scholarly journals An Extended Car-Following Model in Connected and Autonomous Vehicle Environment: Perspective from the Cooperation between Drivers

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Shenzhen Ding ◽  
Xumei Chen ◽  
Zexin Fu ◽  
Fei Peng
Keyword(s):  
Traffic Simulation ◽  
Autonomous Vehicle ◽  
Significant Heterogeneity ◽  
Mixed Traffic ◽  
Trajectory Data ◽  
Data Set ◽  
Microscopic Traffic Simulation ◽  
Car Following ◽  
Proposed Model ◽  
Car Following Model

The development of connected and autonomous vehicle (CAV) technology has received increasing attention in recent years. Although car-following behavior in mixed traffic with CAVs and human-driven vehicles (HDVs) is a core component of microscopic traffic simulation, intelligent traffic systems, etc., the current study of car-following behavior in mixed traffic has some limitations. Furthermore, actual data do not support its applicability to the Chinese traffic environment. To address this gap, this paper designs and organizes a car-following experiment in mixed traffic in Beijing, extracts the trajectory data of CAVs and HDVs based on video recognition, and reconstructs the extracted trajectory data using the Lagrangian theory and Kalman filter theory to ensure the accuracy of the data. Based on this data set, this paper develops an extended car-following model. The model considers the cooperation between drivers by reformulating the prospect theory (PT). The root mean square percentage error (RMSPE) is selected to calibrate and validate the parameters of the proposed model, and the results show that there is significant heterogeneity between CAVs and HDVs in mixed traffic, and the proposed model captures this heterogeneity well. The model presented in this paper provides theoretical support for microscopic traffic simulation in mixed traffic.

scholarly journals Introducing Road Surface Conditions into a Microscopic Traffic Simulation

10.29007/cqps ◽  
2019 ◽  
Author(s):  
Thomas Weber ◽  
Patrick Driesch ◽  
Dieter Schramm
Keyword(s):  
Traffic Simulation ◽  
Surface Condition ◽  
Road Surface ◽  
Automated Driving ◽  
Microscopic Traffic Simulation ◽  
Surface Conditions ◽  
Main Research ◽  
Car Following ◽  
The Road ◽  
Car Following Model

The introduction of highly automated driving functions is one of the main research and development efforts in the automotive industry worldwide. In the early stages of the development process, suppliers and manufacturers often wonder whether and to what extend the potential of the systems under development can be estimated in a cheap and timely manner. In the context of a current research project, a sensor system for the detection of the road surface condition is to be developed and it is to be investigated how such a system can be used to improve higher level driving functions. This paper presents how road surface conditions are introduced in various elements of the microscopic traffic simulation such as the actual network, the network editor, a device for detection, and an adaptation of the standard Krauß car following model. It is also shown how the adaptations can subsequently affect traffic scenarios. Furthermore, a summary is given how this preliminary work integrates into the larger scope of using SUMO as a tool in the process of analyzing the effectiveness of a road surface condition sensor.

scholarly journals Modeling of Car-Following Required Safe Distance Based on Molecular Dynamics

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Dayi Qu ◽  
Xiufeng Chen ◽  
Wansan Yang ◽  
Xiaohua Bian
Keyword(s):  
Molecular Dynamics ◽  
Traffic Simulation ◽  
Safe Distance ◽  
Microscopic Traffic Simulation ◽  
Car Following ◽  
Before And After ◽  
Car Following Model ◽  
The Relationship

In car-following procedure, some distances are reserved between the vehicles, through which drivers can avoid collisions with vehicles before and after them in the same lane and keep a reasonable clearance with lateral vehicles. This paper investigates characters of vehicle operating safety in car following state based on required safe distance. To tackle this problem, we probe into required safe distance and car-following model using molecular dynamics, covering longitudinal and lateral safe distance. The model was developed and implemented to describe the relationship between longitudinal safe distance and lateral safe distance under the condition where the leader keeps uniform deceleration. The results obtained herein are deemed valuable for car-following theory and microscopic traffic simulation.

Development of Operation Guidelines for Leader–Follower Autonomous Maintenance Vehicles at Work Zone Locations

2021 ◽  
pp. 036119812110566
Author(s):  
Qing Tang ◽  
Xianbiao Hu ◽  
Ruwen Qin
Keyword(s):  
Decision Making ◽  
Autonomous Vehicle ◽  
Field Testing ◽  
Work Zone ◽  
Lane Changing ◽  
The Public ◽  
Car Following ◽  
Technical Requirements ◽  
Car Following Model ◽  
Niche Area

The rapid advancement of connected and autonomous vehicle (CAV) technologies, although possibly years away from wide application to the general public travel, are receiving attention from many state Departments of Transportation (DOT) in the niche area of using autonomous maintenance technology (AMT) to reduce fatalities of DOT workers in work zone locations. Although promising results are shown in testing and deployments in several states, current autonomous truck mounted attenuator (ATMA) system operators are not provided with much practical driving guidance on how to drive these new vehicle systems in a way that is safe to both the public and themselves. To this end, this manuscript aims to model and develop a set of rules and instructions for ATMA system operators, particularly when it comes to critical locations where essential decision making is needed. Specifically, three technical requirements are investigated: car-following distance, critical lane-changing gap distance, and intersection clearance time. Newell’s simplified car-following model, and the classic lane-changing behavior model are modified, with roll-ahead distance taken into account, to model the driving behaviors of the ATMA vehicles at those critical decision-making locations. Data are collected from real-world field testing to calibrate and validate the developed models. The modeling outputs suggest important thresholds for ATMA system operators to follow. For example, on a freeway with a speed limit of 70 mph and ATMA operating speed of 10 mph, car-following distance should be no less than 75 ft for the lead truck and 100 ft for the follower truck, the critical lane-changing gap distance is 912 ft, and a minimum intersection clearance is 15 s, which are all much higher than the requirements for a general vehicle.

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.

Conformity and stability analysis of a modified spring–mass–damper system dynamics-based car-following model

2019 ◽  
Vol 33 (06) ◽  
pp. 1950025 ◽  
Author(s):  
Caleb Ronald Munigety
Keyword(s):  
Stability Analysis ◽  
System Dynamics ◽  
Traffic System ◽  
Car Following ◽  
Proposed Model ◽  
Mass Damper ◽  
Traffic Stream ◽  
Behavioral Parameters ◽  
Car Following Model ◽  
The Stability

Modeling the dynamics of a traffic system involves using the principles of both physical and social sciences since it is composed of vehicles as well as drivers. A novel car-following model is proposed in this paper by incorporating the socio-psychological aspects of drivers into the dynamics of a purely physics-based spring–mass–damper mechanical system to represent the driver–vehicle longitudinal movements in a traffic stream. The crux of this model is that a traffic system can be viewed as various masses interacting with each other by means of springs and dampers attached between them. While the spring and damping constants represent the driver behavioral parameters, the mass component represents the vehicle characteristics. The proposed model when tested for its ability to capture the traffic system dynamics both at micro, driver, and macro, stream, levels behaved pragmatically. The stability analysis carried out using perturbation method also revealed that the proposed model is both locally and asymptotically stable.

Calibrating Wiedemann-99 Model Parameters to Trajectory Data of Mixed Vehicular Traffic

2021 ◽  
pp. 036119812110375
Author(s):  
Ankit Anil Chaudhari ◽  
Karthik K. Srinivasan ◽  
Bhargava Rama Chilukuri ◽  
Martin Treiber ◽  
Ostap Okhrin
Keyword(s):  
Goodness Of Fit ◽  
Simulation Models ◽  
Microscopic Level ◽  
Model Parameters ◽  
Mixed Traffic ◽  
Relative Speed ◽  
Trajectory Data ◽  
Conventional Vehicle ◽  
Car Following ◽  
Emergency Braking

We propose a new methodology for calibrating Wiedemann-99 vehicle-following parameters for mixed traffic (different conventional vehicle classes) based on trajectory data. The existing acceleration equations of the Wiedemann model are modified to represent more realistic driving behavior. Exploratory analysis of simulation data revealed that different Wiedemann-99 model parameters could lead to similar macroscopic behavior, highlighting the importance of calibration at the microscopic level. Therefore, the proposed methodology is based on optimizing performance measures at the microscopic level (acceleration, speed, and trajectory profiles) to estimate suitable calibration parameters. Further, the goodness of fit for the observed data is sensitive to the numerical integration method used to compute vehicles’ velocity and position. We found that the calibrated parameters using the proposed methodology perform better than other approaches for calibrating mixed traffic. The results reveal that the calibrated parameter values and, consequently, the thresholds that delineate closing, following, emergency braking, and opening regimes, vary between two-wheelers and cars. The window (in the relative speed versus gap plot) for the unconscious following is larger for cars while the free-flow regime is more extensive for two-wheelers. Moreover, under the same relative speed and gap stimulus, two-wheelers and cars may be in different regimes and display different acceleration responses. Thus, accurate calibration of each vehicle’s parameters is essential for developing micro-simulation models for mixed traffic. The calibration analysis results of strict and overlapping staggered car following signify an impact of staggered car following compared with strict car following which demands separate calibration for strict and staggered following.

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