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 121–130 of 2605 papers

TitleStatusHype
CAVIAR: Co-simulation of 6G Communications, 3D Scenarios and AI for Digital TwinsCode1
ERMVP: Communication-Efficient and Collaboration-Robust Multi-Vehicle Perception in Challenging EnvironmentsCode1
TPatch: A Triggered Physical Adversarial PatchCode1
Social-Transmotion: Promptable Human Trajectory PredictionCode1
Efficient Reinforcement Learning via Decoupling Exploration and UtilizationCode1
Parameterized Decision-making with Multi-modal Perception for Autonomous DrivingCode1
Personalized Autonomous Driving with Large Language Models: Field ExperimentsCode1
Reliability in Semantic Segmentation: Can We Use Synthetic Data?Code1
Efficient Object Detection in Autonomous Driving using Spiking Neural Networks: Performance, Energy Consumption Analysis, and Insights into Open-set Object DiscoveryCode1
BAT: Behavior-Aware Human-Like Trajectory Prediction for Autonomous DrivingCode1
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Benchmark Results

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