Open AccessGauss mixture hidden Markov model to characterise and model discretionary lane‐change behaviours for autonomous vehicles
Author(s) -
Jin Hao,
Duan Chunguang,
Liu Yang,
Lu Pingping
Publication year - 2020
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2019.0446
Subject(s) - hidden markov model , gauss , mixture model , computer science , artificial intelligence , markov model , machine learning , markov chain , simulation , engineering , physics , quantum mechanics
To solve the unacceptable issue caused by the inconsistency of lane‐changing behaviour between autonomous vehicles and actual drivers. A lane‐changing behaviour decision‐making model based on the Gauss mixture hidden Markov model (GM‐HMM) is proposed according to the characteristic of a driver's lane changing behaviour. The proposed model is tested and verified based on the database of Next‐Generation Simulation (NGSIM). The results show that the GM‐HMM is 95.4% similar to the real driver's behaviour. To further verify the proposed model, the proposed algorithm is compared with some machine learning techniques from literature in different test scenarios. The comparison and analysis indicate that the GM‐HMM method can more accurately simulate the real driver's lane‐change behaviour, thus improving the trust of the passengers and other vehicles around autonomous vehicles.