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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2051–2075 of 2605 papers

TitleStatusHype
Hypercongestion, autonomous vehicle, and urban spatial structure—0
Personalization and Recommendation Technologies for MaaS—0
Personalized and Context-aware Route Planning for Edge-assisted Vehicles—0
Personalized Federated Learning for Cross-view Geo-localization—0
Personalized Federated Learning of Driver Prediction Models for Autonomous Driving—0
Physical Adversarial Examples for Multi-Camera Systems—0
Physical Backdoor Trigger Activation of Autonomous Vehicle using Reachability Analysis—0
Physical Computing for Materials Acceleration Platforms—0
Physically constrained short-term vehicle trajectory forecasting with naive semantic maps—0
Physically Interpretable Probabilistic Domain Characterization—0
Physics-Informed Adaptive Deep Koopman Operator Modeling for Autonomous Vehicle Dynamics—0
*: Improving the 3D detector by introducing Voxel2Pillar feature encoding and extracting multi-scale features—0
PIP-Net: Pedestrian Intention Prediction in the Wild—0
Pixel-Level GPS Localization and Denoising using Computer Vision and 6G Communication Beams—0
Place recognition survey: An update on deep learning approaches—0
Planning and Learning: Path-Planning for Autonomous Vehicles, a Review of the Literature—0
Planning Reliability Assurance Tests for Autonomous Vehicles—0
Planning with Occluded Traffic Agents using Bi-Level Variational Occlusion Models—0
Platoon--assisted Vehicular Cloud in VANET: Vision and Challenges—0
Plausibility Verification For 3D Object Detectors Using Energy-Based Optimization—0
P-MapNet: Far-seeing Map Generator Enhanced by both SDMap and HDMap Priors—0
Point Cloud Compression for Efficient Data Broadcasting: A Performance Comparison—0
Point Cloud in the Air—0
Point Cloud Segmentation Using Sparse Temporal Local Attention—0
PointNetKL: Deep Inference for GICP Covariance Estimation in Bathymetric SLAM—0
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Benchmark Results

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