This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
Study on Important Indices Related to Driver Feelings for LKA Intervention Process
Technical Paper
2018-01-1586
ISSN: 0148-7191, e-ISSN: 2688-3627
This content contains downloadable datasets
Annotation ability available
Sector:
Language:
English
Abstract
Lane Keeping Assistance (LKA) system is a very important part in Advanced Driver Assistance Systems (ADAS). It prevents a vehicle from departing out of the lane by exerting intervention. But an inappropriate performance during LKA intervention makes driver feel uncomfortable. The intervention of LKA can be divided into 3 parts: intervention timing, intervention process and intervention ending. Many researches have studied about the intervention timing and ending, but factors during intervention process also affect driver feelings a lot, such as yaw rate and steering wheel velocity. To increase driver’s acceptance of LKA, objective and subjective tests were designed and conducted to explore important indices which are highly correlated with the driver feelings.
Different kinds of LKA controller control intervention process in different ways. Therefore, it’s very important to describe the intervention process uniformly and objectively. This paper proposes 16 Characteristic Indices (CI), such as ‘maximum yaw rate’, to describe steering wheel motion, vehicle motion and other aspects during variable LKA intervention processes. Then, to acquire drivers’ subjective evaluation about LKA, a questionnaire including 3 questions from different aspects was designed for drivers to give Subjective Ratings (SR).
Lastly, to describe the nonlinear correlation between CI and SR, Random Forests (RF) algorithm was used to establish the correlation model. Different from other modeling methods, RF can not only build the model by data training, but also give out the importance of each CI in the model.
Through this method, important indices really affecting the driver feelings during the LKA intervention process were explored. What’s more, by the use of CI, the correlation model can predict the driver feelings regardless of specific LKA controller type, thus important indices can be optimized, which means the prediction about SR can be used to offer necessary guidance to the development and calibration of LKA system.
Authors
Citation
Zhong, B., Chen, H., Chen, J., Lan, X. et al., "Study on Important Indices Related to Driver Feelings for LKA Intervention Process," SAE Technical Paper 2018-01-1586, 2018, https://doi.org/10.4271/2018-01-1586.Data Sets - Support Documents
| Title | Description | Download |
|---|---|---|
| Unnamed Dataset 1 | ||
| Unnamed Dataset 2 | ||
| Unnamed Dataset 3 | ||
| Unnamed Dataset 4 | ||
| Unnamed Dataset 5 | ||
| Unnamed Dataset 6 | ||
| Unnamed Dataset 7 | ||
| Unnamed Dataset 8 | ||
| Unnamed Dataset 9 | ||
| Unnamed Dataset 10 |
Also In
References
- Eichelberger , A.H. and McCartt , A.T. Toyota Drivers' Experiences with Dynamic Radar Cruise Control, Pre-Collision System, and Lane-Keeping Assist Journal of Safety Research 56 67 73 2016 10.1016/j.jsr.2015.12.002
- Alkim , T. The Impact of Lateral ADAS in Traffic Safety Infrastructure and Safety in a Collaborative World Berlin, Heidelberg Springer 2011 191 214 10.1007/978-3-642-18372-0_10
- Kim , J.H. , Lee , S.M. , Shin , S.K. , and Jeong , S.H. Development of Active Lane Keeping Assist System SAE Technical Paper 2013-01-0718 2013 10.4271/2013-01-0718
- Mammar , S. , Glaser , S. , and Netto , M. Time to Line Crossing for Lane Departure Avoidance: A Theoretical Study and an Experimental Setting IEEE Transactions on Intelligent Transportation Systems 7 2 226 241 2006 10.1109/TITS.2006.874707
- Brandt , T. , Sattel , T. , and Bohm , M. Combining Haptic Human-Machine Interaction with Predictive Path Planning for Lane-Keeping and Collision Avoidance Systems Intelligent Vehicles Symposium, 2007 IEEE 2007 582 587 10.1109/IVS.2007.4290178
- Shimakage , M. , Kawazoe , H. , Sadano , O. , and Murakami , T. Design of Lane-Keeping Control with Steering Torque Input for a Lane-Keeping Support System SAE Technical Paper 2001-01-0480 2001 10.4271/2001-01-0480
- Lei , J. , Yang , J. , Zhao , J. , and Huali , W. Backstepping Sliding Mode Lane Keeping Control of Lateral Position Error with Dynamic of Tire Steering Device Optik-International Journal for Light and Electron Optics 127 5 2439 2443 2016 10.1016/j.ijleo.2015.11.100
- Katzourakis , D.I. , Lazic , N. , Olsson , C. , and Lidberg , M.R. Driver Steering Override for Lane-Keeping Aid Using Computer-Aided Engineering IEEE/ASME Transactions on Mechatronics 20 4 1543 1552 2015 10.1109/TMECH.2015.2401044
- Griffiths , P.G. and Brent Gillespie , R. Sharing Control between Humans and Automation Using Haptic Interface: Primary and Secondary Task Performance Benefits Human Factors 47 3 574 590 2005 10.1518/001872005774859944
- Dang , J. , Chen , H. , Li , Q. , Watanabe , T. et al. A Method to Set the Target on-Centre Steering Force Characteristic Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 230 12 1670 1686 2016 10.1177/0954407015619262
- Gómez , G. , Gaspar , L. , Nybacka , M. , Drugge , L. , and Bakker , E. Machine Learning to Classify and Predict Objective and Subjective Assessments of Vehicle Dynamics: the Case of Steering Feel Vehicle System Dynamics 56 1 150 171 2018 10.1080/00423114.2017.1351617
- Nybacka , M. , He , X. , Zhicheng , S. , Drugge , L. , and Bakker , E. Links between Subjective Assessments and Objective Metrics for Steering, and Evaluation of Driver Ratings Vehicle System Dynamics 52 1 31 50 2014 10.1080/00423114.2013.876503
- Trigila , A. , Iadanza , C. , Esposito , C. , and Scarascia-Mugnozza , G. Comparison of Logistic Regression and Random Forests Techniques for Shallow Landslide Susceptibility Assessment in Giampilieri (NE Sicily, Italy) Geomorphology 249 119 136 2015 10.1016/j.geomorph.2015.06.001
- Li , X. , Tian , H. , Wang , Y. , Li , R. et al. Vulnerability of 208 Endemic or Endangered Species in China to the Effects of Climate Change Regional Environmental Change 13 4 843 852 2013 10.1007/s10113-012-0344-z
- Kampichler , C. , Wieland , R. , Calmé , S. , Weissenberger , H. , and Arriaga-Weiss , S. Classification in Conservation Biology: A Comparison of Five Machine-Learning Methods Ecological Informatics 5 6 441 450 2010 10.1016/j.ecoinf.2010.06.003
- Iverson , L.R. , Prasad , A.M. , Matthews , S.N. , and Peters , M. Estimating Potential Habitat for 134 Eastern US Tree Species under Six Climate Scenarios Forest Ecology and Management 254 3 390 406 2008 10.1016/j.foreco.2007.07.023
- Lan , X. , Chen , H. , He , X. , Chen , J. et al. Driver Lane Keeping Characteristic Indices for Personalized Lane Keeping Assistance System SAE Technical Paper 2017-01-1982 2017 10.4271/2017-01-1982