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 2181–2190 of 2605 papers

TitleStatusHype
Training Adversarial Agents to Exploit Weaknesses in Deep Control PoliciesCode0
Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory PredictionCode1
Generating Control Policies for Autonomous Vehicles Using Neural ODEs—0
Pedestrian Models for Autonomous Driving Part I: Low-Level Models, from Sensing to Tracking—0
3D-MiniNet: Learning a 2D Representation from Point Clouds for Fast and Efficient 3D LIDAR Semantic SegmentationCode1
Improvements of Motion Estimation and Coding using Neural Networks—0
Real-time Fusion Network for RGB-D Semantic Segmentation Incorporating Unexpected Obstacle Detection for Road-driving ImagesCode1
Spatiotemporal Relationship Reasoning for Pedestrian Intent PredictionCode1
siaNMS: Non-Maximum Suppression with Siamese Networks for Multi-Camera 3D Object Detection—0
Cooperative LIDAR Object Detection via Feature Sharing in Deep Networks—0
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Benchmark Results

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