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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2526–2550 of 2605 papers

TitleStatusHype
Tracking Noisy Targets: A Review of Recent Object Tracking Approaches—0
Driver Gaze Zone Estimation using Convolutional Neural Networks: A General Framework and Ablative Analysis—0
Joint Attention in Driver-Pedestrian Interaction: from Theory to Practice—0
An LSTM Network for Highway Trajectory Prediction—0
How would surround vehicles move? A Unified Framework for Maneuver Classification and Motion Prediction—0
Long-term Visual Localization using Semantically Segmented Images—0
A Computational Model of Commonsense Moral Decision Making—0
Predicting Future Lane Changes of Other Highway Vehicles using RNN-based Deep Models—0
Rogue Signs: Deceiving Traffic Sign Recognition with Malicious Ads and LogosCode0
Towards Multi-Object Detection and Tracking in Urban Scenario under Uncertainties—0
Approximate FPGA-based LSTMs under Computation Time Constraints—0
Depth Not Needed - An Evaluation of RGB-D Feature Encodings for Off-Road Scene Understanding by Convolutional Neural Network—0
Autonomous Vehicle Fleet Coordination With Deep Reinforcement Learning—0
MFNet: Towards real-time semantic segmentation for autonomous vehicles with multi-spectral scenes—0
Joint 3D Proposal Generation and Object Detection from View AggregationCode0
Transferring Autonomous Driving Knowledge on Simulated and Real Intersections—0
HP-GAN: Probabilistic 3D human motion prediction via GANCode0
Non-line-of-sight Imaging with Partial Occluders and Surface Normals—0
Predicting Scene Parsing and Motion Dynamics in the Future—0
Procedural Modeling and Physically Based Rendering for Synthetic Data Generation in Automotive Applications—0
Combining LiDAR Space Clustering and Convolutional Neural Networks for Pedestrian Detection—0
Robust Fusion of LiDAR and Wide-Angle Camera Data for Autonomous Mobile Robots—0
Flow: A Modular Learning Framework for Mixed Autonomy TrafficCode2
End-to-End Deep Learning for Steering Autonomous Vehicles Considering Temporal Dependencies—0
Socially Compliant Navigation through Raw Depth Inputs with Generative Adversarial Imitation LearningCode0
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Benchmark Results

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