When do drivers concentrate? Attention-based driver behavior modeling with deep reinforcement learning
X Fu, F Gao, J Wu - arXiv preprint arXiv:2002.11385, 2020 - arxiv.org
… driver behaviors in modeling car-following behavior. Some researchers proposed data-driven
methods to learn driver behaviors … However, these driver behavior simulation approaches …
methods to learn driver behaviors … However, these driver behavior simulation approaches …
Inverse reinforcement learning via neural network in driver behavior modeling
QJ Zou, H Li, R Zhang - 2018 IEEE Intelligent Vehicles …, 2018 - ieeexplore.ieee.org
… to express driver behavior model based on the reward which recovered with IRL method
in this large-scale state space. In this paper, we focus on driving behavior modeling with IRL …
in this large-scale state space. In this paper, we focus on driving behavior modeling with IRL …
Driving behavior modeling using naturalistic human driving data with inverse reinforcement learning
… reinforcement learning with the proposed structural assumption to driving behavior modeling
from naturalistic highway driving … of the proposed method in driving behavior modeling both …
from naturalistic highway driving … of the proposed method in driving behavior modeling both …
Inverse reinforcement learning based stochastic driver behavior learning
… reinforcement learning-based driver behavior model is designed to learn a cost function
distribution from driver-… This paper presents a novel driver behavior learning approach that …
distribution from driver-… This paper presents a novel driver behavior learning approach that …
Driver modeling through deep reinforcement learning and behavioral game theory
… When compared with previous studies [2], [36], policies proposed in this work show more
realistic driving behavior since the average crash rate is 2 per million miles driven nationally [54…
realistic driving behavior since the average crash rate is 2 per million miles driven nationally [54…
Probabilistic prediction of interactive driving behavior via hierarchical inverse reinforcement learning
… Hence, to describe the influence of both discrete and continuous decisions, we propose a
hierarchical inverse reinforcement learning (IRL) framework in this paper, to learn trajectory-…
hierarchical inverse reinforcement learning (IRL) framework in this paper, to learn trajectory-…
Recent advances in reinforcement learning-based autonomous driving behavior planning: A survey
… Moreover, IL methods struggle with the complexity of capturing the nuances of human
driving behavior, which can lead to suboptimal or even unsafe driving decisions in unexpected …
driving behavior, which can lead to suboptimal or even unsafe driving decisions in unexpected …
Predicting driving behavior using inverse reinforcement learning with multiple reward functions towards environmental diversity
M Shimosaka, K Nishi, J Sato… - 2015 IEEE Intelligent …, 2015 - ieeexplore.ieee.org
… This paper presented a novel driver behavior model on residential roads by using inverse
reinforce learning with multiple reward functions in order to avoid insufficient amount of …
reinforce learning with multiple reward functions in order to avoid insufficient amount of …
Deep reinforcement learning based high-level driving behavior decision-making model in heterogeneous traffic
Z Bai, W Shangguan, B Cai… - 2019 Chinese Control …, 2019 - ieeexplore.ieee.org
… Abstract: High-level driving behavior decision-making is an … reinforcement learning based
high-level driving behavior … , and a deep reinforcement learning network that learns the optimal …
high-level driving behavior … , and a deep reinforcement learning network that learns the optimal …
Analyzing the suitability of cost functions for explaining and imitating human driving behavior based on inverse reinforcement learning
… In section III, we introduce our approach to inverse reinforcement learning of cost functions,
before we describe the application of the approach in section IV. Finally, we evaluate our …
before we describe the application of the approach in section IV. Finally, we evaluate our …
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