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 361–370 of 2605 papers

TitleStatusHype
Multimodal Object Detection via Probabilistic EnsemblingCode1
Multimodal Virtual Point 3D DetectionCode1
MUSES: The Multi-Sensor Semantic Perception Dataset for Driving under UncertaintyCode1
MVP: Unified Motion and Visual Self-Supervised Learning for Large-Scale Robotic NavigationCode1
Neural Network and ANFIS based auto-adaptive MPC for path tracking in autonomous vehiclesCode1
Neural Network Guided Evolutionary Fuzzing for Finding Traffic Violations of Autonomous VehiclesCode1
Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?Code1
Towards Motion Forecasting with Real-World Perception Inputs: Are End-to-End Approaches Competitive?Code1
AVP-SLAM: Semantic Visual Mapping and Localization for Autonomous Vehicles in the Parking LotCode1
COOPERNAUT: End-to-End Driving with Cooperative Perception for Networked VehiclesCode1
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Benchmark Results

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