SOTAVerified

Autonomous Vehicles

Autonomous vehicles is the task of making a vehicle that can guide itself 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: GSNet: Joint Vehicle Pose and Shape Reconstruction with Geometrical and Scene-aware Supervision )

Papers

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Showing 451–460 of 2605 papers

TitleStatusHype
LipKernel: Lipschitz-Bounded Convolutional Neural Networks via Dissipative LayersCode0
LiDAR-Camera Calibration using 3D-3D Point correspondencesCode0
3D Traffic Simulation for Autonomous Vehicles in Unity and PythonCode0
Leveraging Shape Completion for 3D Siamese TrackingCode0
Loam_livox: A fast, robust, high-precision LiDAR odometry and mapping package for LiDARs of small FoVCode0
BP-SGCN: Behavioral Pseudo-Label Informed Sparse Graph Convolution Network for Pedestrian and Heterogeneous Trajectory PredictionCode0
RUHSNet: 3D Object Detection Using Lidar Data in Real TimeCode0
Boosting Visual Recognition in Real-world Degradations via Unsupervised Feature Enhancement Module with Deep Channel PriorCode0
Learning to Map Vehicles into Bird's Eye ViewCode0
Learning-Based MPC for Fuel Efficient Control of Autonomous Vehicles with Discrete Gear SelectionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1BAAMA3DP22.85—Unverified
2GSNetA3DP20.21—Unverified