scholarly journals Development of a conflict-free unsignalized intersection organization method for multiple connected and autonomous vehicles

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0249170
Author(s):  
Qinglu Ma ◽  
Shu Zhang ◽  
Qi Zhou
Keyword(s):  
Traffic Control ◽  
Autonomous Vehicles ◽  
Control Method ◽  
Operating Efficiency ◽  
Signal Control ◽  
Vehicle Speed ◽  
Operating Characteristics ◽  
Right Of Way ◽  
Unsignalized Intersections ◽  
Traffic Organization

An effective traffic control strategy will improve travel reliability in urban transportation networks. Lack of coordination between vehicles, however, exacerbates congestion due mainly to frequent stops at unsignalized intersections. It is beneficial to develop a conflict-free cooperation method that collects basic safety message from multiple approaching Connected and Autonomous Vehicles (for short, CAVs) and guarantees efficient unsignalized intersection operations with safe and incident free vehicle maneuvers. This paper proposes an interspersed traffic organization method under controlled constraints. Firstly, relied on shared location technology and considered the operating characteristics of CAVs at unsignalized intersections to detect and analyze traffic conflicts to establish a right-of-way judgment model for CAVs. In order to further ensure the safety and operating efficiency of the vehicle, based on the judgment results of right-of-way judgment model, a vehicle speed guidance model is established for different traffic conditions. Taking the real city standard intersection as the experimental analysis object, through data collection and simulation experiment, the signal control method and the organization method proposed in this paper are compared and analyzed. The results showed that the traffic organization method proposed in this paper improves the operational efficiency of 46%, the average travel time is reduced by 6.54s, which is not only better than the signal control method, but also supports the development of car networking technology.

Prediction of Vehicular Traffic Flow using Levenberg-Marquardt Artificial Neural Network Model: Italy Road Transportation System

2022 ◽  
Vol 24 (2) ◽  
pp. E74-E86
Author(s):  
Isaac Oyeyemi Olayode ◽  
Alessandro Severino ◽  
Tiziana Campisi ◽  
Lagouge Kwanda Tartibu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Traffic Flow ◽  
Traffic Control ◽  
Traffic Congestion ◽  
Autonomous Vehicles ◽  
Time Of Day ◽  
Vehicle Speed ◽  
Road Transportation ◽  
Artificial Neural

In the last decades, the Italian road transport system has been characterized by severe and consistent traffic congestion and in particular Rome is one of the Italian cities most affected by this problem. In this study, a LevenbergMarquardt (LM) artificial neural network heuristic model was used to predict the traffic flow of non-autonomous vehicles. Traffic datasets were collected using both inductive loop detectors and video cameras as acquisition systems and selecting some parameters including vehicle speed, time of day, traffic volume and number of vehicles. The model showed a training, test and regression value (R2) of 0.99892, 0.99615 and 0.99714 respectively. The results of this research add to the growing body of literature on traffic flow modelling and help urban planners and traffic managers in terms of the traffic control and the provision of convenient travel routes for pedestrians and motorists.

Developing and Implementing Manual on Uniform Traffic Control Devices Criteria for Selecting Traffic Control for Unsignalized Intersections

2019 ◽  
Vol 2673 (11) ◽  
pp. 319-331
Author(s):  
H. Gene Hawkins ◽  
Kay Fitzpatrick ◽  
Marcus A. Brewer
Keyword(s):  
Traffic Control ◽  
Traffic Engineering ◽  
High Speed ◽  
Research Team ◽  
Right Of Way ◽  
Unsignalized Intersections ◽  
Control Devices ◽  
Traffic Control Devices ◽  
Urban Conditions ◽  
Selection Of

The 2009 United States Manual on Uniform Traffic Control Devices (MUTCD) includes guidance for the use of various types of traffic control at unsignalized intersections. Despite changes and advances in traffic engineering in recent decades, the MUTCD content related to selection of traffic control in Part 2B has seen only minor changes since 1971. The types of unsignalized traffic control addressed in the current research included no control, yield control, two-way stop control, and all-way stop control. The research team developed recommendations using information available from reviews of existing literature, policies, guidelines, and findings from an economic analysis along with the engineering judgment of the research team and panel. The researchers then developed recommended language for the next edition of the MUTCD for unsignalized intersections. This includes consideration of high-speed (rural) and low-speed (urban) conditions along with the number of legs at the intersection. Because the number of expected crashes at an intersection is a function of the number of legs, the decision on appropriate traffic control should also be sensitive to the number of legs present. The proposed language includes introductory general considerations, discusses alternatives to changing right-of-way control, and steps through the various forms of unsignalized control from least restrictive to most restrictive, beginning with no control and concluding with all-way stop control.

Optimal Methods of Traffic Control Systems for Pedestrians Crossing Signalized Intersection under Urban Overpasses

2014 ◽  
Vol 543-547 ◽  
pp. 1237-1241
Author(s):  
Bo Hang Liu ◽  
Xiao Xia Liu ◽  
Wen Sheng Zhang
Keyword(s):  
Control Systems ◽  
Traffic Control ◽  
Control Method ◽  
Signalized Intersection ◽  
Signal Control ◽  
Two Phase ◽  
Control Approach ◽  
Intersection Signal Control ◽  
Vehicle Delay ◽  
Special Case

The vehicle delay is a key problem in intersection signal control system. Because there is enough security space for pedestrians under urban overpasses, the traffic control approach of pedestrians crossing signalized intersection may be further improved. To the above problems, this paper shows two-phase control method in pedestrians crossing signalized intersection under urban overpasses. According to this method, the vehicle delay affected by pedestrians crossing the signalized intersection is studied. Meanwhile, the variation trend of vehicle delay under different road widths are also analyzed. At last, an overpass is conducted for example analysis, and the result shows that this approach is practical and feasible for the special case of pedestrians crossing signal control intersection under urban overpasses, it can effectively reduce the pedestrian and vehicle delay.

scholarly journals Multi-Agent Based Traffic Signal Optimization under Fully Actuated Control

2019 ◽  
Vol 8 (11) ◽  
pp. 2255-2262
Keyword(s):  
Traffic Control ◽  
Control Method ◽  
Control Model ◽  
Traffic Signal ◽  
Signal Control ◽  
Light Phase ◽  
Agent Based ◽  
Effective Manner ◽  
Multi Agent ◽  
On The Road

This paper presents a multi-agent based distributed traffic control model to optimize the traffic signal for multiple intersections. Previous works in the area of traffic signal control suffer from a number of inadequacies, including the use of fixed cycle length, centralized mode of operations and dependency on historical data. Considering these, the aim of this work is to control the traffic signal timings by adjusting the phase sequence in order to minimize the delay in traffic at the intersections. To model the traffic network, a three-tier multi-agent system has been adopted in distributed mode. In addition, a fully actuated signal control algorithm is designed and it utilizes state-space equations to formulate the queue length at the green light phase and red light phase. The proposed model is simulated with SUMO simulator and a comparative analysis has been made between adaptive control method, multi-agent method based on collective learning and multi-agent based fully actuated control method on a similar platform. The results spectacle the proposed traffic control model outperforms that of other existing control methods in all condition; hence it can be deployed to control the tremendous traffic on the road network and to optimize the traffic signal in more effective manner.

scholarly journals Human-Machine Shared Driving Control for Semi-Autonomous Vehicles Using Level of Cooperativeness

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4647
Author(s):  
Anh-Tu Nguyen ◽  
Jagat Jyoti Rath ◽  
Chen Lv ◽  
Thierry-Marie Guerra ◽  
Jimmy Lauber
Keyword(s):  
Autonomous Vehicles ◽  
Control Method ◽  
Shared Control ◽  
Control Technique ◽  
Vehicle Speed ◽  
Time Varying ◽  
Vehicle Model ◽  
Human Machine Interaction ◽  
Human Driver ◽  
Lane Keeping

This paper proposes a new haptic shared control concept between the human driver and the automation for lane keeping in semi-autonomous vehicles. Based on the principle of human-machine interaction during lane keeping, the level of cooperativeness for completion of driving task is introduced. Using the proposed human-machine cooperative status along with the driver workload, the required level of haptic authority is determined according to the driver’s performance characteristics. Then, a time-varying assistance factor is developed to modulate the assistance torque, which is designed from an integrated driver-in-the-loop vehicle model taking into account the yaw-slip dynamics, the steering dynamics, and the human driver dynamics. To deal with the time-varying nature of both the assistance factor and the vehicle speed involved in the driver-in-the-loop vehicle model, a new ℓ∞ linear parameter varying control technique is proposed. The predefined specifications of the driver-vehicle system are guaranteed using Lyapunov stability theory. The proposed haptic shared control method is validated under various driving tests conducted with high-fidelity simulations. Extensive performance evaluations are performed to highlight the effectiveness of the new method in terms of driver-automation conflict management.

Study on Traffic Control of Single Intersection Based on Rough Set

2014 ◽  
Vol 513-517 ◽  
pp. 3644-3650
Author(s):  
Xiang Jun Cheng
Keyword(s):  
Rough Set ◽  
Traffic Control ◽  
Control Method ◽  
Fixed Time ◽  
Current Phase ◽  
Signal Control ◽  
Control Rule ◽  
Rule Set ◽  
Relationship Of ◽  
The Relationship

On the basis of actuated control method, the number of car arrived of the current phase and the number of car arrived of the next phase are considered altogether. It is decided to prolong the green time of the current phase according to the relationship of the number of cars arrived of the current phase and the next phase. The experience and principle of traffic control are described as knowledge here. The entire condition of intersection is consisted from classified arrived car number. The knowledge face to the controlling of isolate intersection is applied in the space of intersection state. Then a rough set is formed. Finally, signal control rule set face to every situation is made after some work was done to make one condition corresponding to one action of signal control. The results of simulation indicates that the total stop delay of cars of this signal control method based on rough set reduces 23.1% averagely than the fixed-time approach and reduces 9.5% averagely than the actuated approach in the same condition.

The human-like trajectory planning for autonomous vehicles based on optimal control in a test track environment

2021 ◽  
pp. 095440702110090
Author(s):  
Xing Xu ◽  
Minglei Li ◽  
Feng Wang ◽  
Ju Xie ◽  
Xiaohan Wu ◽  
...  
Keyword(s):  
Optimal Control ◽  
Autonomous Vehicles ◽  
Optimal Trajectory ◽  
Control Method ◽  
Autonomous Vehicle ◽  
Trajectory Data ◽  
Test Track ◽  
Optimal Control Method ◽  
The Future ◽  
On The Road

A human-like trajectory could give a safe and comfortable feeling for the occupants in an autonomous vehicle especially in corners. The research of this paper focuses on planning a human-like trajectory along a section road on a test track using optimal control method that could reflect natural driving behaviour considering the sense of natural and comfortable for the passengers, which could improve the acceptability of driverless vehicles in the future. A mass point vehicle dynamic model is modelled in the curvilinear coordinate system, then an optimal trajectory is generated by using an optimal control method. The optimal control problem is formulated and then solved by using the Matlab tool GPOPS-II. Trials are carried out on a test track, and the tested data are collected and processed, then the trajectory data in different corners are obtained. Different TLCs calculations are derived and applied to different track sections. After that, the human driver’s trajectories and the optimal line are compared to see the correlation using TLC methods. The results show that the optimal trajectory shows a similar trend with human’s trajectories to some extent when driving through a corner although it is not so perfectly aligned with the tested trajectories, which could conform with people’s driving intuition and improve the occupants’ comfort when driving in a corner. This could improve the acceptability of AVs in the automotive market in the future. The driver tends to move to the outside of the lane gradually after passing the apex when driving in corners on the road with hard-lines on both sides.

Application of neural networks in predictions of brake wear particulate matter emission

2021 ◽  
pp. 095440702110363
Author(s):  
Saša Vasiljević ◽  
Jasna Glišović ◽  
Nadica Stojanović ◽  
Ivan Grujić
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Particulate Matter ◽  
Traffic Control ◽  
World Health ◽  
Vehicle Speed ◽  
System Pressure ◽  
The Neural Network ◽  
Vehicle Systems ◽  
Brake Wear

According to the World Health Organization, air pollution with PM10 and PM2.5 (PM-particulate matter) is a significant problem that can have serious consequences for human health. Vehicles, as one of the main sources of PM10 and PM2.5 emissions, pollute the air and the environment both by creating particles by burning fuel in the engine, and by wearing of various elements in some vehicle systems. In this paper, the authors conducted the prediction of the formation of PM10 and PM2.5 particles generated by the wear of the braking system using a neural network (Artificial Neural Networks (ANN)). In this case, the neural network model was created based on the generated particles that were measured experimentally, while the validity of the created neural network was checked by means of a comparative analysis of the experimentally measured amount of particles and the prediction results. The experimental results were obtained by testing on an inertial braking dynamometer, where braking was performed in several modes, that is under different braking parameters (simulated vehicle speed, brake system pressure, temperature, braking time, braking torque). During braking, the concentration of PM10 and PM2.5 particles was measured simultaneously. The total of 196 measurements were performed and these data were used for training, validation, and verification of the neural network. When it comes to simulation, a comparison of two types of neural networks was performed with one output and with two outputs. For each type, network training was conducted using three different algorithms of backpropagation methods. For each neural network, a comparison of the obtained experimental and simulation results was performed. More accurate prediction results were obtained by the single-output neural network for both particulate sizes, while the smallest error was found in the case of a trained neural network using the Levenberg-Marquardt backward propagation algorithm. The aim of creating such a prediction model is to prove that by using neural networks it is possible to predict the emission of particles generated by brake wear, which can be further used for modern traffic systems such as traffic control. In addition, this wear algorithm could be applied on other vehicle systems, such as a clutch or tires.

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