Fault Detection Based on Parity Equations in Multiple Lane Road Car-Following Models Using Bayesian Lane Change Estimation

Pop, Mădălin-Dorin; Proştean, Octavian; Proștean, Gabriela

doi:10.3390/jsan9040052

Cited by 6 publications

(4 citation statements)

References 42 publications

(61 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A simulated car-following model has as main components a subsystem designed for parameters handling and a subsystem responsible for the control strategy of the vehicle behind (i.e., FV-follower vehicle) based on the observed behavior of the vehicle moving ahead (i.e., LV-leader vehicle), while ensuring collision avoidance [36]. In this research, the analysis of the calibration process requires the design and implementation of two calibration models, one using the Mamdani and the other the Takagi-Sugeno inference engines, in establishing the corresponding compensation values s_OFFSET_M and s_OFFSET_TS to be applied to the simulated running distance of FV (x 4 ).…”

Section: Methodsmentioning

confidence: 99%

“…According to Yin et al [37], this road traffic model consists of four levels of representation, such as crossroads configuration, links, lane choice, and car following. The last level mentioned considers a single lane of traffic where the vehicles are moving in a chain and describes the control strategy of the vehicle behind (FV-follower vehicle) based on the observed behavior of the vehicle moving ahead (LV-leader vehicle) [36].…”

Section: Continuous-time Car-following Modeling and Calibrationmentioning

confidence: 99%

See 1 more Smart Citation

Mamdani vs. Takagi–Sugeno Fuzzy Inference Systems in the Calibration of Continuous-Time Car-Following Models

Pop,

Pescaru,

Micea

2023

Sensors

Self Cite

View full text Add to dashboard Cite

The transition to intelligent transportation systems (ITSs) is necessary to improve traffic flow in urban areas and reduce traffic congestion. Traffic modeling simplifies the understanding of the traffic paradigm and helps researchers to estimate traffic behavior and identify appropriate solutions for traffic control. One of the most used traffic models is the car-following model, which aims to control the movement of a vehicle based on the behavior of the vehicle ahead while ensuring collision avoidance. Differences between the simulated and observed model are present because the modeling process is affected by uncertainties. Furthermore, the measurement of traffic parameters also introduces uncertainties through measurement errors. To ensure that a simulation model fully replicates the observed model, it is necessary to have a calibration process that applies the appropriate compensation values to the simulation model parameters to reduce the differences compared to the observed model parameters. Fuzzy inference techniques proved their ability to solve uncertainties in continuous-time models. This article aims to provide a comparative analysis of the application of Mamdani and Takagi–Sugeno fuzzy inference systems (FISs) in the calibration of a continuous-time car-following model by proposing a methodology that allows for parallel data processing and the determination of the simulated model output resulting from the application of both fuzzy techniques. Evaluation of their impact on the follower vehicle considers the running distance and the dynamic safety distance based on the observed behavior of the leader vehicle. In this way, the identification of the appropriate compensation values to be applied to the input of the simulated model has a great impact on the development of autonomous driving solutions, where the real-time processing of sensor data has a crucial impact on establishing the car-following strategy while ensuring collision avoidance. This research performs a simulation experiment in Simulink (MATLAB R2023a, Natick, MA, USA: The MathWorks Inc.) and considers traffic data collected by inductive loops as parameters of the observed model. To emphasize the role of Mamdani and Takagi–Sugeno FISs, a noise injection is applied to the model parameters with the help of a band-limited white-noise Simulink block to simulate sensor measurement errors and errors introduced by the simulation process. A discussion based on performance evaluation follows the simulation experiment, and even though both techniques can be successfully applied in the calibration of the car-following models, the Takagi–Sugeno FIS provides more accurate compensation values, which leads to a closer behavior to the observed model.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Continuous-time Car-following Modeling and Calibrationmentioning

confidence: 99%

Mamdani vs. Takagi–Sugeno Fuzzy Inference Systems in the Calibration of Continuous-Time Car-Following Models

Pop,

Pescaru,

Micea

2023

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…Six papers have been selected in this special issue that covers the secure design [1], emerging threats [2], counter measures [3,4], operation [5], and forensic investigation [6] methodologies of CPS and IoT systems.…”

mentioning

confidence: 99%

“…The second selected article, entitled "Fault Detection Based on Parity Equations in Multiple Lane Road Car-Following Models Using Bayesian Lane Change Estimation" submitted by researchers from Romania, addresses the safety of IoT-enabled transport systems [5]. This paper presents a fault detection analysis of the extension to a multiplelane car-following model that uses the Bayesian reasoning concept to estimate lane change behaviour.…”

mentioning

confidence: 99%