Gauss mixture hidden Markov model to characterise and model discretionary lane‐change behaviours for autonomous vehicles. Issue 5 (12th March 2020)

• Record Type: Journal Article
• Authors: Jin, Hao; Duan, Chunguang; Liu, Yang; Lu, Pingping
• Is Part Of: IET intelligent transport systems. Volume 14:Issue 5(2020)
• Page Start: 401
• Page End: 411
• Publication Date: 2020-03-12
• Subjects: traffic engineering computing -- hidden Markov models -- decision making -- Gaussian processes -- learning (artificial intelligence) -- road traffic; Gauss mixture -- Markov model -- discretionary lane‐change -- autonomous vehicles -- unacceptable issue -- actual drivers -- lane‐changing behaviour decision‐making model -- driver -- GM‐HMM method; Intelligent transportation systems -- Periodicals; Electronics in transportation -- Periodicals; 388.31205
• DOI: 10.1049/iet-its.2019.0446
• Languages: English
• ISSNs: 1751-956X

Abstract

Abstract : 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.