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 1551–1560 of 2605 papers

TitleStatusHype
Semantic Segmentation for Autonomous Driving: Model Evaluation, Dataset Generation, Perspective Comparison, and Real-Time CapabilityCode0
Planning and Learning: Path-Planning for Autonomous Vehicles, a Review of the Literature—0
Learning Relaxation for Multigrid—0
Adaptive Decision Making at the Intersection for Autonomous Vehicles Based on Skill Discovery—0
CheckINN: Wide Range Neural Network Verification in Imandra (Extended)—0
Expert-LaSTS: Expert-Knowledge Guided Latent Space for Traffic ScenariosCode0
VTrackIt: A Synthetic Self-Driving Dataset with Infrastructure and Pooled Vehicle Information—0
Experiments on Anomaly Detection in Autonomous Driving by Forward-Backward Style Transfers—0
A Near Sensor Edge Computing System for Point Cloud Semantic Segmentation—0
Statistical Detection of Adversarial examples in Blockchain-based Federated Forest In-vehicle Network Intrusion Detection Systems—0
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Benchmark Results

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