scholarly journals A Car-Following Driver Model Capable of Retaining Naturalistic Driving Styles

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Jie Hu ◽  
Sheng Luo
Keyword(s):  
Real World ◽  
Intelligent Transportation ◽  
Driving Behavior ◽  
Research Topic ◽  
Driver Model ◽  
Speed Profile ◽  
The Real ◽  
Car Following ◽  
Control Paradigm ◽  
Naturalistic Driving

The modeling of car-following behavior is an attractive research topic in traffic simulation and intelligent transportation. The driver plays an important role in car following but is ignored by most car-following models. This paper presents a novel car-following driver model, which can retain aspects of human driving styles. First, simulated car-following data are generated by using the speed control driver model and the real-world driving behavior data if the real-world car-following data are not available. Then, the car-following driver model is established by imitating human driving maneuver during real-world car following. This is accomplished by using a neural network-based learning control paradigm and car-following data. Finally, the FTP-72 driving cycle is borrowed as the speed profile of the leading vehicle for the model test. The driving style is quantitatively analyzed by AESD. The results show that the proposed car-following driver model is capable of retaining the naturalistic driving styles while well accomplishing the car-following task with the error of relative distance mostly less than 5 meters for every driving styles.

Autonomous Vehicles’ Car-Following Drivability Evaluation Based on Driving Behavior Spectrum Reference Model

2021 ◽  
pp. 036119812199485
Author(s):  
Xiao Qi ◽  
Ying Ni ◽  
Yiming Xu ◽  
Ye Tian ◽  
Junhua Wang ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Reference Model ◽  
Numerical Test ◽  
Safety Evaluation ◽  
Driving Behavior ◽  
Car Following ◽  
Driving Behaviors ◽  
Human Driver ◽  
Normal Human ◽  
Naturalistic Driving

A large portion of the accidents involving autonomous vehicles (AVs) are not caused by the functionality of AV, but rather because of human intervention, since AVs’ driving behavior was not properly understood by human drivers. Such misunderstanding leads to dangerous situations during interaction between AV and human-driven vehicle (HV). However, few researches considered HV-AV interaction safety in AV safety evaluation processes. One of the solutions is to let AV mimic a normal HV’s driving behavior so as to avoid misunderstanding to the most extent. Therefore, to evaluate the differences of driving behaviors between existing AV and HV is necessary. DRIVABILITY is defined in this study to characterize the similarity between AV’s driving behaviors and expected behaviors by human drivers. A driving behavior spectrum reference model built based on human drivers’ behaviors is proposed to evaluate AVs’ car-following drivability. The indicator of the desired reaction time (DRT) is proposed to characterize the car-following drivability. Relative entropy between the DRT distribution of AV and that of the entire human driver population are used to quantify the differences between driving behaviors. A human driver behavior spectrum was configured based on naturalistic driving data by human drivers collected in Shanghai, China. It is observed in the numerical test that amongst all three types of preset AVs in the well-received simulation package VTD, the brisk AV emulates a normal human driver to the most extent (ranking at 55th percentile), while the default AV and the comfortable AV rank at 35th and 8th percentile, respectively.

scholarly journals A Vehicle Type Dependent Car-following Model Based on Naturalistic Driving Study

Electronics ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 453 ◽  
Author(s):  
Ping Wu ◽  
Feng Gao ◽  
Keqiang Li
Keyword(s):  
Gaussian Mixture ◽  
Identification Accuracy ◽  
Driver Model ◽  
Relative Distance ◽  
Car Following ◽  
Vehicle Type ◽  
Vehicle Velocity ◽  
Preceding Vehicle ◽  
Car Following Model ◽  
Naturalistic Driving

In this paper, a car-following model considering the preceding vehicle type is proposed to describe the longitudinal driving behavior closer to reality. Based on the naturalistic driving data sampled in real traffic for more than half a year, the relation between ego vehicle velocity and relative distance was analyzed by a multi-variable Gaussian Mixture model, from which it is found that the driver following behavior is influenced by the type of leading vehicle. Then a Hidden Markov model was designed to identify the vehicle type. This car-following model was trained and tested by using the naturalistic driving data. It can identify the leading vehicle type, i.e., passenger car, bus, and truck, and predict the ego vehicle velocity and relative distance based on a series of limited historical data in real time. The experimental validation results show that the identification accuracy of vehicle type under the static and dynamical conditions are 96.6% and 83.1%, respectively. Furthermore, comparing the results with the well-known collision avoidance model and intelligent driver model show that this new model is more accurate and can be used to design advanced driver assist systems for better adaptability to traffic conditions.

scholarly journals Driving Behavior Modeling Based on Consistent Variable Selection in a PWARX Model

2021 ◽  
Vol 11 (11) ◽  
pp. 4938
Author(s):  
Jude Chibuike Nwadiuto ◽  
Hiroyuki Okuda ◽  
Tatsuya Suzuki
Keyword(s):  
Decision Making ◽  
Variable Selection ◽  
Motion Control ◽  
Driving Behavior ◽  
Optimal Number ◽  
Behavior Modeling ◽  
Driver Model ◽  
Explanatory Variables ◽  
Car Following ◽  
Proposed Model

This paper proposes the hybrid system model identified by a PWARX (piecewise affine autoregressive exogenous) model for modeling human driving behavior. In the proposed model, the mode segmentation is carried out automatically and the optimal number of modes is decided by a novel methodology based on consistent variable selection. In addition, model flexibility is added within the ARX (autoregressive exogenous) partitions in the form of statistical variable selection. The proposed method is able to capture both the decision-making and motion-control facets of the driving behavior. The resulting model is an optimal basal model which is not affected by the choice of data, where the explanatory variables are allowed to vary within each ARX region, thus, allowing a higher-level understanding of the motion-control aspect of the driving behavior, as well as explaining the driver’s decision-making. The proposed model is applied to model the car-following driving task based on real-road driving data, as well as to ROS-CARLA-based car-following simulation and compared to Gipp’s driver model. Obtained results that show better performance both on prediction performance and mimicking actual real-road driving demonstrates and validates the usefulness of the model.

Identifying Contributory Factors to Heterogeneity in Driving Behavior: Clustering and Classification Approach

2019 ◽  
Vol 2673 (10) ◽  
pp. 343-353
Author(s):  
Rachel M. James ◽  
Britton E. Hammit
Keyword(s):  
Clustering Algorithm ◽  
Driving Behavior ◽  
Driver Model ◽  
Classification Algorithms ◽  
Homogeneous Groups ◽  
Car Following ◽  
Level Data ◽  
Car Following Model ◽  
Clustering And Classification ◽  
Naturalistic Driving Study

Previous research efforts using aerially collected trajectory-level data have confirmed the existence of inter-driver heterogeneity, where different car-following model (CFM) specifications and calibrated parameter sets are required to adequately capture drivers’ driving behavior. This research hypothesizes that there also exist clusters of drivers whose behavior is sufficiently similar to be considered a homogeneous group. To test this hypothesis, this study applies a 664-trip sample of trajectory-level data from the SHRP2 Naturalistic Driving Study to calibrate the Gipps, Intelligent Driver Model, and Wiedemann 99 CFMs. Using the calibrated parameter coefficients, this research provides evidence of the existence of homogeneous groups of driving behavior using the expectation maximization clustering algorithm. Four classification algorithms are then applied to classify the trip’s cluster ID according to driver demographics. Driver age, income, and marital status were most commonly identified as important classification attributes, while gender, work status, and living status appear less significant. The classification algorithms, which sought to classify a trip’s behavioral cluster ID by the driver-specific attributes, achieved the highest accuracy rate when predicting the desired velocity car-following parameter clusters. This effort illustrates that some drivers drive sufficiently alike to form a cluster of similar behavior; moreover, it was confirmed that driver-specific attributes can be utilized to classify drivers into these homogeneous driver groups.

Drivers overtaking cyclists in the real-world: Evidence from a naturalistic driving study

2019 ◽  
Vol 119 ◽  
pp. 199-206 ◽  
Author(s):  
Jordanka Kovaceva ◽  
Gustav Nero ◽  
Jonas Bärgman ◽  
Marco Dozza
Keyword(s):  
Real World ◽  
The Real ◽  
Real World Evidence ◽  
Naturalistic Driving Study ◽  
Naturalistic Driving

How to Model Real-World Driving Behavior? Probability-Based Driver Model for Energy Analyses

2019 ◽  
Author(s):  
Tobias Schuermann ◽  
Michael Bargende ◽  
Kai André Boehm ◽  
Tobias Goedecke ◽  
Stefan Schmiedler ◽  
...  
Keyword(s):  
Real World ◽  
Driving Behavior ◽  
Driver Model
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