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 4701–4725 of 6092 papers

TitleStatusHype
SMAC-Seg: LiDAR Panoptic Segmentation via Sparse Multi-directional Attention Clustering—0
Quantized Convolutional Neural Networks Through the Lens of Partial Differential Equations—0
Integrated Decision and Control at Multi-Lane Intersections with Mixed Traffic Flow—0
RPR-Net: A Point Cloud-based Rotation-aware Large Scale Place Recognition Network—0
A Pedestrian Detection and Tracking Framework for Autonomous Cars: Efficient Fusion of Camera and LiDAR Data—0
WAD: A Deep Reinforcement Learning Agent for Urban Autonomous Driving—0
A Comparison of Deep Saliency Map Generators on Multispectral Data in Object Detection—0
Graph Laplacian Diffusion Localization of Connected and Automated Vehicles—0
A Benchmark for Spray from Nearby Cutting Vehicles—0
Embodied AI-Driven Operation of Smart Cities: A Concise Review—0
L3C-Stereo: Lossless Compression for Stereo Images—0
Geo-Context Aware Study of Vision-Based Autonomous Driving Models and Spatial Video Data—0
AutoLay: Benchmarking amodal layout estimation for autonomous driving—0
An Independent Study of Reinforcement Learning and Autonomous Driving—0
Retrieval and Localization with Observation Constraints—0
Calibration Method of the Monocular Omnidirectional Stereo Camera—0
LOKI: Long Term and Key Intentions for Trajectory Prediction—0
An Evaluation of RGB and LiDAR Fusion for Semantic Segmentation—0
LIF-Seg: LiDAR and Camera Image Fusion for 3D LiDAR Semantic Segmentation—0
SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation—0
Detecting OODs as datapoints with High Uncertainty—0
Robustness testing of AI systems: A case study for traffic sign recognition—0
Decoder Fusion RNN: Context and Interaction Aware Decoders for Trajectory Prediction—0
Reimagining an autonomous vehicle—0
Capture Uncertainties in Deep Neural Networks for Safe Operation of Autonomous Driving Vehicles—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