Fuzzy inference systems for discretionary lane changing decisions: Model improvements and research challenges
The lane changing decision model (LCDM) is a critical component in semi- and fully-automated driving systems. Recent research has found that the fuzzy inference system (FIS) is a promising approach to implementing LCDMs. To improve the FIS’s performance, this research reviewed the challenges in the...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co., Ltd.
2025-03-01
|
| Series: | International Journal of Transportation Science and Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2046043024000480 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850271819175559168 |
|---|---|
| author | Ehsan Yahyazadeh Rineh Ruey Long Cheu |
| author_facet | Ehsan Yahyazadeh Rineh Ruey Long Cheu |
| author_sort | Ehsan Yahyazadeh Rineh |
| collection | DOAJ |
| description | The lane changing decision model (LCDM) is a critical component in semi- and fully-automated driving systems. Recent research has found that the fuzzy inference system (FIS) is a promising approach to implementing LCDMs. To improve the FIS’s performance, this research reviewed the challenges in the development an FIS model to make the yes,no decisions in discretionary lane changes. The FIS model was revised to bring its fuzzy inference rules more consistent with the fuzzy membership functions, and its composition and defuzzification methods more in line with the classical fuzzy logic theory. An equitable test data set with approximately equal number of yes,no data points was assembled from the same next generation simulation (NGSIM) data used in the past research. The test results proved that: (1) an LCDM’s performance was dependent on how the yes,no decisions in the test data set were manually labeled; (2) separating the fuzzy inference rules into a yes group and a no group and compute the results separately yielded potentially better decision accuracy. Furthermore, The gene expression programming model (GEPM) performed better than the improved FIS-based model. The findings led the authors to suggest two possible research directions: (1) add the subject vehicle’s speed as an input to the LCDM and redesign the decision-making model; (2) construct models for congested and uncongested traffic separately. The authors further suggested the use of instrumented vehicles to collect a set of high-fidelity lane changing data in the naturalistic driving environment. |
| format | Article |
| id | doaj-art-b31d318dce5e46f0b9178c1d479e6b2d |
| institution | OA Journals |
| issn | 2046-0430 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | International Journal of Transportation Science and Technology |
| spelling | doaj-art-b31d318dce5e46f0b9178c1d479e6b2d2025-08-20T01:52:06ZengKeAi Communications Co., Ltd.International Journal of Transportation Science and Technology2046-04302025-03-011731232710.1016/j.ijtst.2024.05.001Fuzzy inference systems for discretionary lane changing decisions: Model improvements and research challengesEhsan Yahyazadeh Rineh0Ruey Long Cheu1Department of Civil Engineering, The University of Texas at El Paso, El Paso, TX, United StatesCorresponding author.; Department of Civil Engineering, The University of Texas at El Paso, El Paso, TX, United StatesThe lane changing decision model (LCDM) is a critical component in semi- and fully-automated driving systems. Recent research has found that the fuzzy inference system (FIS) is a promising approach to implementing LCDMs. To improve the FIS’s performance, this research reviewed the challenges in the development an FIS model to make the yes,no decisions in discretionary lane changes. The FIS model was revised to bring its fuzzy inference rules more consistent with the fuzzy membership functions, and its composition and defuzzification methods more in line with the classical fuzzy logic theory. An equitable test data set with approximately equal number of yes,no data points was assembled from the same next generation simulation (NGSIM) data used in the past research. The test results proved that: (1) an LCDM’s performance was dependent on how the yes,no decisions in the test data set were manually labeled; (2) separating the fuzzy inference rules into a yes group and a no group and compute the results separately yielded potentially better decision accuracy. Furthermore, The gene expression programming model (GEPM) performed better than the improved FIS-based model. The findings led the authors to suggest two possible research directions: (1) add the subject vehicle’s speed as an input to the LCDM and redesign the decision-making model; (2) construct models for congested and uncongested traffic separately. The authors further suggested the use of instrumented vehicles to collect a set of high-fidelity lane changing data in the naturalistic driving environment.http://www.sciencedirect.com/science/article/pii/S2046043024000480Lane changeFuzzy inference system (FIS)Next generation simulation (NGSIM)Gap acceptanceGene expression programming |
| spellingShingle | Ehsan Yahyazadeh Rineh Ruey Long Cheu Fuzzy inference systems for discretionary lane changing decisions: Model improvements and research challenges International Journal of Transportation Science and Technology Lane change Fuzzy inference system (FIS) Next generation simulation (NGSIM) Gap acceptance Gene expression programming |
| title | Fuzzy inference systems for discretionary lane changing decisions: Model improvements and research challenges |
| title_full | Fuzzy inference systems for discretionary lane changing decisions: Model improvements and research challenges |
| title_fullStr | Fuzzy inference systems for discretionary lane changing decisions: Model improvements and research challenges |
| title_full_unstemmed | Fuzzy inference systems for discretionary lane changing decisions: Model improvements and research challenges |
| title_short | Fuzzy inference systems for discretionary lane changing decisions: Model improvements and research challenges |
| title_sort | fuzzy inference systems for discretionary lane changing decisions model improvements and research challenges |
| topic | Lane change Fuzzy inference system (FIS) Next generation simulation (NGSIM) Gap acceptance Gene expression programming |
| url | http://www.sciencedirect.com/science/article/pii/S2046043024000480 |
| work_keys_str_mv | AT ehsanyahyazadehrineh fuzzyinferencesystemsfordiscretionarylanechangingdecisionsmodelimprovementsandresearchchallenges AT rueylongcheu fuzzyinferencesystemsfordiscretionarylanechangingdecisionsmodelimprovementsandresearchchallenges |