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 761–770 of 2605 papers

TitleStatusHype
Autonomous Braking and Throttle System: A Deep Reinforcement Learning Approach for Naturalistic Driving—0
Behavior Identification and Prediction for a Probabilistic Risk Framework—0
Deep Neural Networks with Koopman Operators for Modeling and Control of Autonomous Vehicles—0
Autonomous Algorithm for Training Autonomous Vehicles with Minimal Human Intervention—0
Drive&Act: A Multi-Modal Dataset for Fine-Grained Driver Behavior Recognition in Autonomous Vehicles—0
DriveAction: A Benchmark for Exploring Human-like Driving Decisions in VLA Models—0
A Multicriteria Decision Making Approach to Study the Barriers to the Adoption of Autonomous Vehicles—0
Deep Learning with Attention Mechanism for Predicting Driver Intention at Intersection—0
DriveGuard: Robustification of Automated Driving Systems with Deep Spatio-Temporal Convolutional Autoencoder—0
Deep Learning Serves Traffic Safety Analysis: A Forward-looking Review—0
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Benchmark Results

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