Human-like Decision Making for Autonomous Vehicles at the Intersection Using Inverse Reinforcement Learning

Zheng, Wei; Qu, Fangbing; Yang, Lin; Gong, Jianya

doi:10.3390/s22124500

Cited by 7 publications

(2 citation statements)

References 49 publications

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“…The general process of this method involves observing driver behavior by collecting driving data from a group of drivers, building a driver behavior or preference model, and obtaining a personalized vehicle controller based on the driving behavior model and the measured driving data of a new individual driver [3,4]. The driver behavior model can be built based on a steering or car-following driver model [10,11] and machine learning methods [12][13][14], such as inverse reinforcement learning, which has been used to learn human-like driving [15][16][17] since the work in Ref. [18].…”

Section: Introductionmentioning

confidence: 99%

Online Personalized Preference Learning Method Based on In-Formative Query for Lane Centering Control Trajectory

Wei

Chen

Xia

et al. 2023

Sensors

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The personalization of autonomous vehicles or advanced driver assistance systems has been a widely researched topic, with many proposals aiming to achieve human-like or driver-imitating methods. However, these approaches rely on an implicit assumption that all drivers prefer the vehicle to drive like themselves, which may not hold true for all drivers. To address this issue, this study proposes an online personalized preference learning method (OPPLM) that utilizes a pairwise comparison group preference query and the Bayesian approach. The proposed OPPLM adopts a two-layer hierarchical structure model based on utility theory to represent driver preferences on the trajectory. To improve the accuracy of learning, the uncertainty of driver query answers is modeled. In addition, informative query and greedy query selection methods are used to improve learning speed. To determine when the driver’s preferred trajectory has been found, a convergence criterion is proposed. To evaluate the effectiveness of the OPPLM, a user study is conducted to learn the driver’s preferred trajectory in the curve of the lane centering control (LCC) system. The results show that the OPPLM can converge quickly, requiring only about 11 queries on average. Moreover, it accurately learned the driver’s favorite trajectory, and the estimated utility of the driver preference model is highly consistent with the subject evaluation score.

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Section: Introductionmentioning

confidence: 99%

Online Personalized Preference Learning Method Based on In-Formative Query for Lane Centering Control Trajectory

Wei

Chen

Xia

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…With the continuous improvement of intelligence levels, the driving manipulation body of automated vehicles is transforming from human divers to an automated system [ 1 ]. Therefore, the testing and evaluation system (TES) of automated vehicles must be developed to verify their performance comprehensively and reliably [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Research on the Physics–Intelligence Hybrid Theory Based Dynamic Scenario Library Generation for Automated Vehicles

Zhang

Sun

et al. 2022

Sensors

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The testing and evaluation system has been the key technology and security with its necessity in the development and deployment of maturing automated vehicles. In this research, the physics–intelligence hybrid theory-based dynamic scenario library generation method is proposed to improve system performance, in particular, the testing efficiency and accuracy for automated vehicles. A general framework of the dynamic scenario library generation is established. Then, the parameterized scenario based on the dimension optimization method is specified to obtain the effective scenario element set. Long-tail functions for performance testing of specific ODD are constructed as optimization boundaries and critical scenario searching methods are proposed based on the node optimization and sample expansion methods for the low-dimensional scenario library generation and the reinforcement learning for the high-dimensional one, respectively. The scenario library generation method is evaluated with the naturalistic driving data (NDD) of the intelligent electric vehicle in the field test. Results show better efficient and accuracy performances compared with the ideal testing library and the NDD, respectively, in both low- and high-dimensional scenarios.

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A Personalized Ramp Merging Decision-Making Method for Autonomous Driving Based on Reverse Reinforcement Learning

Qu,

Qi,

Xiao

et al. 2024

Lecture Notes in Electrical Engineering

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Human-like Decision Making for Autonomous Vehicles at the Intersection Using Inverse Reinforcement Learning

Cited by 7 publications

References 49 publications

Online Personalized Preference Learning Method Based on In-Formative Query for Lane Centering Control Trajectory

Online Personalized Preference Learning Method Based on In-Formative Query for Lane Centering Control Trajectory

Research on the Physics–Intelligence Hybrid Theory Based Dynamic Scenario Library Generation for Automated Vehicles

A Personalized Ramp Merging Decision-Making Method for Autonomous Driving Based on Reverse Reinforcement Learning

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