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 1776–1800 of 2605 papers

TitleStatusHype
Towards Data-Driven Synthesis of Autonomous Vehicle Safety Concepts—0
Towards Viewpoint Robustness in Bird's Eye View Segmentation—0
TR2MTL: LLM based framework for Metric Temporal Logic Formalization of Traffic Rules—0
Tracking Noisy Targets: A Review of Recent Object Tracking Approaches—0
TrackletMapper: Ground Surface Segmentation and Mapping from Traffic Participant Trajectories—0
Traditional methods in Edge, Corner and Boundary detection—0
Traffic Control in a Mixed Autonomy Scenario at Urban Intersections: An Optimization-based Framework—0
Traffic Flow Simulation for Autonomous Driving—0
Traffic Management of Autonomous Vehicles using Policy Based Deep Reinforcement Learning and Intelligent Routing—0
TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents—0
Traffic Smoothing Controllers for Autonomous Vehicles Using Deep Reinforcement Learning and Real-World Trajectory Data—0
Training A Neural Network For Partially Occluded Road Sign Identification In The Context Of Autonomous Vehicles—0
t-RAIN: Robust generalization under weather-aliasing label shift attacks—0
Trajectories and Platoon-forming Algorithm for Intersections with Heterogeneous Autonomous Traffic—0
Trajectory planning for autonomous intersection management of connected vehicles—0
Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directions—0
Trajectory Prediction in Autonomous Driving with a Lane Heading Auxiliary Loss—0
Trajectory Prediction using Equivariant Continuous Convolution—0
Trajectory-Prediction with Vision: A Survey—0
Transferable and Adaptable Driving Behavior Prediction—0
Transferable Deep Reinforcement Learning Framework for Autonomous Vehicles with Joint Radar-Data Communications—0
Transfer Learning and Organic Computing for Autonomous Vehicles—0
Transferring Autonomous Driving Knowledge on Simulated and Real Intersections—0
Transformer-based models and hardware acceleration analysis in autonomous driving: A survey—0
Transformers and Large Language Models for Efficient Intrusion Detection Systems: A Comprehensive Survey—0
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Benchmark Results

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