scholarly journals High-Resolution Traffic Sensing with Probe Autonomous Vehicles: A Data-Driven Approach

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 464
Author(s):  
Wei Ma ◽  
Sean Qian
Keyword(s):  
High Resolution ◽  
Autonomous Vehicles ◽  
Penetration Rate ◽  
Data Driven ◽  
Traffic Information ◽  
Flow Density ◽  
Massive Data ◽  
Market Penetration ◽  
High Resolution Data ◽  
Data Driven Approach

Recent decades have witnessed the breakthrough of autonomous vehicles (AVs), and the sensing capabilities of AVs have been dramatically improved. Various sensors installed on AVs will be collecting massive data and perceiving the surrounding traffic continuously. In fact, a fleet of AVs can serve as floating (or probe) sensors, which can be utilized to infer traffic information while cruising around the roadway networks. Unlike conventional traffic sensing methods relying on fixed location sensors or moving sensors that acquire only the information of their carrying vehicle, this paper leverages data from AVs carrying sensors for not only the information of the AVs, but also the characteristics of the surrounding traffic. A high-resolution data-driven traffic sensing framework is proposed, which estimates the fundamental traffic state characteristics, namely, flow, density and speed in high spatio-temporal resolutions and of each lane on a general road, and it is developed under different levels of AV perception capabilities and for any AV market penetration rate. Experimental results show that the proposed method achieves high accuracy even with a low AV market penetration rate. This study would help policymakers and private sectors (e.g., Waymo) to understand the values of massive data collected by AVs in traffic operation and management.

scholarly journals GANs-based PIV resolution enhancement without the need of high-resolution input

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Alejandro Güemes ◽  
Carlos Sanmiguel Vila ◽  
Stefano Discetti
Keyword(s):  
High Resolution ◽  
Resolution Enhancement ◽  
Super Resolution ◽  
Turbulent Channel Flow ◽  
Data Driven ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Incomplete Projections ◽  
Data Driven Approach ◽  
High Resolution Images

A data-driven approach to reconstruct high-resolution flow fields is presented. The method is based on exploiting the recent advances of SRGANs (Super-Resolution Generative Adversarial Networks) to enhance the resolution of Particle Image Velocimetry (PIV). The proposed approach exploits the availability of incomplete projections on high-resolution fields using the same set of images processed by standard PIV. Such incomplete projection is made available by sparse particle-based measurements such as super-resolution particle tracking velocimetry. Consequently, in contrast to other works, the method does not need a dual set of low/high-resolution images, and can be applied directly on a single set of raw images for training and estimation. This data-enhanced particle approach is assessed employing two datasets generated from direct numerical simulations: a fluidic pinball and a turbulent channel flow. The results prove that this data-driven method is able to enhance the resolution of PIV measurements even in complex flows without the need of a separate high-resolution experiment for training.

scholarly journals A data-driven approach to characterize the impact of connected and autonomous vehicles on traffic flow

2020 ◽  
pp. 1-9
Author(s):  
Amir Bahador Parsa ◽  
Ramin Shabanpour ◽  
Abolfazl (Kouros) Mohammadian ◽  
Joshua Auld ◽  
Thomas Stephens
Keyword(s):  
Traffic Flow ◽  
Autonomous Vehicles ◽  
Data Driven ◽  
Data Driven Approach ◽  
The Impact

Diagnosing Power Module Degradation with High-Resolution, Data-Driven Methods

2021 ◽  
Author(s):  
Christoph H. van der Broeck ◽  
Timothy A. Polom ◽  
Rik W. De Doncker
Keyword(s):  
High Resolution ◽  
Data Driven ◽  
Power Module ◽  
High Resolution Data ◽  
Resolution Data

scholarly journals RSU LOCALIZATION MODEL AND SIMULATION OPTIMIZATION FOR VII NETWORK

Transport ◽  
2012 ◽  
Vol 26 (4) ◽  
pp. 394-402 ◽  
Author(s):  
Jian Sun ◽  
Yuwei Yang
Keyword(s):  
Penetration Rate ◽  
Traffic Monitoring ◽  
Simulation Software ◽  
Traffic Information ◽  
Market Penetration ◽  
Test Bed ◽  
Evaluation Measures ◽  
Localization Model ◽  
Component Object ◽  
The Impact

While facing the needs for Vehicle Infrastructure Integration (VII) applications in traffic management, the paper deals with the problem of locating Road Side Units (RSU) for VII deployment. After analyzing the difference between traditional problems of locating traffic information detector and the problem of RSU location, a significance ranking model for RSU localization and three kinds of Significance Degree (SD) computing strategies are put forward. A VII simulation environment for the purpose of RSU localization optimization within VISSIM microscopic traffic simulation software is established developing add-on functions using VISSIM's Component Object Model (COM). A VII test bed of the Olympic Park network in Beijing is taken as an example to evaluate the performance of RSU localization model. The results of simulation experiments indicate that the mixed SD strategy considering both speed and route monitoring is superior to the other two SD strategies. Then, the impact of RSU number and OBE market penetration rate on the evaluation measures of traffic monitoring are studied with reference to the proposed mixed SD strategy. In this case, the evaluation measures of optimized RSU configurations generated by the ranking algorithm are always better than those of random RSU configurations. In addition, the benefits of optimized RSU configurations increase along with RSU number and market penetration rate while the benefits of random RSU configurations are more fluctuant.

Impact of Autonomous-Vehicle-Only Lanes in Mixed Traffic Conditions

2019 ◽  
Vol 2673 (9) ◽  
pp. 430-439 ◽  
Author(s):  
Hwapyeong Yu ◽  
Sehyun Tak ◽  
Minju Park ◽  
Hwasoo Yeo
Keyword(s):  
Autonomous Vehicles ◽  
Road Traffic ◽  
Positive Impact ◽  
Penetration Rate ◽  
Autonomous Vehicle ◽  
Market Penetration ◽  
Mixed Traffic ◽  
Negative Effects ◽  
Average Speed ◽  
Time To Collision

The introduction of autonomous vehicles (AVs) in the near future will have a significant impact on road traffic. AVs may have advantages in efficiency and convenience, but safety can be compromised in mixed operations of manual vehicles and AVs. To deal with the issues associated with mixed traffic and to avoid its negative effects, a special purpose lane reserved for AVs can be proposed to segregate AVs from manual vehicles. In this research, we analyze the effect on efficiency and safety of AVs in mixed traffic and in a situation where an AV-only lane is deployed. In the analysis, we investigate the average speed, the throughput, and the inverse time-to-collision (ITTC). We differentiate the behaviors of manual vehicles and AVs through the reaction time, desired speed, and car-following models. As a result, we observe that the efficiency is improved when the market penetration rate of AVs increases, especially when the highway throughput increases by up to 84% in the case of mixed traffic. However, safety worsens when the market penetration of AVs is under 40%. In this case, the average speed can be improved and the frequency of dangerous situations (ITTC > 0.49) can be reduced drastically in the merging section by making the innermost lane AV-only. Accordingly, we conclude that AV-only lanes can have a significant positive impact on efficiency and safety when the market penetration rate of AVs is low.

scholarly journals Towards an End-to-End Analysis and Prediction System for Weather, Climate, and Marine Applications in the Red Sea

2020 ◽  
pp. 1-61 ◽  
Author(s):  
Ibrahim Hoteit ◽  
Yasser Abualnaja ◽  
Shehzad Afzal ◽  
Boujemaa Ait-El-Fquih ◽  
Triantaphyllos Akylas ◽  
...  
Keyword(s):  
High Resolution ◽  
Red Sea ◽  
Data Driven ◽  
Environmental Applications ◽  
Regional Modeling ◽  
Prediction System ◽  
High Resolution Data ◽  
End To End ◽  
Marine Applications ◽  
Resolution Data

Capsule SummaryAn integrated, high resolution, data-driven regional modeling system has been recently developed for the Red Sea region and is being used for research and various environmental applications.

Utilizing Lane-Based Strategy to Incorporate Mixed Traffic in Interchange Control for Connected and Autonomous Vehicles

2019 ◽  
Vol 2673 (5) ◽  
pp. 454-465 ◽  
Author(s):  
Majeed Algomaiah ◽  
Zhixia Li
Keyword(s):  
Autonomous Vehicles ◽  
Penetration Rate ◽  
Traffic Signals ◽  
Heavy Vehicle ◽  
Market Penetration ◽  
Mixed Traffic ◽  
Traffic Demand ◽  
Communication Range ◽  
Throughput And Delay ◽  
Lane Based

This work examines the next-generation interchange control system (NIC) that aims to control connected and autonomous vehicles (CAV) at interchanges with the consideration of different mixed traffic cases. The first objective of the paper is to test several parameters including traffic demand, heavy vehicle percentage, communication range, and advance stop line (ASL) to investigate their impact on throughput and delay. The second objective is to incorporate mixed traffic in the NIC, utilizing a lane-based strategy that is responsive to market penetration rates. The NIC coordinates vehicles to traverse the interchange terminal by using a reservation-based control strategy with a first-come-first-served (FCFS) reservation protocol. The algorithm of this system was modeled in the simulation software package VISSIM using a slightly modified real-world scenario of interchange. The evaluation of the system starts with testing some key variables when market penetration rate is 100%. The results demonstrate that the increase in traffic demand and heavy vehicle percentage affects the performance of the NIC by increasing the delay. Although the effects of communication range and advance stop location do not have clear patterns, the communication range of 600 ft and ASL of 100 ft indicate a relatively lower delay. Throughput and delay results reveal that the NIC outperforms traffic signals when the market penetration rate is 75%, whereas a 25% market penetration rate provides similar performance to traffic signals.

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