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 71–80 of 2605 papers

TitleStatusHype
Counterfactual Multi-Agent Policy GradientsCode1
CRAT-Pred: Vehicle Trajectory Prediction with Crystal Graph Convolutional Neural Networks and Multi-Head Self-AttentionCode1
Coordinated PSO-PID based longitudinal control with LPV-MPC based lateral control for autonomous vehiclesCode1
MSMA: Multi-agent Trajectory Prediction in Connected and Autonomous Vehicle Environment with Multi-source Data IntegrationCode1
CoTV: Cooperative Control for Traffic Light Signals and Connected Autonomous Vehicles using Deep Reinforcement LearningCode1
CRT-Fusion: Camera, Radar, Temporal Fusion Using Motion Information for 3D Object DetectionCode1
Deep Multi-agent Reinforcement Learning for Highway On-Ramp Merging in Mixed TrafficCode1
CoMAL: Collaborative Multi-Agent Large Language Models for Mixed-Autonomy TrafficCode1
Collaborative Motion Prediction via Neural Motion Message PassingCode1
Continual Driving Policy Optimization with Closed-Loop Individualized CurriculaCode1
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Benchmark Results

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