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https://doi.org/10.2139/ssrn.4679160

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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/rob.20147openalex-extracted
Stanley: The robot that won the DARPA Grand Challenge
inferredcites →https://doi.org/10.1007/s40815-020-00912-yopenalex-extracted
Hierarchical Path Planning of Unmanned Surface Vehicles: A Fuzzy Artificial Potential Field Approach
inferredcites →https://doi.org/10.1016/j.aap.2023.107044openalex-extracted
Wide-area vehicle trajectory data based on advanced tracking and trajectory splicing technologies: Potentials in transportation research
inferredcites →https://doi.org/10.1016/j.robot.2011.06.002openalex-extracted
Smooth coverage path planning and control of mobile robots based on high-resolution grid map representation
inferredcites →https://doi.org/10.1016/j.robot.2019.01.003openalex-extracted
Advanced planning for autonomous vehicles using reinforcement learning and deep inverse reinforcement learning
inferredcites →https://doi.org/10.1016/j.trc.2019.08.011openalex-extracted
Automated vehicle’s behavior decision making using deep reinforcement learning and high-fidelity simulation environment
inferredcites →https://doi.org/10.1109/access.2021.3076530openalex-extracted
Motion Planning for Mobile Robots—Focusing on Deep Reinforcement Learning: A Systematic Review
inferredcites →https://doi.org/10.1109/cvpr.2019.00691openalex-extracted
Learning to Learn How to Learn: Self-Adaptive Visual Navigation Using Meta-Learning
inferredcites →https://doi.org/10.1109/etfa.2019.8868950openalex-extracted
Supervised global path planning for mobile robots with obstacle avoidance
inferredcites →https://doi.org/10.1109/humanoids.2015.7363502openalex-extracted
Knowledge transfer for learning robot models via Local Procrustes Analysis
inferredcites →https://doi.org/10.1109/icra.2011.5980223openalex-extracted
Motion planning for autonomous driving with a conformal spatiotemporal lattice
inferredcites →https://doi.org/10.1109/icra48506.2021.9561723openalex-extracted
What data do we need for training an AV motion planner?
inferredcites →https://doi.org/10.1109/ijcnn.2013.6706721openalex-extracted
Alignment-based transfer learning for robot models
inferredcites →https://doi.org/10.1109/iros.2017.8206505openalex-extracted
Combining neural networks and tree search for task and motion planning in challenging environments
inferredcites →https://doi.org/10.1109/itsc.2019.8916986openalex-extracted
A Comparative Study on Transferable Driver Behavior Learning Methods in the Lane-Changing Scenario
inferredcites →https://doi.org/10.1109/itsc.2019.8917392openalex-extracted
Driving Decision and Control for Automated Lane Change Behavior based on Deep Reinforcement Learning
inferredcites →https://doi.org/10.1109/iv47402.2020.9304815openalex-extracted
Hierarchical Reinforcement Learning Combined with Motion Primitives for Automated Overtaking
inferredcites →https://doi.org/10.1109/ivs.2017.7995720openalex-extracted
Human-like behavior generation for intelligent vehicles in urban environment based on a hybrid potential map
inferredcites →https://doi.org/10.1109/ivs.2018.8500548openalex-extracted
End-to-End Driving Activities and Secondary Tasks Recognition Using Deep Convolutional Neural Network and Transfer Learning
inferredcites →https://doi.org/10.1109/ivs.2018.8500684openalex-extracted
Transfer Learning for Driver Model Adaptation via Modified Local Procrustes Analysis
inferredcites →https://doi.org/10.1109/ivs.2019.8813781openalex-extracted
Transferable Driver Behavior Learning via Distribution Adaption in the Lane Change Scenario
inferredcites →https://doi.org/10.1109/lra.2020.3011912openalex-extracted
Deep Reinforcement Learning for Safe Local Planning of a Ground Vehicle in Unknown Rough Terrain
inferredcites →https://doi.org/10.1109/lra.2020.3036597openalex-extracted
Towards Target-Driven Visual Navigation in Indoor Scenes via Generative Imitation Learning
inferredcites →https://doi.org/10.1109/msp.2020.2988287openalex-extracted
Deep Inverse Reinforcement Learning for Behavior Prediction in Autonomous Driving: Accurate Forecasts of Vehicle Motion
inferredcites →https://doi.org/10.1109/robio.2018.8665177openalex-extracted
Learning to Interrupt: A Hierarchical Deep Reinforcement Learning Framework for Efficient Exploration
inferredcites →https://doi.org/10.1109/tcst.2008.2012116openalex-extracted
Real-Time Motion Planning With Applications to Autonomous Urban Driving
inferredcites →https://doi.org/10.1109/tie.2019.2898599openalex-extracted
A Motion Planning and Tracking Framework for Autonomous Vehicles Based on Artificial Potential Field Elaborated Resistance Network Approach
inferredcites →https://doi.org/10.1109/tits.2010.2046037openalex-extracted
Maneuver-Based Trajectory Planning for Highly Autonomous Vehicles on Real Road With Traffic and Driver Interaction
inferredcites →https://doi.org/10.1109/tits.2012.2198214openalex-extracted
Local Path Planning for Off-Road Autonomous Driving With Avoidance of Static Obstacles
inferredcites →https://doi.org/10.1109/tits.2015.2498841openalex-extracted
A Review of Motion Planning Techniques for Automated Vehicles
inferredcites →https://doi.org/10.1109/tits.2019.2925510openalex-extracted
Transfer Learning for Driver Model Adaptation in Lane-Changing Scenarios Using Manifold Alignment
inferredcites →https://doi.org/10.1109/tkde.2006.16openalex-extracted
Text classification without negative examples revisit
inferredcites →https://doi.org/10.1109/tmech.2015.2493980openalex-extracted
Real-Time Trajectory Planning for Autonomous Urban Driving: Framework, Algorithms, and Verifications
inferredcites →https://doi.org/10.1109/tmi.2014.2366792openalex-extracted
Transfer Learning Improves Supervised Image Segmentation Across Imaging Protocols
inferredcites →https://doi.org/10.1109/tvt.2019.2917025openalex-extracted
Virtual-to-Real Knowledge Transfer for Driving Behavior Recognition: Framework and a Case Study
inferredcites →https://doi.org/10.1162/neco.1993.5.4.613openalex-extracted
Improving Generalization for Temporal Difference Learning: The Successor Representation
inferredcites →https://doi.org/10.1162/neco_a_00282openalex-extracted
The Successor Representation and Temporal Context
inferredcites →https://doi.org/10.1177/0278364911406761openalex-extracted
Sampling-based algorithms for optimal motion planning
inferredcites →https://doi.org/10.1523/jneurosci.0151-18.2018openalex-extracted
The Successor Representation: Its Computational Logic and Neural Substrates
inferredcites →https://doi.org/10.18653/v1/d17-1038openalex-extracted
Learning to select data for transfer learning with Bayesian Optimization
inferredcites →https://doi.org/10.3390/sym10100450openalex-extracted
Path Planning for the Mobile Robot: A Review
inferredcites →https://doi.org/10.48550/arxiv.1804.06264openalex-extracted
inferredcites →https://doi.org/10.48550/arxiv.2009.14509openalex-extracted
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