SOTAVerified

Motion Planning

( Image credit: Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 701–725 of 920 papers

TitleStatusHype
Solution Concepts in Hierarchical Games under Bounded Rationality with Applications to Autonomous DrivingCode0
Stochastic Model Predictive Control with a Safety Guarantee for Automated Driving: Extended Version—0
Real-time Lane detection and Motion Planning in Raspberry Pi and Arduino for an Autonomous Vehicle Prototype—0
POMP: Pomcp-based Online Motion Planning for active visual search in indoor environments—0
MATS: An Interpretable Trajectory Forecasting Representation for Planning and ControlCode1
Planning with Learned Object Importance in Large Problem Instances using Graph Neural NetworksCode1
T^-Lite: A Fast Time-Risk Optimal Motion Planning Algorithm for Multi-Speed Autonomous Vehicles—0
Path Planning Followed by Kinodynamic Smoothing for Multirotor Aerial Vehicles (MAVs)—0
Designing inverse dynamic controller with integral action for motion planning of surgical robot in the presence of bounded disturbances—0
Perceive, Predict, and Plan: Safe Motion Planning Through Interpretable Semantic Representations—0
Lift, Splat, Shoot: Encoding Images From Arbitrary Camera Rigs by Implicitly Unprojecting to 3DCode2
Testing the Safety of Self-driving Vehicles by Simulating Perception and Prediction—0
DSDNet: Deep Structured self-Driving Network—0
Neural Manipulation Planning on Constraint Manifolds—0
Learning from Sparse DemonstrationsCode1
Near-Optimal Reactive Synthesis Incorporating Runtime Information—0
CAMPs: Learning Context-Specific Abstractions for Efficient Planning in Factored MDPsCode1
Learning the Solution Manifold in Optimization and Its Application in Motion Planning—0
Implicit Latent Variable Model for Scene-Consistent Motion Forecasting—0
Autonomy and Unmanned Vehicles Augmented Reactive Mission-Motion Planning Architecture for Autonomous Vehicles—0
VRUNet: Multi-Task Learning Model for Intent Prediction of Vulnerable Road Users—0
ADD: Analytically Differentiable Dynamics for Multi-Body Systems with Frictional ContactCode1
Predicting Sample Collision with Neural Networks—0
Confidence-rich grid mapping—0
One Thousand and One Hours: Self-driving Motion Prediction DatasetCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1GPT-DriverCollision0.35—Unverified