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 931–940 of 2605 papers

TitleStatusHype
Integrating Higher-Order Dynamics and Roadway-Compliance into Constrained ILQR-based Trajectory Planning for Autonomous Vehicles—0
Interaction-Aware Decision-Making for Autonomous Vehicles in Forced Merging Scenario Leveraging Social Psychology Factors—0
Implicit Sensing in Traffic Optimization: Advanced Deep Reinforcement Learning Techniques—0
Scene Informer: Anchor-based Occlusion Inference and Trajectory Prediction in Partially Observable EnvironmentsCode1
Hierarchical Imitation Learning for Stochastic Environments—0
Stackelberg Driver Model for Continual Policy Improvement in Scenario-Based Closed-Loop Autonomous DrivingCode1
DISeR: Designing Imaging Systems with Reinforcement Learning—0
Autonomous Vehicles an overview on system, cyber security, risks, issues, and a way forward—0
Continual Driving Policy Optimization with Closed-Loop Individualized CurriculaCode1
An Optimal Control Framework for Influencing Human Driving Behavior in Mixed-Autonomy Traffic—0
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Benchmark Results

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