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 1921–1930 of 2605 papers

TitleStatusHype
Why model why? Assessing the strengths and limitations of LIMECode0
SS-SFDA : Self-Supervised Source-Free Domain Adaptation for Road Segmentation in Hazardous EnvironmentsCode1
Polarization-driven Semantic Segmentation via Efficient Attention-bridged FusionCode1
A Fast Point Cloud Ground Segmentation Approach Based on Coarse-To-Fine Markov Random Field—0
Quantized Neural Networks for Radar Interference Mitigation—0
Variational Monocular Depth Estimation for Reliability Prediction—0
Discovering Avoidable Planner Failures of Autonomous Vehicles using Counterfactual Analysis in Behaviorally Diverse Simulation—0
Multi-Agent Reinforcement Learning for Markov Routing Games: A New Modeling Paradigm For Dynamic Traffic Assignment—0
Emergent Road Rules In Multi-Agent Driving EnvironmentsCode1
Robust super-resolution depth imaging via a multi-feature fusion deep networkCode1
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Benchmark Results

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