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 3801–3825 of 6092 papers

TitleStatusHype
Urban-Focused Multi-Task Offline Reinforcement Learning with Contrastive Data Sharing—0
UrbanLoco: A Full Sensor Suite Dataset for Mapping and Localization in Urban Scenes—0
Urban-StyleGAN: Learning to Generate and Manipulate Images of Urban Scenes—0
USAM-Net: A U-Net-based Network for Improved Stereo Correspondence and Scene Depth Estimation using Features from a Pre-trained Image Segmentation network—0
Using an ensemble color space model to tackle adversarial examples—0
Using Collision Momentum in Deep Reinforcement Learning Based Adversarial Pedestrian Modeling—0
Using Convolutional Neural Networks for Determining Reticulocyte Percentage in Cats—0
Using Cooperative Co-evolutionary Search to Generate Metamorphic Test Cases for Autonomous Driving Systems—0
Using Diffusion Ensembles to Estimate Uncertainty for End-to-End Autonomous Driving—0
Using DP Towards A Shortest Path Problem-Related Application—0
Using Experience Classification for Training Non-Markovian Tasks—0
Using Image Priors to Improve Scene Understanding—0
Using Knowledge Awareness to improve Safety of Autonomous Driving—0
USTC FLICAR: A Sensors Fusion Dataset of LiDAR-Inertial-Camera for Heavy-duty Autonomous Aerial Work Robots—0
UST: Unifying Spatio-Temporal Context for Trajectory Prediction in Autonomous Driving—0
USVTrack: USV-Based 4D Radar-Camera Tracking Dataset for Autonomous Driving in Inland Waterways—0
Utilizing Eye Gaze to Enhance the Generalization of Imitation Networks to Unseen Environments—0
V2V-LLM: Vehicle-to-Vehicle Cooperative Autonomous Driving with Multi-Modal Large Language Models—0
V2X-Boosted Federated Learning for Cooperative Intelligent Transportation Systems with Contextual Client Selection—0
V2X Cooperative Perception for Autonomous Driving: Recent Advances and Challenges—0
V2X-DG: Domain Generalization for Vehicle-to-Everything Cooperative Perception—0
V2X-Lead: LiDAR-based End-to-End Autonomous Driving with Vehicle-to-Everything Communication Integration—0
V2X-Radar: A Multi-modal Dataset with 4D Radar for Cooperative Perception—0
V2X-REALM: Vision-Language Model-Based Robust End-to-End Cooperative Autonomous Driving with Adaptive Long-Tail Modeling—0
V2X-Sim: Multi-Agent Collaborative Perception Dataset and Benchmark 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