A BEHAVIOURAL CAR-FOLLOWING MODEL AND SOLVING THE QUICK THINKING DRIVER MODEL

2019 ◽  
Author(s):  
Zuzana Malacká
Keyword(s):  
Driver Model ◽  
Car Following ◽  
Car Following Model

scholarly journals The Research of Car-Following Model Based on Real-Time Maximum Deceleration

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Longhai Yang ◽  
Xiqiao Zhang ◽  
Jiekun Gong ◽  
Juntao Liu
Keyword(s):  
Real Time ◽  
Vehicle Dynamics ◽  
Driver Model ◽  
Cruise Control ◽  
Maximum Acceleration ◽  
Car Following ◽  
Time Headway ◽  
Asphalt Road ◽  
Car Following Model ◽  
Maximum Deceleration

This paper is concerned with the effect of real-time maximum deceleration in car-following. The real-time maximum acceleration is estimated with vehicle dynamics. It is known that an intelligent driver model (IDM) can control adaptive cruise control (ACC) well. The disadvantages of IDM at high and constant speed are analyzed. A new car-following model which is applied to ACC is established accordingly to modify the desired minimum gap and structure of the IDM. We simulated the new car-following model and IDM under two different kinds of road conditions. In the first, the vehicles drive on a single road, taking dry asphalt road as the example in this paper. In the second, vehicles drive onto a different road, and this paper analyzed the situation in which vehicles drive from a dry asphalt road onto an icy road. From the simulation, we found that the new car-following model can not only ensure driving security and comfort but also control the steady driving of the vehicle with a smaller time headway than IDM.

scholarly journals Modeling Car-Following Behaviors and Driving Styles with Generative Adversarial Imitation Learning

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5034
Author(s):  
Yang Zhou ◽  
Rui Fu ◽  
Chang Wang ◽  
Ruibin Zhang
Keyword(s):  
Autonomous Driving ◽  
Imitation Learning ◽  
Driver Model ◽  
Area Network ◽  
Car Following ◽  
Time Headway ◽  
Reward Function ◽  
Proposed Model ◽  
Wave Radar ◽  
Car Following Model

Building a human-like car-following model that can accurately simulate drivers’ car-following behaviors is helpful to the development of driving assistance systems and autonomous driving. Recent studies have shown the advantages of applying reinforcement learning methods in car-following modeling. However, a problem has remained where it is difficult to manually determine the reward function. This paper proposes a novel car-following model based on generative adversarial imitation learning. The proposed model can learn the strategy from drivers’ demonstrations without specifying the reward. Gated recurrent units was incorporated in the actor-critic network to enable the model to use historical information. Drivers’ car-following data collected by a test vehicle equipped with a millimeter-wave radar and controller area network acquisition card was used. The participants were divided into two driving styles by K-means with time-headway and time-headway when braking used as input features. Adopting five-fold cross-validation for model evaluation, the results show that the proposed model can reproduce drivers’ car-following trajectories and driving styles more accurately than the intelligent driver model and the recurrent neural network-based model, with the lowest average spacing error (19.40%) and speed validation error (5.57%), as well as the lowest Kullback-Leibler divergences of the two indicators used for driving style clustering.

scholarly journals Investigating the Impact of Connected and Automated Vehicles on Signalized and Unsignalized Intersections Safety in Mixed Traffic

2022 ◽  
Vol 2 (1) ◽  
pp. 24-40
Author(s):  
Amirhosein Karbasi ◽  
Steve O’Hern
Keyword(s):  
Case Studies ◽  
Human Error ◽  
Road Traffic ◽  
Driver Model ◽  
Cruise Control ◽  
Driver Assistance Systems ◽  
Road Traffic Crashes ◽  
Car Following ◽  
Car Following Model ◽  
The Impact

Road traffic crashes are a major safety problem, with one of the leading factors in crashes being human error. Automated and connected vehicles (CAVs) that are equipped with Advanced Driver Assistance Systems (ADAS) are expected to reduce human error. In this paper, the Simulation of Urban MObility (SUMO) traffic simulator is used to investigate how CAVs impact road safety. In order to define the longitudinal behavior of Human Drive Vehicles (HDVs) and CAVs, car-following models, including the Krauss, the Intelligent Driver Model (IDM), and Cooperative Adaptive Cruise Control (CACC) car-following models were used to simulate CAVs. Surrogate safety measures were utilized to analyze CAVs’ safety impact using time-to-collision. Two case studies were evaluated: a signalized grid network that included nine intersections, and a second network consisting of an unsignalized intersection. The results demonstrate that CAVs could potentially reduce the number of conflicts based on each of the car following model simulations and the two case studies. A secondary finding of the research identified additional safety benefits of vehicles equipped with collision avoidance control, through the reduction in rear-end conflicts observed for the CACC car-following model.

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.

Modeling and Evaluating Traffic Flow at Sag Curves When Imposing Variable Speed Limits on Connected Vehicles

2018 ◽  
Vol 2672 (20) ◽  
pp. 193-202 ◽  
Author(s):  
Reza Vatani Nezafat ◽  
Ehsan Beheshtitabar ◽  
Mecit Cetin ◽  
Elizabeth Williams ◽  
George F. List
Keyword(s):  
Traffic Flow ◽  
Optimization Method ◽  
Driver Model ◽  
Traffic Density ◽  
Connected Vehicles ◽  
Market Penetration ◽  
Car Following ◽  
Simulation Based ◽  
Variable Speed Limits ◽  
Car Following Model

Sag curves, road segments where the gradient changes from downwards to upwards, generally reduce the roadway capacity and cause congestion. This results from a change in longitudinal driving behavior when entering a sag curve as drivers tend to reduce speeds or increase headways as vehicles reach the uphill section. In this research, a control strategy is investigated through manipulating the speed of connected vehicles (CVs) in the upstream of the sag curve to avoid the formation of bottlenecks caused by the change in driver behavior. Traffic flow along a sag curve is simulated using the intelligent driver model (IDM), a time-continuous car-following model. A feedback control algorithm is developed for adjusting the approach speeds of CVs so that the throughput of the sag curve is maximized. Depending on the traffic density at the sag curve, adjustments are made for the speeds of the CVs. A simulation-based optimization method using a meta-heuristic algorithm is employed to determine the critical control parameters. Various market penetration rates for CVs are also considered in the simulations. Even at relatively low market penetration rates (e.g., 5–10%), significant improvements in travel times and throughput are observed.

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.

scholarly journals String Stability and Platoon Safety Analysis of a New Car-following Model Considering a Stabilization Strategy

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Mingfei Mu ◽  
Junjie Zhang ◽  
Changmiao Wang ◽  
Jun Zhang ◽  
Can Yang
Keyword(s):  
Safety Analysis ◽  
String Stability ◽  
Car Following ◽  
Car Following Model
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