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 831–840 of 2605 papers

TitleStatusHype
Prospective Role of Foundation Models in Advancing Autonomous Vehicles—0
Development and Assessment of Autonomous Vehicles in Both Fully Automated and Mixed Traffic Conditions—0
Bootstrapping Autonomous Driving Radars with Self-Supervised LearningCode1
Residual Graph Convolutional Network for Bird's-Eye-View Semantic Segmentation—0
GPT-4 Enhanced Multimodal Grounding for Autonomous Driving: Leveraging Cross-Modal Attention with Large Language ModelsCode1
STEREOFOG -- Computational DeFogging via Image-to-Image Translation on a real-world DatasetCode0
Analyze Drivers' Intervention Behavior During Autonomous Driving -- A VR-incorporated Approach—0
Dolphins: Multimodal Language Model for DrivingCode1
Improving Efficiency of DNN-based Relocalization Module for Autonomous Driving with Server-side Computing—0
Towards Efficient 3D Object Detection in Bird's-Eye-View Space for Autonomous Driving: A Convolutional-Only Approach—0
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Benchmark Results

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