Scenario understanding and motion prediction for autonomous vehicles—review and comparison
Scenario understanding and motion prediction are essential components for completely
replacing human drivers and for enabling highly and fully automated driving (SAE-Level …
replacing human drivers and for enabling highly and fully automated driving (SAE-Level …
A review of mobile robot motion planning methods: from classical motion planning workflows to reinforcement learning-based architectures
Motion planning is critical to realize the autonomous operation of mobile robots. As the
complexity and randomness of robot application scenarios increase, the planning capability …
complexity and randomness of robot application scenarios increase, the planning capability …
Driving behavior modeling using naturalistic human driving data with inverse reinforcement learning
Driving behavior modeling is of great importance for designing safe, smart, and
personalized autonomous driving systems. In this paper, an internal reward function-based …
personalized autonomous driving systems. In this paper, an internal reward function-based …
Efficient deep reinforcement learning with imitative expert priors for autonomous driving
Deep reinforcement learning (DRL) is a promising way to achieve human-like autonomous
driving. However, the low sample efficiency and difficulty of designing reward functions for …
driving. However, the low sample efficiency and difficulty of designing reward functions for …
Conditional predictive behavior planning with inverse reinforcement learning for human-like autonomous driving
Making safe and human-like decisions is an essential capability of autonomous driving
systems, and learning-based behavior planning presents a promising pathway toward …
systems, and learning-based behavior planning presents a promising pathway toward …
Analyzing the suitability of cost functions for explaining and imitating human driving behavior based on inverse reinforcement learning
Autonomous vehicles are sharing the road with human drivers. In order to facilitate
interactive driving and cooperative behavior in dense traffic, a thorough understanding and …
interactive driving and cooperative behavior in dense traffic, a thorough understanding and …
A human factors approach to validating driver models for interaction-aware automated vehicles
A major challenge for autonomous vehicles is interacting with other traffic participants safely
and smoothly. A promising approach to handle such traffic interactions is equipping …
and smoothly. A promising approach to handle such traffic interactions is equipping …
Medirl: Predicting the visual attention of drivers via maximum entropy deep inverse reinforcement learning
Inspired by human visual attention, we propose a novel inverse reinforcement learning
formulation using Maximum Entropy Deep Inverse Reinforcement Learning (MEDIRL) for …
formulation using Maximum Entropy Deep Inverse Reinforcement Learning (MEDIRL) for …
Communication resources constrained hierarchical federated learning for end-to-end autonomous driving
While federated learning (FL) improves the generalization of end-to-end autonomous driving
by model aggregation, the conventional single-hop FL (SFL) suffers from slow convergence …
by model aggregation, the conventional single-hop FL (SFL) suffers from slow convergence …
Robot learning from demonstration for path planning: A review
ZW Xie, Q Zhang, ZN Jiang, H Liu - Science China Technological …, 2020 - Springer
Learning from demonstration (LfD) is an appealing method of helping robots learn new
skills. Numerous papers have presented methods of LfD with good performance in robotics …
skills. Numerous papers have presented methods of LfD with good performance in robotics …