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 131–140 of 2605 papers

TitleStatusHype
3D-MiniNet: Learning a 2D Representation from Point Clouds for Fast and Efficient 3D LIDAR Semantic SegmentationCode1
Driving Style Representation in Convolutional Recurrent Neural Network Model of Driver IdentificationCode1
DSLR: Dynamic to Static LiDAR Scan Reconstruction Using Adversarially Trained AutoencoderCode1
Domain Generalization for Vision-based Driving Trajectory GenerationCode1
Domain Adaptation based Object Detection for Autonomous Driving in Foggy and Rainy WeatherCode1
Do Pedestrians Pay Attention? Eye Contact Detection in the WildCode1
BAAM: Monocular 3D Pose and Shape Reconstruction With Bi-Contextual Attention Module and Attention-Guided ModelingCode1
BAT: Behavior-Aware Human-Like Trajectory Prediction for Autonomous DrivingCode1
DUSA: Decoupled Unsupervised Sim2Real Adaptation for Vehicle-to-Everything Collaborative PerceptionCode1
End-to-end Lane Shape Prediction with TransformersCode1
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Benchmark Results

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