scholarly journals Soluciones de conducción para vehículos autónomos, y propuestas adaptativas para establecer los tiempos de semáforos y elegir rutas

2019 ◽  
Vol 20 (2) ◽  
Author(s):  
José Gerardo Carrillo González
Keyword(s):  
Travel Time ◽  
Traffic Flow ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Traffic Lights ◽  
Linear Path ◽  
Time Cycle ◽  
Adaptive Solutions ◽  
The Times ◽  
The Right

Two objectives are pursued in this article: 1) with adaptive solutions, improve the traffic flow by setting the time cycle of traffic lights at intersections and reduce the travel time by selecting the vehicles route (treated as separated problems). 2) Avoid driving conflicts among autonomous vehicles (which have defined trajectories) and these with a non-autonomous vehicle (which follows a free path). The traffic lights times are set with formulas that continuously recalculate the times values according the number of vehicles on the intersecting streets. For selecting the vehicles route an algorithm was developed, this calculates different routes (connected streets that conform a solution from the origin to the destination) and selects a route with low density. The results of the article indicate that the adaptive solutions to set the traffic lights times and to select the vehicles path, present a greater traffic flow and a shorter travel time, respectively, than conventional solutions. To avoid collisions among autonomous vehicles which follow a linear path, an algorithm was developed, this was successfully tested in different scenarios through simulations, besides the algorithm allows the interaction of a vehicle manually controlled (circulating without restrictions) with the autonomous vehicles. The algorithm regulates the autonomous vehicles acceleration (deceleration) and assigns the right of way among these and with the human controlled vehicle.

scholarly journals Exploring the Distribution of Traffic Flow for Shared Human and Autonomous Vehicle Roads

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3425
Author(s):  
Huanping Li ◽  
Jian Wang ◽  
Guopeng Bai ◽  
Xiaowei Hu
Keyword(s):  
Traffic Flow ◽  
Autonomous Vehicles ◽  
Flow Model ◽  
Road Traffic ◽  
Autonomous Vehicle ◽  
Transformation Model ◽  
Hydrodynamic Theory ◽  
Traffic Flow Model ◽  
Traffic System ◽  
The Stability

In order to explore the changes that autonomous vehicles would bring to the current traffic system, we analyze the car-following behavior of different traffic scenarios based on an anti-collision theory and establish a traffic flow model with an arbitrary proportion (p) of autonomous vehicles. Using calculus and difference methods, a speed transformation model is established which could make the autonomous/human-driven vehicles maintain synchronized speed changes. Based on multi-hydrodynamic theory, a mixed traffic flow model capable of numerical calculation is established to predict the changes in traffic flow under different proportions of autonomous vehicles, then obtain the redistribution characteristics of traffic flow. Results show that the reaction time of autonomous vehicles has a decisive influence on traffic capacity; the q-k curve for mixed human/autonomous traffic remains in the region between the q-k curves for 100% human and 100% autonomous traffic; the participation of autonomous vehicles won’t bring essential changes to road traffic parameters; the speed-following transformation model minimizes the safety distance and provides a reference for the bottom program design of autonomous vehicles. In general, the research could not only optimize the stability of transportation system operation but also save road resources.

A STUDY OF CITY TRAFFIC IN AFTERNOON RUSH HOURS

2013 ◽  
Vol 24 (06) ◽  
pp. 1350040 ◽  
Author(s):  
EDGAR ÁVALOS ◽  
D. W. HUANG ◽  
W. N. HUANG
Keyword(s):  
Cellular Automaton ◽  
Travel Time ◽  
Traffic Flow ◽  
Traffic Lights ◽  
Suburban Areas

The traffic of vehicles from downtown to suburban areas is investigated numerically. We propose a cellular automaton to simulate the traffic of vehicles within a city regulated by traffic lights. Both traffic flow and travel time are presented and we discuss some strategies to optimize these quantities.

scholarly journals PATTERNS OF URBAN TRANSPORT FLOWS GENERATION

2020 ◽  
Vol 1 (154) ◽  
pp. 248-252 ◽  
Author(s):  
I. Chumachenko ◽  
A. Galkin ◽  
N. Davidich ◽  
Y. Kush ◽  
I. Litomin
Keyword(s):  
Nervous System ◽  
Travel Time ◽  
Traffic Flow ◽  
Individual Characteristics ◽  
Urban Transport ◽  
Traffic Intensity ◽  
Important Criterion ◽  
Traffic Lights ◽  
The Road ◽  
Expert Opinions

The article is devoted to explaining the issue of exploring the patterns of formation of urban traffic flows in case of the development of urban transport systems projects. Existing methods for predicting traffic flow parameters are developed for all drivers of vehicles, regardless of their individual characteristics, and contain only travel time as a parameter. It is proposed to use the route run, travel time, traffic intensity as the possible criteria, the route runs along the main roads, the condition of the road surface, the number of traffic lights on the route, and fatigue when driving. Based on the results of a questionnaire survey of drivers of individual vehicles, the significance of the criteria for choosing a route of movement for drivers with various types of nervous systems is assessed. The most significant criterion was set up when choosing a route for travel is the “condition of the road surface”. The second most important criterion is “run along the route”. The third criterion was “travel time”. The criterion “traffic intensity” has become even less significant for drivers. The next most important criterion was “the route take place over the main roads”. Even less significant was the criterion “quantity of traffic lights on the route”. The criterion “fatigue during movement” became the least significant. To assess the consistency of expert opinions, a concordance coefficient was used. The values of the concordance coefficient showed that there is a consistency of expert opinions both for the total population of drivers and for their groups, divided on the basis of “temperament”. It was found that when choosing a travel route, drivers are guided by numerous criteria. Moreover, the advantage or disadvantage of one or another criterion depends on its individual characteristics, which are determined by the properties of the central nervous system. Keywords: driver, route, traffic flow, vehicle, questionnaire, nervous system, criterion, significance.

scholarly journals Automated detecting and placing road objects from street-level images

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Chaoquan Zhang ◽  
Hongchao Fan ◽  
Wanzhi Li
Keyword(s):  
Autonomous Vehicles ◽  
Image Sequence ◽  
Positional Accuracy ◽  
Traffic Lights ◽  
The Road ◽  
Processing Pipeline ◽  
Street Level ◽  
Road Intersections ◽  
The Right

AbstractNavigation services utilized by autonomous vehicles or ordinary users require the availability of detailed information about road-related objects and their geolocations, especially at road intersections. However, these road intersections are generally represented as point elements without detailed information, or are even not available in current versions of crowdsourced mapping databases including OpenStreetMap (OSM). This study proposes an approach to automatically detect road objects from street-level images and place them to correct locations according to urban rules. Our processing pipeline relies on two convolutional neural networks: the first one segments the images, while the second one detects and classifies the specific objects. Moreover, to locate the detected objects, we propose an attributed topological binary tree (ATBT) based on urban rules for each image in an image sequence to depict the coherent relations of topologies, attributes and semantics of the road objects. Then the ATBT is further matched with map features on OSM to determine the right placed location. The proposed method has been applied to a case study in Berlin, Germany. We validate the effectiveness of the proposed method on two object classes: traffic signs and traffic lights. Experimental results demonstrate that the proposed app roach provides promising results in terms of completeness and positional accuracy.

scholarly journals Development and Application of Dynamic Timing Optimization Platform for Big Data Intelligent Traffic Signals

2019 ◽  
Vol 136 ◽  
pp. 01008
Author(s):  
Zhao Wang ◽  
Mengjie Wang ◽  
Wenqiang Bao
Keyword(s):  
Big Data ◽  
Travel Time ◽  
Traffic Flow ◽  
Delay Time ◽  
Traffic Signals ◽  
Traffic Signal ◽  
Total Delay ◽  
Timing Optimization ◽  
Traffic Lights ◽  
Signal Dynamic

As the number of car ownership increases, road traffic flow continues to increase. At the same time, traffic pressure at intersections is increasing as well. At present, most of the traffic lights in China are fixed cycle control. This timing control algorithm obviously cannot make timely adjustments according to changes in traffic flow. In this case, a large number of transportation resources would be wasted. It is very necessary to establish a dynamic timing system for Big data intelligent traffic signals. In this research, the video recognition method was used to acquire the number of vehicles at the intersection in real time, and the obtained data was processed by the optimization algorithm to make a reasonable dynamic timing of the traffic signals. The test results show that after using the big data intelligent traffic signal dynamic timing optimization control platform, in the experimental area, the overall total delay time was reduced by 23%, and the travel time was reduced by 15%. During the off-peak period, the overall total delay time in the experimental region was reduced by 17% and travel time was reduced by 10%. The big data intelligent traffic signal dynamic timing optimization platform would improve the operational efficiency and traffic supply capacity of the existing transportation infrastructure, and could provide real convenience for citizens.

scholarly journals Simulation Study of Autonomous Vehicles’ Effect on Traffic Flow Characteristics including Autonomous Buses

2020 ◽  
Vol 2020 ◽  
pp. 1-17 ◽  
Author(s):  
Tanveer Muhammad ◽  
Faizan Ahmad Kashmiri ◽  
Hassan Naeem ◽  
Xin Qi ◽  
Hsu Chia-Chun ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Flow Rate ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Flow Characteristics ◽  
Transportation Industry ◽  
Lane Changing ◽  
Autonomous Cars ◽  
Ca Model ◽  
Multiple Scenarios

Autonomous vehicles are expected to revolutionize the transportation industry. The goal of this research is to study the heterogeneity in traffic flow dynamics by comparing different penetration rates of four different types of vehicles: autonomous cars (AC), autonomous buses (AB), manual cars (MC), and manual buses (MB). For the purpose of this research, a modified cellular automata (CA) model is developed in order to analyze the effect of heterogeneous vehicles (manual and autonomous). Previously, studies have focused on manual and autonomous cars, but we believe a gap in perception and analysis of mixed traffic still exists, as inclusion of other modes of autonomous vehicle research is very limited. Therefore, we have explicitly examined the effect of the AB on overall traffic flow. Moreover, two types of lane changing behavior (aggressive lane changing and polite lane changing) were also integrated into the model. Multiple scenarios through different compositions of vehicles were simulated. As per the results, if AB is employed concurrently with AC, there will be a significant improvement in traffic flow and road capacity, as equally more passengers can be accommodated in AB as AC is also anticipated to be used in carpooling. Secondly, when the vehicles change the lanes aggressively, there is a substantial growth in the flow rate and capacity of the network. Polite lane change does not significantly affect the flow rate.

scholarly journals Real Time Traffic Light Detection by Autonomous Vehicles using Artificial Neural Network Techniques

2019 ◽  
Vol 8 (10) ◽  
pp. 2129-2133
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Autonomous Vehicles ◽  
Traffic Light ◽  
Artificial Intelligence Technique ◽  
Light Detection ◽  
Traffic Lights ◽  
Real Time Traffic ◽  
The Right ◽  
Sufficient Precision

Autonomous vehicles are the reality of the future, they will open up the way for future advanced systems where computers are expected to take over the decision making of driving. These automobiles are capable of sensing their environment and moving with little or no human input. The main goal of this research is to detect traffic light in real-time for autonomous vehicles. Apart from taking decisions to navigate in the right manner the autonomous vehicles important task is to detect traffic lights, so that it can obey the traffic rules with sufficient precision. The work carried out in this research makes use of two Artificial Intelligence technique, these techniques are compared in accomplishing the task of traffic light detection in real time. The two models that are designed and implemented are Convolution neural network (CNN) and Deep Convolution Inverse Graphics Network (DCIGN). The results clearly show that DCIGN out performance CNN by 8%.

Exploring the Potential of Multiagent Learning for Autonomous Intersection Control

2009 ◽  
pp. 280-290 ◽  
Author(s):  
Matteo Vasirani ◽  
Sascha Ossowski
Keyword(s):  
Travel Time ◽  
Traffic Flow ◽  
Autonomous Vehicles ◽  
Coordination Mechanism ◽  
Multiagent Learning ◽  
Average Travel Time ◽  
Promising Application ◽  
Flow Through ◽  
Near Future ◽  
Intersection Control

The problem of advanced intersection control is being discovered as a promising application field for multiagent technology. In this context, drivers interact autonomously with a coordination facility that controls the traffic flow through an intersection, with the aim of avoiding collisions and minimizing delays. This is particularly interesting in the case of autonomous vehicles that are controlled entirely by agents, a scenario that will become possible in the near future. In this chapter, the authors seize the opportunities of multiagent learning offered by such a scenario, by introducing a coordination mechanism where teams of agents coordinate their velocities when approaching the intersection in a decentralized way. They show that this approach enables the agents to improve the intersection efficiency, by reducing the average travel time and so contributing to alleviate traffic congestions.

scholarly journals Maneuver-Based Objectification of User Comfort Affecting Aspects of Driving Style of Autonomous Vehicle Concepts

2020 ◽  
Vol 10 (11) ◽  
pp. 3946 ◽  
Author(s):  
Ferdinand Schockenhoff ◽  
Hannes Nehse ◽  
Markus Lienkamp
Keyword(s):  
Pilot Study ◽  
Travel Time ◽  
Autonomous Vehicles ◽  
User Study ◽  
Autonomous Vehicle ◽  
User Needs ◽  
Vehicle Concept ◽  
Driving Style ◽  
The Individual ◽  
User Comfort

Driving maneuvers try to objectify user needs regarding the driving dynamics for a vehicle concept. As autonomous vehicles will not be driven by people, the driving style that merges the individual aspects of driving dynamics, like user comfort, will be part of the vehicle concept itself. New driving maneuvers are, therefore, necessary to objectify the driving style of autonomous vehicle concepts with all its interdependencies relating to the individual aspects. This paper presents a methodology to design such driving maneuvers and includes a pilot study and a user study. As an example, the methodology was applied to the parameters of user comfort and travel time. The driven maneuvers resulted in statistical equations to objectify the interdependencies of these two aspects. Finally, this paper provides an outlook for needed maneuvers in order to tackle the entire driving style with its multidimensional facets.

scholarly journals Behavior Identification and Prediction for a Probabilistic Risk Framework

2019 ◽  
Author(s):  
Jasprit S. Gill ◽  
Pierluigi Pisu ◽  
Venkat N. Krovi ◽  
Matthias J. Schmid
Keyword(s):  
Autonomous Vehicles ◽  
Adaptive Estimation ◽  
Autonomous Vehicle ◽  
Search Space ◽  
Complex Environments ◽  
Current State ◽  
Risk Framework ◽  
Detailed Simulation ◽  
The Right ◽  
State Of The Environment

Abstract Operation in a real world traffic requires the ability to plan motion in complex environments (multiple moving participants) from autonomous vehicles. Navigation through such environments necessitates the provision of the right search space for the trajectory or maneuver planners so that the safest motion for the ego vehicle can be identified. Analyzing risks based on the predicted trajectories of all traffic participants (given the current state of the environment and its participants) aids in the proper formulation of this search space. This study introduces a fresh taxonomy of safety and risk that an autonomous vehicle should be capable of handling. It formulates a reference system architecture for implementation as well as describes a novel way of identifying and predicting the behaviors of other traffic participants utilizing classic Multi Model Adaptive Estimation (MMAE). Detailed simulation results and a discussion about the associated tuning of the implemented model conclude this work.

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