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 301–325 of 2605 papers

TitleStatusHype
GMS-VINS:Multi-category Dynamic Objects Semantic Segmentation for Enhanced Visual-Inertial Odometry Using a Promptable Foundation Model—0
Explainable deep learning improves human mental models of self-driving cars—0
Dependency-Aware CAV Task Scheduling via Diffusion-Based Reinforcement Learning—0
DECODE: Domain-aware Continual Domain Expansion for Motion PredictionCode0
Rate-Informed Discovery via Bayesian Adaptive Multifidelity Sampling—0
Enhancing Lane Segment Perception and Topology Reasoning with Crowdsourcing Trajectory Priors—0
CRASH: Challenging Reinforcement-Learning Based Adversarial Scenarios For Safety Hardening—0
A Parameter Adaptive Trajectory Tracking and Motion Control Framework for Autonomous Vehicle—0
Generating Out-Of-Distribution Scenarios Using Language Models—0
End-to-End Steering for Autonomous Vehicles via Conditional Imitation Co-Learning—0
Enhancing Object Detection Accuracy in Autonomous Vehicles Using Synthetic Data—0
Physically Interpretable Probabilistic Domain Characterization—0
A Real-Time DETR Approach to Bangladesh Road Object Detection for Autonomous Vehicles—0
Enhancing Autonomous Driving Safety through World Model-Based Predictive Navigation and Adaptive Learning Algorithms for 5G Wireless Applications—0
A Survey on Adversarial Robustness of LiDAR-based Machine Learning Perception in Autonomous Vehicles—0
Experimental comparison of graph-based approximate nearest neighbor search algorithms on edge devices—0
FedRAV: Hierarchically Federated Region-Learning for Traffic Object Classification of Autonomous VehiclesCode0
Verification and Validation of Autonomous Systems—0
WHALES: A Multi-agent Scheduling Dataset for Enhanced Cooperation in Autonomous DrivingCode1
Zonal Architecture Development with evolution of Artificial Intelligence—0
MGNiceNet: Unified Monocular Geometric Scene UnderstandingCode0
On-Board Vision-Language Models for Personalized Autonomous Vehicle Motion Control: System Design and Real-World Validation—0
Iterative Camera-LiDAR Extrinsic Optimization via Surrogate Diffusion—0
AppSign: Multi-level Approximate Computing for Real-Time Traffic Sign Recognition in Autonomous Vehicles—0
Map-Free Trajectory Prediction with Map Distillation and Hierarchical Encoding—0
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Benchmark Results

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