Incorporating Autonomous Vehicles in the Traditional Four-Step Model

2020 ◽  
Vol 2674 (7) ◽  
pp. 348-360 ◽  
Author(s):  
Felipe F. Dias ◽  
Gopindra S. Nair ◽  
Natalia Ruíz-Juri ◽  
Chandra R. Bhat ◽  
Arash Mirzaei
Keyword(s):  
Autonomous Vehicles ◽  
Network Performance ◽  
Network Capacity ◽  
Modeling Tools ◽  
Passenger Car ◽  
Step Model ◽  
Central Texas ◽  
Passenger Car Equivalent ◽  
Travel Decisions ◽  
Near Future

Automated vehicles (AVs) are a concrete possibility in the near future. As AVs may shift transportation paradigms, transportation agencies are eager to update their models to consider them in planning. In this context, the use of advanced models may be challenging, given the uncertainty in the use and deployment of AVs. In this paper, we present a general framework to extend the four-step model to include AVs, and test our extension on North Central Texas Council of Governments’ model. Our approach introduces a module for AV ownership and exogenous parameters into an existing four-step model to account for changes in travel decisions for AV owners, and for the impacts of AVs on network performance. The latter is modeled using the concept of passenger-car-equivalent to avoid imposing network-wide assumptions on AVs’ capacity consumption. We analyze five scenarios, representing different assumptions on the impacts of AVs, and include references to inform the selection of modeling parameters. We compute aggregate metrics that suggest that the model is sensitive to the proposed parameters, with the passenger-car-equivalent assumptions having the largest impact on model outcomes. Results suggest that, even when we assume that AVs can better use network capacity and that trip-making rates do not drastically increase, AVs may lead to an increase of about 2.8% in vehicle-hours traveled while also improving speeds by about 1.8%. If AVs introduce additional friction on traffic, the system performance may deteriorate. The analyses presented here suggest that existing modeling tools may be adjusted to support analyses of a future with AVs.

scholarly journals A Review of Car-Following Models and Modeling Tools for Human and Autonomous-Ready Driving Behaviors in Micro-Simulation

Smart Cities ◽  
2021 ◽  
Vol 4 (1) ◽  
pp. 314-335
Author(s):  
Hafiz Usman Ahmed ◽  
Ying Huang ◽  
Pan Lu
Keyword(s):  
Autonomous Vehicles ◽  
Adaptive Cruise Control ◽  
Cruise Control ◽  
Modeling Tools ◽  
Microscopic Traffic Simulation ◽  
Simulation Tools ◽  
Car Following ◽  
Driving Behaviors ◽  
Micro Simulation ◽  
Models And Modeling

The platform of a microscopic traffic simulation provides an opportunity to study the driving behavior of vehicles on a roadway system. Compared to traditional conventional cars with human drivers, the car-following behaviors of autonomous vehicles (AVs) and connected autonomous vehicles (CAVs) would be quite different and hence require additional modeling efforts. This paper presents a thorough review of the literature on the car-following models used in prevalent micro-simulation tools for vehicles with both human and robot drivers. Specifically, the car-following logics such as the Wiedemann model and adaptive cruise control technology were reviewed based on the vehicle’s dynamic behavior and driving environments. In addition, some of the more recent “AV-ready (autonomous vehicles ready) tools” in micro-simulation platforms are also discussed in this paper.

scholarly journals LVD-NMPC: A learning-based vision dynamics approach to nonlinear model predictive control for autonomous vehicles

2021 ◽  
Vol 18 (3) ◽  
pp. 172988142110195
Author(s):  
Sorin Grigorescu ◽  
Cosmin Ginerica ◽  
Mihai Zaha ◽  
Gigel Macesanu ◽  
Bogdan Trasnea
Keyword(s):  
Neural Network ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Autonomous Vehicles ◽  
Process Model ◽  
Network Performance ◽  
Vision System ◽  
Nonlinear Model Predictive Control ◽  
Scaled Model

In this article, we introduce a learning-based vision dynamics approach to nonlinear model predictive control (NMPC) for autonomous vehicles, coined learning-based vision dynamics (LVD) NMPC. LVD-NMPC uses an a-priori process model and a learned vision dynamics model used to calculate the dynamics of the driving scene, the controlled system’s desired state trajectory, and the weighting gains of the quadratic cost function optimized by a constrained predictive controller. The vision system is defined as a deep neural network designed to estimate the dynamics of the image scene. The input is based on historic sequences of sensory observations and vehicle states, integrated by an augmented memory component. Deep Q-learning is used to train the deep network, which once trained can also be used to calculate the desired trajectory of the vehicle. We evaluate LVD-NMPC against a baseline dynamic window approach (DWA) path planning executed using standard NMPC and against the PilotNet neural network. Performance is measured in our simulation environment GridSim, on a real-world 1:8 scaled model car as well as on a real size autonomous test vehicle and the nuScenes computer vision dataset.

scholarly journals Advanced Driver Assistant Systems Focused on Pedestrians’ Safety: A User Experience Approach

2021 ◽  
Vol 13 (8) ◽  
pp. 4264
Author(s):  
Matúš Šucha ◽  
Ralf Risser ◽  
Kristýna Honzíčková
Keyword(s):  
Autonomous Vehicles ◽  
Qualitative Data ◽  
Expert Interviews ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Assistant Systems ◽  
Vehicle Technology ◽  
Assistance Systems ◽  
Technical Solutions ◽  
Near Future

Globally, pedestrians represent 23% of all road deaths. Many solutions to protect pedestrians are proposed; in this paper, we focus on technical solutions of the ADAS–Advanced Driver Assistance Systems–type. Concerning the interaction between drivers and pedestrians, we want to have a closer look at two aspects: how to protect pedestrians with the help of vehicle technology, and how pedestrians–but also car drivers–perceive and accept such technology. The aim of the present study was to analyze and describe the experiences, needs, and preferences of pedestrians–and drivers–in connection with ADAS, or in other words, how ADAS should work in such a way that it would protect pedestrians and make walking more relaxed. Moreover, we interviewed experts in the field in order to check if, in the near future, the needs and preferences of pedestrians and drivers can be met by new generations of ADAS. A combination of different methods, specifically, an original questionnaire, on-the-spot interviewing, and expert interviews, was used to collect data. The qualitative data was analyzed using qualitative text analysis (clustering and categorization). The questionnaire for drivers was answered by a total of 70 respondents, while a total of 60 pedestrians agreed to complete questionnaires concerning pedestrian safety. Expert interviews (five interviews) were conducted by means of personal interviews, approximately one hour in duration. We conclude that systems to protect pedestrians–to avoid collisions of cars with pedestrians–are considered useful by all groups, though with somewhat different implications. With respect to the features of such systems, the considerations are very heterogeneous, and experimentation is needed in order to develop optimal systems, but a decisive argument put forward by some of the experts is that autonomous vehicles will have to be programmed extremely defensively. Given this argument, we conclude that we will need more discussion concerning typical interaction situations in order to find solutions that allow traffic to work both smoothly and safely.

scholarly journals Cognitive Interference Alignment Schemes for IoT Oriented Heterogeneous Two-Tier Networks

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2548 ◽  
Author(s):  
Run Tian ◽  
Lin Ma ◽  
Zhe Wang ◽  
Xuezhi Tan
Keyword(s):  
Spectrum Sharing ◽  
Network Performance ◽  
Signal To Noise Ratio ◽  
Interference Management ◽  
Interference Alignment ◽  
Network Capacity ◽  
High Signal ◽  
Noise Power ◽  
Optimal Capacity ◽  
Iot Devices

This paper considers interference management and capacity improvement for Internet of Things (IoT) oriented two-tier networks by exploiting cognition between network tiers with interference alignment (IA). More specifically, we target our efforts on the next generation two-tier networks, where a tier of femtocell serving multiple IoT devices shares the licensed spectrum with a tier of pre-existing macrocell via a cognitive radio. Aiming to manage the cross-tier interference caused by cognitive spectrum sharing as well as ensure an optimal capacity of the femtocell, two novel self-organizing cognitive IA schemes are proposed. First, we propose an interference nulling based cognitive IA scheme. In such a scheme, both co-tier and cross-tier interferences are aligned into the orthogonal subspace at each IoT receiver, which means all the interference can be perfectly eliminated without causing any performance degradation on the macrocell. However, it is known that the interference nulling based IA algorithm achieves its optimum only in high signal to noise ratio (SNR) scenarios, where the noise power is negligible. Consequently, when the imposed interference-free constraint on the femtocell can be relaxed, we also present a partial cognitive IA scheme that further enhances the network performance under a low and intermediate SNR. Additionally, the feasibility conditions and capacity analyses of the proposed schemes are provided. Both theoretical and numerical results demonstrate that the proposed cognitive IA schemes outperform the traditional orthogonal precoding methods in terms of network capacity, while preserving for macrocell users the desired quality of service.

scholarly journals Passenger Car Equivalent Estimation for Rural Highways: Methodological Review

2020 ◽  
Vol 48 ◽  
pp. 801-816
Author(s):  
Ballari Syed Omar ◽  
Pranab Kar ◽  
Mallikarjuna Chunchu
Keyword(s):  
Passenger Car ◽  
Methodological Review ◽  
Rural Highways ◽  
Passenger Car Equivalent

Evaluation of the Traffic Impacts of Mass Evacuation of Halifax: Flood Risk and Dynamic Traffic Microsimulation Modeling

2018 ◽  
Vol 2672 (1) ◽  
pp. 148-160 ◽  
Author(s):  
MD Jahedul Alam ◽  
Muhammad Ahsanul Habib ◽  
Kevin Quigley ◽  
Tim L. Webster
Keyword(s):  
Flood Risk ◽  
Network Performance ◽  
Comprehensive Evaluation ◽  
Dynamic Traffic Assignment ◽  
Network Capacity ◽  
Transport Network ◽  
Dynamic Traffic ◽  
Traffic Impacts ◽  
Microsimulation Modeling ◽  
Mass Evacuation

This paper presents a comprehensive evaluation of traffic impacts of a mass evacuation of the Halifax Peninsula under several flooding scenarios. Flood extent and associated damages to the transport network are identified through digital elevation modeling that intersects with the Halifax stream and transport networks. The resulting flood scenarios inform a traffic microsimulation model that uses a dynamic traffic assignment-based microsimulation approach and simulates the evacuation of 34,808 evacuees estimated from the Halifax Regional Transport Network Model. The simulation results suggest that flooding of the links by 7.9 m flood reduces alternative evacuation routes by 31.2%. It takes 15 hours to evacuate 83% of evacuees while the remaining 17% are not accommodated in the network due to reduced network capacity. The number of vehicles in the network has peaked at 13,000 in this flooding scenario. An evaluation of network performance reveals a sustained congestion prevailing from 4th to 7th hour of the evacuation. The novelty of this study is that it develops a comprehensive tool of flood risk and dynamic traffic microsimulation modeling to offer an in-depth evaluation of potential impacts during evacuation. The results will help emergency professionals in evacuation planning and making emergency decisions.

Determining Passenger Car Equivalent for Motorcycle at Mid-Block of Sesetan Road

2015 ◽  
Vol 776 ◽  
pp. 95-100
Author(s):  
I. Gusti Raka Purbanto
Keyword(s):  
Motor Vehicles ◽  
Heavy Vehicles ◽  
Highway Capacity ◽  
Passenger Car ◽  
Highway Capacity Manual ◽  
The Road ◽  
Traffic Conditions ◽  
Mode Share ◽  
Passenger Car Equivalent ◽  
On The Road

Motorcycle dominates traffic in Bali, particularly in urban roads, which occupy more than 85% of mode share. The three types of vehicles, i.e. motorcycles, heavy and light vehicles share the roadways together. Under mixed traffic conditions, motorcycle may be travelling in between and alongside two consecutive motor vehicles. Considering such a situation, passenger car equivalent values should be examined thoroughly. This study aims to determine passenger car equivalent (PCEs) of motorcycle at mid-block of Sesetan Road. Three approaches are used to examine the PCEs values. This study found that the PCE of motorcycles are in a range between 0.2 and 0.4. This values are about the same to the existing PCE of the Indonesian Highway Capacity Manual (1997). This study also pointed out that motorcyclists and car drivers may behave differently to the existence of motorcycles. Car drivers are more aware than motorcyclists on the existence of motorcycle on the road. Further, more samples are required to obtain comprehensive results. In addition, the presence of heavy vehicles need to be considered for future study.

scholarly journals Will the Thai ABC Car Owners, Buy a Smart ABC Car? a Case Study in Bangkok Metropolis

2018 ◽  
Vol 7 (3.30) ◽  
pp. 202
Author(s):  
Keerati Sittichainarong ◽  
Aaron Loh ◽  
Preecha Methavasaraphak ◽  
John Barnes
Keyword(s):  
Automobile Industry ◽  
Autonomous Vehicles ◽  
New Technology ◽  
Influential Factors ◽  
Self Identity ◽  
Factors Affecting ◽  
Intent To Purchase ◽  
Intention To Purchase ◽  
Near Future

Thailand is the biggest manufacturer of trucks and cars outside of Japan and China in Asia. Many had reported that "smart" technology especially that which leads towards driverless or autonomous vehicles will be the most important single development that will affect the automobile industry both domestically and globally.  Hence this research is therefore on the readiness of Thai car owners to adopt the new technology and the intention to purchase a smart car in the near future. Specifically, it is a case study on the influential factors affecting the intent to purchase a smart car by owners of a top Japanese brand in Bangkok. A questionnaire survey was conducted on 385 existing car owners of the Japanese brand under consideration in metropolitan areas of Bangkok and the data returned analyzed by multiple linear regression. The outcome of the research pointed towards ‘Self-identity” and ‘Emotional connection’ as the most influential factors towards the intent to purchase a smart car.  

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