Simulations of highway chaos using fuzzy logic

Das, Sanjoy; Bowles, B.

doi:10.1109/nafips.1999.781668

Cited by 26 publications

(13 citation statements)

References 3 publications

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“…The fuzzy logic model can better deal with the uncertainty in driving decision, and it consists of several if-then rules. For example, Das and Bowles [4] proposed a gap acceptance model based on the fuzzy logic, which mainly considered the speed of the front and rear vehicle in the target lane, and the distance between them and the target vehicle. Balal et al [5] developed a LCD model based on the fuzzy reasoning system, which took the distance between the main vehicle and the host vehicle as the LCD factors.…”

Section: Related Workmentioning

confidence: 99%

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DP and DS-LCD: A New Lane Change Decision Model Coupling Driver’s Psychology and Driving Style

Tao

et al. 2020

IEEE Access

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Lane-Changing Decision (LCD) behavior is complex, dangerous, and varied by the Driver's Psychology (DP) and Driving Style (DS). For lacking the consideration of DP and DS, the existing LCD models cannot predict the LCD process varying with the drivers and traffic conditions accurately. To deal with the problems, a new LCD model coupling DP and DS, named DP&DS-LCD, is put forward. In the model, a psychological field model is constructed to represent the DP effect on the scene vehicles. And then, K-means and K-Nearest Neighbor (KNN) algorithm are respectively adopted in learning and recognition phases to recognize the current driving style pattern. Finally, based on the DP and DS, the multi-Grained Cascade Forest (gcForest) algorithm is applied to predict the LCD behavior. In experiments, DP&DS-LCD is compared with other three LCD models by using the opening I-80 database from Next Generation Simulation project (NGSIM). And the results showed that the DP&DS-LCD model achieved the best performance. Therefore, the DP&DS-LCD model is effective and could provide support for the decision of autonomous vehicles by predicting the surrounding vehicles' Lane-Changing (LC) behavior. INDEX TERMS Autonomous vehicles, driver's psychology, driving style, lane change decision.

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Section: Related Workmentioning

confidence: 99%

“…Currently, many methods have been presented to predict LCD (e.g. rule-based [1]- [3], fuzzy reasoning [4]- [6], datadriven algorithm [7]- [10], etc.). However, these studies focused on modeling the surrounding traffic conditions on LC vehicles, and lacked consideration of DP and DS.…”

Section: Introductionmentioning

confidence: 99%

DP and DS-LCD: A New Lane Change Decision Model Coupling Driver’s Psychology and Driving Style

Tao

et al. 2020

IEEE Access

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“…Finally, the defuzzification stage converts the fuzzified outputs back to crisp values. Variants of FIS have been applied to model different aspects of lane changing process [3,8,16], but with lesser degree of success compared to [2]. A fuzzy set is a set of several linguistic values that a variable may take on.…”

Section: Fuzzy Inference Systemmentioning

confidence: 99%

Comparative Evaluation of Fuzzy Inference System, Support Vector Machine and Multilayer Feed-Forward Neural Network in Making Discretionary Lane Changing Decisions

Balal¹,

Cheu²

2018

NNW

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This paper compares Fuzzy Inference System (FIS), Support Vector Machine (SVM) and MultiLayer Feed-forward neural network (MLF) in modeling a driver's decision when making a discretionary lane changing move on a freeway. The FIS model has been developed and published in an earlier work by the authors, whereas the SVM and MLF models are newly developed in this research. The FIS, SVM and MLF models use the same four inputs: the gap between the subject vehicle and the leading vehicle in the original lane, the gap between the subject vehicle and the leading vehicle in the destination lane, the gap between the subject vehicle and the trailing vehicle in the destination lane, and the distance between the preceding and trailing vehicles in the destination lane. The models give a binary decision of "no, stay in the same lane" or "yes, move to the destination lane now". These models were trained and then tested with the Next Generation SIMulation (NGSIM) vehicle trajectory data. The results have shown that the FIS has the highest accuracies in making correct lane changing decisions. It recommends "yes, move to the destination lane now" with 82.2 % accuracy, and "no, stay in the same lane" with 99.5 % accuracy. The SVM model also outperformed the traditional gap acceptance model which was used as the benchmark. However, the MLF model was not as accurate as the gap acceptance model.

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“…In this model, lane-changing maneuvers are based on two premises: changing to a slower lane and changing to a faster lane. Das and Bowles proposed a new microscopic simulation methodology based on fuzzy rules for implementation in the Autonomous Agent SIMulation Package (AASIM) software [71]. In this fuzzy-logic-based model, lane-changing maneuvers are classified as MLC and DLC.…”

Section: A Fuzzy-logic-based Modelsmentioning

confidence: 99%

Review of Microscopic Lane-Changing Models and Future Research Opportunities

Rahman

Chowdhury

Xie

et al. 2013

IEEE Trans. Intell. Transport. Syst.

181

107

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Driver behaviors, particularly lane-changing behaviors, have an important effect on the safety and throughput of the roadway-vehicle-based transportation system. Lane-changing models are a vital component of various microscopic traffic simulation tools, which are extensively used and playing an increasingly important role in Intelligent Transportation Systems studies. The authors conducted a detailed review and systematic comparison of existing microscopic lane-changing models that are related to roadway traffic simulation to provide a better understanding of respective properties, including strengths and weaknesses of the lane-changing models, and to identify potential for model improvement using existing and emerging data collection technologies. Many models have been developed in the last few decades to capture the uncertainty in lane change modeling; however, lane-changing behavior in the real world is very complex due to driver distraction (e.g., texting and cellphone or smartphone use) and environmental (e.g., pavement and lighting conditions) and geometric (e.g., horizontal and vertical curves) factors of the roadway, which have not been adequately considered in existing models. Therefore, large and detailed microscopic vehicle trajectory data sets are needed to develop new lane changing models that address these issues, and to calibrate and validate lane-changing models for representing the real world reliably. Possible measures to improve the accuracy and reliability of lane-changing models are also discussed in this paper.Index Terms-Driver behavior, lane-changing models.

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Simulations of highway chaos using fuzzy logic

Cited by 26 publications

References 3 publications

DP and DS-LCD: A New Lane Change Decision Model Coupling Driver’s Psychology and Driving Style

DP and DS-LCD: A New Lane Change Decision Model Coupling Driver’s Psychology and Driving Style

Comparative Evaluation of Fuzzy Inference System, Support Vector Machine and Multilayer Feed-Forward Neural Network in Making Discretionary Lane Changing Decisions

Review of Microscopic Lane-Changing Models and Future Research Opportunities

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