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 1926–1950 of 2605 papers

TitleStatusHype
Multi-Vehicle Interaction Scenarios Generation with Interpretable Traffic Primitives and Gaussian Process Regression—0
Multi-Vehicle Mixed-Reality Reinforcement Learning for Autonomous Multi-Lane Driving—0
Mutual Information Analysis in Multimodal Learning Systems—0
MV2DFusion: Leveraging Modality-Specific Object Semantics for Multi-Modal 3D Detection—0
MVFAN: Multi-View Feature Assisted Network for 4D Radar Object Detection—0
Natural Language Interactions in Autonomous Vehicles: Intent Detection and Slot Filling from Passenger Utterances—0
Navigating Intelligence: A Survey of Google OR-Tools and Machine Learning for Global Path Planning in Autonomous Vehicles—0
Navigating Occluded Intersections with Autonomous Vehicles using Deep Reinforcement Learning—0
Navigating Threats: A Survey of Physical Adversarial Attacks on LiDAR Perception Systems in Autonomous Vehicles—0
Navya3DSeg -- Navya 3D Semantic Segmentation Dataset & split generation for autonomous vehicles—0
N-DriverMotion: Driver motion learning and prediction using an event-based camera and directly trained spiking neural networks on Loihi 2—0
NDST: Neural Driving Style Transfer for Human-Like Vision-Based Autonomous Driving—0
NeRF2Points: Large-Scale Point Cloud Generation From Street Views' Radiance Field Optimization—0
Network Generalization Prediction for Safety Critical Tasks in Novel Operating Domains—0
Network-level Safety Metrics for Overall Traffic Safety Assessment: A Case Study—0
Neural Error Covariance Estimation for Precise LiDAR Localization—0
Neural Map Prior for Autonomous Driving—0
Neural Network Based Model Predictive Control for an Autonomous Vehicle—0
Neural Semantic Map-Learning for Autonomous Vehicles—0
Neuroevolutionary Multi-objective approaches to Trajectory Prediction in Autonomous Vehicles—0
NeuroFlow: Development of lightweight and efficient model integration scheduling strategy for autonomous driving system—0
Neuromorphic Computing is Turing-Complete—0
Neuromorphic Mimicry Attacks Exploiting Brain-Inspired Computing for Covert Cyber Intrusions—0
Next Wave Artificial Intelligence: Robust, Explainable, Adaptable, Ethical, and Accountable—0
NO Need to Worry about Adversarial Examples in Object Detection in Autonomous Vehicles—0
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Benchmark Results

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