scholarly journals Evaluating the impacts of autonomous cars on the capacity of freeways in Brazil using the HCM-6 PCE methodology

TRANSPORTES ◽  
2021 ◽  
Vol 29 (2) ◽  
Author(s):  
Renan Favero ◽  
José Reynaldo Setti
Keyword(s):  
Autonomous Vehicles ◽  
Statistical Analyses ◽  
Adjustment Factor ◽  
Control Variables ◽  
Autonomous Cars ◽  
Heavy Goods Vehicles ◽  
The Impact

This paper analyses the factors that affect the impact of autonomous vehicles (AVs) on the capacity of a freeway in Brazil using an adaptation of the HCM-6 procedure for truck PCE estimation. A version of Vissim, recalibrated to represent traffic streams and AVs on Brazilian freeways, was used to simulate more than 25,000 scenarios representing combinations of traffic (e.g., AV fleets, AV platoons, percentage of AVs and of heavy goods vehicles) and road (grades and number of lanes) characteristics. AV impacts on capacity were evaluated by means of the capacity adjustment factor (CAF) and a model to estimate CAF from control variables was fitted and validated. The results indicate increases of up to 30% in capacity with 60% of platooning-capable AVs. Statistical analyses show that the fraction of AVs in the stream and the proportion of platooning-capable AVs are the factors with the greatest impact on this increase in capacity.

scholarly journals Assessment of Social Acceptance for Autonomous Vehicles in Southeastern Poland

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5778
Author(s):  
Agnieszka Dudziak ◽  
Monika Stoma ◽  
Andrzej Kuranc ◽  
Jacek Caban
Keyword(s):  
Autonomous Vehicles ◽  
Social Acceptance ◽  
New Technologies ◽  
Consumer Preferences ◽  
Autonomous Vehicle ◽  
Developed Countries ◽  
Automotive Market ◽  
Advantages And Disadvantages ◽  
Autonomous Cars ◽  
The Impact

New technologies reaching out for meeting the needs of an aging population in developed countries have given rise to the development and gradual implementation of the concept of an autonomous vehicle (AV) and have even made it a necessity and an important business paradigm. However, in parallel, there is a discussion about consumer preferences and the willingness to pay for new car technologies and intelligent vehicle options. The main aim of the study was to analyze the impact of selected factors on the perception of the future of autonomous cars by respondents from the area of Southeastern Poland in terms of a comparison with traditional cars, with particular emphasis on the advantages and disadvantages of this concept. The research presented in this study was conducted in 2019 among a group of 579 respondents. Data analysis made it possible to identify potential advantages and disadvantages of the concept of introducing autonomous cars. A positive result of the survey is that 68% of respondents stated that AV will be gradually introduced to our market, which confirms the high acceptance of this technology by Poles. The obtained research results may be valuable information for governmental and local authorities, but also for car manufacturers and their future users. It is an important issue in the area of shaping the strategy of actions concerning further directions of development on the automotive market.

The Impact of Mental States on Semi-autonomous Driving Takeover Performance: A Systematic Review

2020 ◽  
Vol 64 (1) ◽  
pp. 1372-1376
Author(s):  
Gaojian Huang ◽  
Christine Petersen ◽  
Brandon J. Pitts
Keyword(s):  
Systematic Review ◽  
Autonomous Vehicles ◽  
Response Times ◽  
Autonomous Driving ◽  
Mental States ◽  
Manual Control ◽  
Monitoring Systems ◽  
Wide Range ◽  
Different Types ◽  
The Impact

Semi-autonomous vehicles still require drivers to occasionally resume manual control. However, drivers of these vehicles may have different mental states. For example, drivers may be engaged in non-driving related tasks or may exhibit mind wandering behavior. Also, monitoring monotonous driving environments can result in passive fatigue. Given the potential for different types of mental states to negatively affect takeover performance, it will be critical to highlight how mental states affect semi-autonomous takeover. A systematic review was conducted to synthesize the literature on mental states (such as distraction, fatigue, emotion) and takeover performance. This review focuses specifically on five fatigue studies. Overall, studies were too few to observe consistent findings, but some suggest that response times to takeover alerts and post-takeover performance may be affected by fatigue. Ultimately, this review may help researchers improve and develop real-time mental states monitoring systems for a wide range of application domains.

scholarly journals An Efficiency Enhancing Methodology for Multiple Autonomous Vehicles in an Urban Network Adopting Deep Reinforcement Learning

2021 ◽  
Vol 11 (4) ◽  
pp. 1514 ◽  
Author(s):  
Quang-Duy Tran ◽  
Sang-Hoon Bae
Keyword(s):  
Reinforcement Learning ◽  
Traffic Congestion ◽  
Autonomous Vehicles ◽  
Penetration Rate ◽  
Autonomous Vehicle ◽  
Effective Means ◽  
Urban Network ◽  
Learning Agents ◽  
Policy Optimization ◽  
The Impact

To reduce the impact of congestion, it is necessary to improve our overall understanding of the influence of the autonomous vehicle. Recently, deep reinforcement learning has become an effective means of solving complex control tasks. Accordingly, we show an advanced deep reinforcement learning that investigates how the leading autonomous vehicles affect the urban network under a mixed-traffic environment. We also suggest a set of hyperparameters for achieving better performance. Firstly, we feed a set of hyperparameters into our deep reinforcement learning agents. Secondly, we investigate the leading autonomous vehicle experiment in the urban network with different autonomous vehicle penetration rates. Thirdly, the advantage of leading autonomous vehicles is evaluated using entire manual vehicle and leading manual vehicle experiments. Finally, the proximal policy optimization with a clipped objective is compared to the proximal policy optimization with an adaptive Kullback–Leibler penalty to verify the superiority of the proposed hyperparameter. We demonstrate that full automation traffic increased the average speed 1.27 times greater compared with the entire manual vehicle experiment. Our proposed method becomes significantly more effective at a higher autonomous vehicle penetration rate. Furthermore, the leading autonomous vehicles could help to mitigate traffic congestion.

scholarly journals Impacts of Autonomous Vehicles on Greenhouse Gas Emissions—Positive or Negative?

2021 ◽  
Vol 18 (11) ◽  
pp. 5567
Author(s):  
Moneim Massar ◽  
Imran Reza ◽  
Syed Masiur Rahman ◽  
Sheikh Muhammad Habib Abdullah ◽  
Arshad Jamal ◽  
...  
Keyword(s):  
Greenhouse Gas ◽  
Autonomous Vehicles ◽  
Significant Contribution ◽  
Ghg Emissions ◽  
Interval Estimate ◽  
Modal Shift ◽  
Vehicle Mile Travel ◽  
Emission Changes ◽  
Negative Impacts ◽  
The Impact

The potential effects of autonomous vehicles (AVs) on greenhouse gas (GHG) emissions are uncertain, although numerous studies have been conducted to evaluate the impact. This paper aims to synthesize and review all the literature regarding the topic in a systematic manner to eliminate the bias and provide an overall insight, while incorporating some statistical analysis to provide an interval estimate of these studies. This paper addressed the effect of the positive and negative impacts reported in the literature in two categories of AVs: partial automation and full automation. The positive impacts represented in AVs’ possibility to reduce GHG emission can be attributed to some factors, including eco-driving, eco traffic signal, platooning, and less hunting for parking. The increase in vehicle mile travel (VMT) due to (i) modal shift to AVs by captive passengers, including elderly and disabled people and (ii) easier travel compared to other modes will contribute to raising the GHG emissions. The result shows that eco-driving and platooning have the most significant contribution to reducing GHG emissions by 35%. On the other side, easier travel and faster travel significantly contribute to the increase of GHG emissions by 41.24%. Study findings reveal that the positive emission changes may not be realized at a lower AV penetration rate, where the maximum emission reduction might take place within 60–80% of AV penetration into the network.

Autonomous vehicles: a review of the ethical, social and economic implications of the AI revolution

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shweta Banerjee
Keyword(s):  
Artificial Intelligence ◽  
Autonomous Vehicles ◽  
Design Methodology ◽  
Technological Advancement ◽  
Economic Implications ◽  
Content Type ◽  
The Public ◽  
Autonomous Cars ◽  
The Subject ◽  
Practical Implications

PurposeThere are ethical, legal, social and economic arguments surrounding the subject of autonomous vehicles. This paper aims to discuss some of the arguments to communicate one of the current issues in the rising field of artificial intelligence.Design/methodology/approachMaking use of widely available literature that the author has read and summarised showcasing her viewpoints, the author shows that technology is progressing every day. Artificial intelligence and machine learning are at the forefront of technological advancement today. The manufacture and innovation of new machines have revolutionised our lives and resulted in a world where we are becoming increasingly dependent on artificial intelligence.FindingsTechnology might appear to be getting out of hand, but it can be effectively used to transform lives and convenience.Research limitations/implicationsFrom robotics to autonomous vehicles, countless technologies have and will continue to make the lives of individuals much easier. But, with these advancements also comes something called “future shock”.Practical implicationsFuture shock is the state of being unable to keep up with rapid social or technological change. As a result, the topic of artificial intelligence, and thus autonomous cars, is highly debated.Social implicationsThe study will be of interest to researchers, academics and the public in general. It will encourage further thinking.Originality/valueThis is an original piece of writing informed by reading several current pieces. The study has not been submitted elsewhere.

Investors’ Attention and American Depository Receipts Pricing: Evidence from Indian Stocks

2019 ◽  
Vol 9 (12) ◽  
pp. 381-386
Author(s):  
A Sarath Babu
Keyword(s):  
Volume Index ◽  
American Depository Receipts ◽  
Control Variables ◽  
Search Volume ◽  
Depository Receipts ◽  
Book To Market Ratio ◽  
Google Search ◽  
The Impact

This paper is an attempt to examine the impact of investors’ attention on returns and the traded volume of American Depository Receipts prices for selected ten Indian Stocks. The Google search volume index has been used as a proxy for investors’ attention in this paper. However, factors such as size and book to market ratio were used to indicate as control variables. The results reveal that investors’ attention variable significantly affects ADRs traded volume, but has no impact on the ADR prices.

Dysregulation of ECRG4 is associated with malignant properties and of prognostic importance in human gastric cancer

2021 ◽  
pp. 1-12
Author(s):  
Yanjie You ◽  
Shengjuan Hu
Keyword(s):  
Gastric Cancer ◽  
Prognostic Significance ◽  
Statistical Analyses ◽  
Human Gastric Cancer ◽  
Advanced Tumor Stage ◽  
Protein Levels ◽  
Cancer Pathogenesis ◽  
Advanced Tumor ◽  
Cancer Tissues ◽  
The Impact

BACKGROUND: We have previously characterized esophageal carcinoma-related gene 4 (ECRG4) as a novel tumor suppressor gene, which is frequently inactivated in nasopharyngeal carcinoma and breast cancer. Nevertheless, the expression status and prognostic significance of ECRG4 maintain elusive in human gastric cancer. Herein, we examined ECRG4 expression profile in gastric cancer and assessed its association with clinicopathological characteristics and patient survival. METHODS: Online data mining, real-time RT-PCR and immunohistochemistry were employed to determined ECRG4 expression at transcriptional and protein levels in tumors vs. noncancerous tissues. Statistical analyses including the Kaplan-Meier survival analysis and the Cox hazard model were utilized to detect the impact on clinical outcome. Moreover, ECRG4 expression was silenced in gastric cancer SGC7901 cells, and cell proliferation, colony formation and invasion assays were carried out. RESULTS: ECRG4 mRNA and protein levels were obviously downregulated in cancer tissues than noncancerous tissues. Statistical analyses demonstrated that low ECRG4 expression was found in 34.5% (58/168) of primary gastric cancer tissues, which was associated with higher histological grade (P= 0.018), lymph node metastasis (P= 0.011), invasive depth (P= 0.020), advanced tumor stage (P= 0.002) and poor overall survival (P< 0.001). Multivariate analysis showed ECRG4 expression is an independent prognostic predictor (P< 0.001). Silencing ECRG4 expression promoted gastric cancer cell growth and invasion. Western blot analysis revealed the anti-metastatic functions of ECRG4 by downregulating of E-cadherin and α-Catenin, as well as upregulating N-cadherin and Vimentin. CONCLUSIONS: Our observations reveal that ECRG4 expression is involved in gastric cancer pathogenesis and progression, and may serve as a candidate prognostic biomarker for this disease.

scholarly journals Feedforward ins aiding: an investigation of maneuvers for in-flight alignment

2007 ◽  
Vol 18 (4) ◽  
pp. 459-470 ◽  
Author(s):  
Jacques Waldmann
Keyword(s):  
Autonomous Vehicles ◽  
Inertial Sensors ◽  
Low Cost ◽  
Measurement Unit ◽  
Model Mismatch ◽  
External Data ◽  
Rate Gyro ◽  
External Position ◽  
The Impact ◽  
Gyro Drift

Navigation in autonomous vehicles involves integrating measurements from on-board inertial sensors and external data collected by various sensors. In this paper, the computer-frame velocity error model is augmented with a random constant model of accelerometer bias and rate-gyro drift for use in a Kalman filter-based fusion of a low-cost rotating inertial navigation system (INS) with external position and velocity measurements. The impact of model mismatch and maneuvers on the estimation of misalignment and inertial measurement unit (IMU) error is investigated. Previously, the literature focused on analyzing the stripped observability matrix that results from applying piece-wise constant acceleration segments to a stabilized, gimbaled INS to determine the accuracy of misalignment, accelerometer bias, and rate-gyro drift estimation. However, its validation via covariance analysis neglected model mismatch. Here, a vertically undamped, three channel INS with a rotating IMU with respect to the host vehicle is simulated. Such IMU rotation does not require the accurate mechanism of a gimbaled INS (GINS) and obviates the need to maneuver away from the desired trajectory during in-flight alignment (IFA) with a strapdown IMU. In comparison with a stationary GINS at a known location, IMU rotation enhances estimation of accelerometer bias, and partially improves estimation of rate-gyro drift and misalignment. Finally, combining IMU rotation with distinct acceleration segments yields full observability, thus significantly enhancing estimation of rate-gyro drift and misalignment.

scholarly journals Exploring the Mechanism of Crashes with Autonomous Vehicles Using Machine Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Hengrui Chen ◽  
Hong Chen ◽  
Ruiyu Zhou ◽  
Zhizhen Liu ◽  
Xiaoke Sun
Keyword(s):  
Machine Learning ◽  
Autonomous Vehicles ◽  
Classification And Regression Tree ◽  
Gradient Boosting ◽  
Support Vector ◽  
Crash Severity ◽  
Apriori Algorithm ◽  
Driving Mode ◽  
Extreme Gradient Boosting ◽  
The Impact

The safety issue has become a critical obstacle that cannot be ignored in the marketization of autonomous vehicles (AVs). The objective of this study is to explore the mechanism of AV-involved crashes and analyze the impact of each feature on crash severity. We use the Apriori algorithm to explore the causal relationship between multiple factors to explore the mechanism of crashes. We use various machine learning models, including support vector machine (SVM), classification and regression tree (CART), and eXtreme Gradient Boosting (XGBoost), to analyze the crash severity. Besides, we apply the Shapley Additive Explanations (SHAP) to interpret the importance of each factor. The results indicate that XGBoost obtains the best result (recall = 75%; G-mean = 67.82%). Both XGBoost and Apriori algorithm effectively provided meaningful insights about AV-involved crash characteristics and their relationship. Among all these features, vehicle damage, weather conditions, accident location, and driving mode are the most critical features. We found that most rear-end crashes are conventional vehicles bumping into the rear of AVs. Drivers should be extremely cautious when driving in fog, snow, and insufficient light. Besides, drivers should be careful when driving near intersections, especially in the autonomous driving mode.

Impact of corruption on Chinese investment in African countries

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shilin Yuan ◽  
Haiyang Chen ◽  
Wei Zhang
Keyword(s):  
Developing Countries ◽  
Developing Country ◽  
Host Country ◽  
Practical Implication ◽  
Host Countries ◽  
African Countries ◽  
Control Variables ◽  
Content Type ◽  
The Impact ◽  
New Evidence

Purpose This paper aims to examine the impact of host country corruption on foreign direct investment (FDI) from China to developing countries in Africa. With the opposing arguments that corruption is detrimental to or instrumental in FDI and mixed empirical evidence, this paper contributes to the literature by providing new evidence on the issue. Additionally, little research has been done on the impact of corruption on FDI made by developing country multinationals to developing countries. This paper fills a void in this area. Design/methodology/approach Based on the published literature, as well as China and Africa contexts, the authors develop hypotheses that host countries with low corruption receive more FDI and resource-seeking investments weaken the relationship. The annual stock of Chinese FDI in 35 African countries, host country corruption data and other control variables from 2007 to 2015 are collected. Feasible generalized least squares models are used to test the hypotheses. Additional robustness tests are also conducted. Findings The findings support the hypotheses. Specifically, Chinese investors make more investments in host countries with low corruption except for resource-seeking investments in resource-rich host counties. The results are statistically significant accounting for various control variables. The results of the robustness tests show that the main findings are robust. Originality/value First, this study provides new evidence on the impact of corruption on FDI. Second, this study also fills a void by examining FDI from a developing country, China to other developing countries in Africa. Finally, this study also has a practical implication for Chinese multinationals investing in Africa.

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