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| Content Provider | IET Digital Library |
|---|---|
| Author | Jin, Hao Duan, Chunguang Liu, Yang Lu, Pingping |
| 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. |
| Starting Page | 401 |
| Ending Page | 411 |
| Page Count | 11 |
| ISSN | 1751956X |
| Volume Number | 14 |
| e-ISSN | 17519578 |
| Issue Number | Issue 5, May (2020) |
| Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/iet-its/14/5 |
| Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/iet-its.2019.0446 |
| Journal | IET Intelligent Transport Systems |
| Publisher Date | 2020-01-28 |
| Access Restriction | Open |
| Rights Holder | © The Institution of Engineering and Technology |
| Subject Keyword | Actual Drivers Autonomous Vehicle Decision Making Discretionary Lane-change Driver Gauss Mixture Gaussian Processes GM-HMM Method Hidden Markov Model Knowledge Engineering Technique Lane-changing Behaviour Decision-making Model Learning in AI Markov Model Markov Processes Road Traffic Traffic Engineering Computing Unacceptable Issue |
| Content Type | Text |
| Resource Type | Article |
| Subject | Law Transportation Environmental Science Mechanical Engineering |
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