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 541–550 of 2605 papers

TitleStatusHype
Learning to Drive by Imitating Surrounding Vehicles—0
Advancing Autonomous Vehicle Intelligence: Deep Learning and Multimodal LLM for Traffic Sign Recognition and Robust Lane Detection—0
Discrete Contrastive Learning for Diffusion Policies in Autonomous Driving—0
Safety-Critical Traffic Simulation with Adversarial Transfer of Driving Intentions—0
INTENT: Trajectory Prediction Framework with Intention-Guided Contrastive Clustering—0
Navigating Intelligence: A Survey of Google OR-Tools and Machine Learning for Global Path Planning in Autonomous Vehicles—0
Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directions—0
BEVMOSNet: Multimodal Fusion for BEV Moving Object Segmentation—0
ecg2o: A Seamless Extension of g2o for Equality-Constrained Factor Graph OptimizationCode0
Range and Angle Estimation with Spiking Neural Resonators for FMCW Radar—0
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Benchmark Results

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