Datasets—0
DeepBillboard: Systematic Physical-World Testing of Autonomous Driving Systems—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