This document describes a study that designed and developed a microscopic artificial intelligence traffic model intended for civilian vehicle research. The model generates semi-autonomous vehicles that drivers can interact with in a virtual simulation. The goals are to justify the need for such a model, increase driver training and safety. Key aspects of the model include algorithms for linear and radial vehicle motion, collision detection, traffic signals, stop signs, and lane changing. The conclusion discusses expanding the model with more realistic human behavior models and integrated traffic-driving-network simulation capabilities.
Presentation on advance traffic engineering.pptxEtahEneji1
This presentation was done to fulfil the course requirement for the pursuit of my M. ENG on the course title: Advanced traffic engineering Course code : (CIV 8331).
Course Lecturer : ENGR. PROF H. M. AlHASSAN
Presentation on advance traffic engineering.pptxEtahEneji1
This presentation was done to fulfil the course requirement for the pursuit of my M. ENG on the course title: Advanced traffic engineering Course code : (CIV 8331).
Course Lecturer : ENGR. PROF H. M. AlHASSAN
Modeling business management systems transportationSherin El-Rashied
Introduction
How IT &Business Process Fit Together
What is modeling?
What is Simulation?
Modeling & Simulation in Business Process Management
The Seven-Step Model-Building Process
Transportation
An overview on transportation modeling
Transport model scope & structure
Car Traffic Jam Problem
Aim of Transportation Model
Types of Traffic Models
Microscopic Traffic model & Simulation
Cellular Automaton model
Conclusion
Solving Transportation Problem by Software Application
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The TTI Center for Transportation Safety is home to a Realtime Technologies, Inc. (RTI) driving simulator that provides measurements of drivers’ responses to roadway situations, in-vehicle technologies, and driving-related tasks. RTI’s
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In India, traffic control management is a difficult task due to an increment in the number of vehicles for the same infrastructure and systems. In the smart-city project, the Adaptive Traffic Light Control System (ATLCS) is one of the major research concerns for an Intelligent Transportation System (ITS) development to reduce traffic congestion and accidents, create a healthy environment, etc. Here, we have proposed a Vehicular Density Value (VDV) based adaptive traffic light control system method for 4-way intersection points using a selection of rotation, area of interest, and Statistical Block Matching Approach (SBMA). Graphical User Interface (GUI) and Hardware-based results are shown in the result section. We have compared, the normal traffic light control system with the proposed adaptive traffic light control system in the results section. The same results are verified using a hardware (raspberry-pi) device with different sizes, colors, and shapes of vehicles using the same method.
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The suggested method helps predicting vehicles movement in order to give the driver more time to react and avoid collisions on roads. The algorithm is dynamically modelling the road scene around the vehicle based on the data from the onboard camera. All moving objects are monitored and represented by the dynamic model on a 2D map. After analyzing every object’s movement, the algorithm predicts its possible behavior.
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Statistics indicate that most road accidents occur due to a lack of time to react to instant traffic. This problem can be addressed with self-driving vehicles with the application of automated systems to detect such traffic events. The Autonomous Vehicle Navigation System (ATS) has been a standard in the Intelligent Transport System (ITS) and many Driver Assistance Systems (DAS) have been adopted to support these Advanced Autonomous Vehicles (IAVs). To develop these recognition systems for automated self-driving cars, it's important to monitor and operate in real-time traffic events. It requires the correct detection and response of traffic event an automated vehicle. In this paper proposed to develop such a system by applying image recognition to detect and respond to a road blocker by means of real-time distance measurement. To study the performance by measuring accuracy and precision of road blocker detection system and distance calculation, various experiments were conducted by using Shalom frame dataset and detection accuracy, precision of 99%, 100%, while distance calculation 97%, 99% has been achieved by this approach.
The International Journal of Management Research and Business Strategy is a international journal in English published every day in our life. It offers a fast publication schedule of maintaining rigorous peer review..The use of recommended electronic formats for article delivery the process and submitted research review articles and Case Studies are subjected to immediate screening by the editors.
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Introduction
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About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
3. INTRODUCTION
Roadway safety is a major concern all over the world, Statistics
show that over a million fatalities occur annually due to motor
vehicle accidents globally.
This research describes the design & development of a
microscopic artificially intelligent traffic model that is intended
for civilian ground vehicle research applications.
This Model generates a fleet of semi-intelligent vehicles
whereby a driver interacts within a virtual driving simulation
environment
4. AIMS &OBJECTIVES
This study aims to justify the need for the design and development of a
microscopic artificial intelligent traffic model intended for civilian ground
vehicle research applications.
To increase the training and skills of the driver and the road safety as well
5. LITERATURE REVIEW
The microscopic traffic model described in this study helps broaden the range of
applications for which stand alone driving/traffic simulators are applicable.
The integration of this technology can be used for other applications which are
relevant to transportation safety and public health such as:
Clinical & human factors studies in vehicle simulation
Analysis of “green” studies in transportation science
Future transportation planning
Military Applications i.e autonomous warfare
6. ARTIFICIAL INTELLIGENT TRAFFIC MODEL
DESIGN
LINEAR & RADIAL MOTION : To allow AI vehicles navigate within the virtual
environment, a function was designed to dictate their basic motion path.
LINEAR MOTION EQUATION: RADIAL MOTION EQUATION:
v = s/t 𝜔 = 𝜃/𝑡
v = u + at 𝜔 = 𝑤0 + 𝛼𝑡
s = ut + 1/2𝑡2 𝜃 = 𝑤0 t +
1
2
𝛼𝑡2
𝑣2 = 𝑢2 + 2as 𝛼 =
𝑑𝑤
𝑑𝑡
=
𝑑2𝜃
𝑑𝑡2
7. Where as
v = velocity (ft/s) 𝜔 = angular velocity (rad/s)
s = linear displacement (ft) 𝜃 = angular displacement (rad)
t = time (s) t = time (s)
u = initial linear velocity(ft/s) 𝑤0 = angular velocity at time zero (rad/s)
a = acceleration (ft/𝑠2
) 𝛼 = angular acceleration (rad/𝑠2
)
Collision Detection : The key to optimize these calculations is to efficiently discard non-colliding
objects before applying a full collision test . The algorithm was designed to accommodate the motion of
AI vehicles during all three phases of it’s motion:
Acceleration/Coasting : During this phase, braking is required to slow down or stop the vehicle if
required, after this collision has been detected the vehicles move outside of the scope of the collision
box of the AI vehicle
8. Deceleration : In this case the AI vehicle is already slowing down to stop , if the AI vehicle detects collision and comes
to a complete stop , the algorithm resumes the original motion of the AI vehicle when the colliding vehicle is outside of
scope
Turning : During this phase, the size of collision box is reduced in order to avoid false collision detection with vehicles in
adjacent lanes.
TRAFFIC SIGNAL MODEL : This algorithm handles all decisions to be made depending on the current status (i.e, red(R)
yellow(Y), green(G) of each intersection. There are various scenarios considered in order to make the traffic signal
algorithm decisions similar to authentic environment traffic signal scenarios:
If the traffic signal state is green when the AI vehicle enters deceleration, the motion algorithm equates the deceleration
of the AI vehicle to zero, which then continues traversing the intersection without slowing down
If the signal state is yellow or red when the AI vehicle enters deceleration, the motion algorithm to recalculate the
deceleration of the AI vehicle required to stop the vehicle at the intersection, or to continue the motion of the AI vehicle
if it is too near (or inside) the intersection
Another scenario considered was if the AI vehicle enters the deceleration phase, the traffic signal status is red, and the
AI vehicle decides to slow down and stop. While it is slowing down, the traffic signal status suddenly turns green; the
traffic signal algorithm is called again and it instructs the motion algorithm to stop decelerating, and the AI vehicle
continues traversing the intersection without stopping.
9. STOP SIGN MODEL :
The scope of the traffic signal algorithm was expanded to include all traffic signals within the
current virtual environment and its logic was extended for application at intersections governed
by stop signs. The algorithm was designed to account for “all-way” stop signs and “single-way”
stop signs.
LANE-CHANGING MODEL :
Lateral movement (e.g., lane changing) by AI vehicles is necessary to represent real environment
traffic scenarios. For example, the AI vehicles in the left-most lane will take a left turn at an
approaching intersection, vehicles in middle lanes will continue straight, and the vehicles in the
right-most lane will turn right (or go straight). These decisions were made using stochastic effects,
effects, as each AI vehicle approaches each given intersection. For an AI vehicle to change lanes,
the heading angle no longer conforms to the (global) heading angle with the street upon which
the vehicle is traveling
10. CONCLUSION
Roadway safety and sustainability continue to be major public health concerns, and subsequently,
simulators (and other M&S technologies) continue to become more abundant in a wide variety of
Intelligent Transportation Systems (ITS) research applications (e.g., autonomous driving, human factors,
and rehabilitation).
Furthermore, in a majority of driving simulators, accompanying traffic is pre-programmed and does
not react according to the real time actions of the human subject operating the simulation.
Related research has noted that existing microscopic traffic simulation models (i.e., those based on
available car-following, gap-acceptance, and lane-changing models) often lack the level-of-detail
required for safety evaluations, which demand models that more accurately reflect errors in drivers’
perception, decision-making, and actions. Largely for these reasons, an Artificially Intelligent Traffic
Model (AITM) was constructed to operate in conjunction with a custom-designed driving simulation
environment.
11. FUTURE WORK
While vehicle and transportation network technologies continue to evolve, there is an increasing
urgency for improved fidelity for driving/traffic simulation research. As such, the research described
here can be expanded and improved in numerous ways, and to conclude the paper, a few detailed
suggestions are offered here
Implement customized human behavior models to enhance the traffic mobility
Provide a high-fidelity, multiple-participant capability to facilitate research that involves real-time
interaction between human participants.
Integrated Traffic-Driving-Network Simulation
12. REFERENCES
Boxill, S.A., and Yu, L., (2000). “An Evaluation of Traffic Simulation Models for Supporting ITS
Development”, Center for Transportation Training and Research, Report 167602-1, October 2000
Centers for Disease Control and Prevention (CDC), (2012). “Web-based Injury Statistics Query and
Reporting System (WISQARS)”, online web link, http://www.cdc.gov/injury/wisqars/, National Center for
Injury Prevention and Control, Centers for Disease Control and Prevention.
Department of Transportation (DOT), (2011). “An Approach to Communications Security for a
Communications Data Delivery System for V2V/V2I Safety: Technical Description and Identification of
Policy and Institutional Issues”, White Paper, FHWA-JPO-11-130, November, 2011.
Hulme, K.F., Huang, S., Sadek, A.W., and Qiao, C., (2010). “Next Generation, Integrated Hardware-in-the-
loop Transportation Simulation Modeling.” The Interservice/Industry Training, Simulation and Education
Conference, Orlando, FL.