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
Variational Monocular Depth Estimation for Reliability Prediction—0
Discovering Avoidable Planner Failures of Autonomous Vehicles using Counterfactual Analysis in Behaviorally Diverse Simulation—0
Multi-Agent Reinforcement Learning for Markov Routing Games: A New Modeling Paradigm For Dynamic Traffic Assignment—0
Emergent Road Rules In Multi-Agent Driving EnvironmentsCode1
Robust super-resolution depth imaging via a multi-feature fusion deep networkCode1
CGAP2: Context and gap aware predictive pose framework for early detection of gestures—0
Assessment of System-Level Cyber Attack Vulnerability for Connected and Autonomous Vehicles Using Bayesian Networks—0
DeepSeqSLAM: A Trainable CNN+RNN for Joint Global Description and Sequence-based Place RecognitionCode1
Efficient falsification approach for autonomous vehicle validation using a parameter optimisation technique based on reinforcement learning—0
GndNet: Fast Ground Plane Estimation and Point Cloud Segmentation for Autonomous VehiclesCode1
Trajectory Prediction in Autonomous Driving with a Lane Heading Auxiliary Loss—0
CenterFusion: Center-based Radar and Camera Fusion for 3D Object DetectionCode1
Model-based Reinforcement Learning from Signal Temporal Logic Specifications—0
End-to-end Lane Shape Prediction with TransformersCode1
Multi-Agent Reinforcement Learning for Channel Assignment and Power Allocation in Platoon-Based C-V2X Systems—0
Trajectory Planning for Autonomous Vehicles Using Hierarchical Reinforcement LearningCode1
Behavior Planning at Urban Intersections through Hierarchical Reinforcement Learning—0
Reactive motion planning with probabilistic safety guarantees—0
Motion Prediction on Self-driving Cars: A Review—0
IDE-Net: Interactive Driving Event and Pattern Extraction from Human Data—0
A Scenario-Based Development Framework for Autonomous Driving—0
SLAM in the Field: An Evaluation of Monocular Mapping and Localization on Challenging Dynamic Agricultural Environment—0
Optimizing Mixed Autonomy Traffic Flow With Decentralized Autonomous Vehicles and Multi-Agent RLCode1
Self-awareness in intelligent vehicles: Feature based dynamic Bayesian models for abnormality detection—0
Learning Personalized Discretionary Lane-Change Initiation for Fully Autonomous Driving Based on Reinforcement Learning—0
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Benchmark Results

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