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

Zheng Wu, Fangbing Qu, Lin Yang, Jianwei Gong

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it

0cells of the map it votes in

2citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

Online Personalized Preference Learning Method Based on In-Formative Query for Lane Centering Control Trajectory.Sensors (Basel, Switzerland) · 2023
Article
Research on the Physics-Intelligence Hybrid Theory Based Dynamic Scenario Library Generation for Automated Vehicles.Sensors (Basel, Switzerland) · 2022
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Zheng WuVehicle Engineering Department, Inner Mongolia Technical College of Mechanics and Electrics, Huhehot 010070, China.

Fangbing QuSchool of Mechanical and Engineering, Beijing Institute of Technology, Beijing 100081, China.ORCID 0000-0002-7208-849X

Lin YangSchool of Mechanical and Engineering, Beijing Institute of Technology, Beijing 100081, China.

Jianwei GongSchool of Mechanical and Engineering, Beijing Institute of Technology, Beijing 100081, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the rapid development of autonomous driving technology, both self-driven and human-driven vehicles will share roads in the future and complex information exchange among vehicles will be required. Therefore, autonomous vehicles need to behave as similar to human drivers as possible, to ensure that their behavior can be effectively understood by the drivers of other vehicles and be more in line with the cognition of humans on driving behavior. Therefore, this paper studies the evaluation function of human drivers, using the method of inverse reinforcement learning, aiming for the learned behavior to better imitate the behavior of human drivers. At the same time, this paper proposes a semi-Markov model, to extract the intentions of surrounding related vehicles and divides them into defensive and cooperative, leading the vehicle to adopt a reasonable response to different types of driving scenarios.

Indexed as

Accidents, TrafficAutomobile DrivingAutonomous VehiclesDecision MakingHumansLearninghuman-like interactiveintelligent vehiclesinverse reinforcement learningsemi-Markov model

Identifiers

PMID35746281

DOI10.3390/s22124500

PMCPMC9227206

What OpenQuestion holds

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LicenceCC BY

Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.