Introduction Of Automated Vehicles Research Articles

Operational safety of automated and human driving in mixed traffic environments: A perspective of car-following behavior

The advent of automated vehicles (AVs) will provide opportunities for safer, smoother, and smarter road transportation. During the transition from the current human-driven vehicle (HV) to a fully AV traffic environment, there will be a mixed traffic flow including both HVs and AVs. The impact of introducing AVs into existing traffic, however, has not yet been fully understood. In this paper, we advance this understanding by conducting mixed traffic safety evaluation from the perspective of car-following behavior using real-world AV operational data of mixed traffic. To understand how the AVs impact other vehicles on the road, we analyzed the operational behaviors of HV-following-HV, AV-following-HV, and HV-following-AV. A selected car-following model is calibrated, and results show that there are significant differences between the HV-following-HV and the other two groups, indicating safe AV behavior and changes in HV behavior (i.e. less aggressive, safer) after the introduction of AVs into the traffic. Additionally, to understand AV behavioral safety, we investigate behavior predictions (one of the most critical inputs for AVs to make car-following decisions) of AVs and their surrounding vehicles using a mature baseline model and a new Conditional Variational Autoencoder (CVAE) framework. The result shows potential risks of inaccurate predictions of the baseline model and the necessity to consider additional factors, such as vehicle interactions and driver behavior, into the prediction for risk mitigation. Arterial vehicle trajectory data from the Lyft Level 5 Dataset is applied to test the proposed methodological framework to understand the car-following safety risks of HVs and AVs in the mixed traffic stream.

Impact of Automated Vehicles on Urban Form

Automated vehicles (AV) are a rapidly developing technology. It is expected that completely self-driving cars in the largest cities of the world will be by 2040-2050. All over the world technical, political, legal and economic issues of self-driving technology are being explored. Self-driving cars will not only affect daily trips, but also will change the appearance of cities. Automobiles — their size, accessibility, and environmental impact — are an important factor in urban planning in the last century. This article describes the problems of introducing self-driving cars and its impact on urban form and architecture. The technology of self-driving cars will change the urban environment. Those changes will be irreversible, so it is important to have an idea about the consequences of its implementation, in order to avoid negative developments and try to get the maximum benefit from the introduction of automated vehicles. Following the analysis of the scientific literature, two main scenarios for the introduction of automated vehicles according to the form of ownership — individual and public, including the system of a mass self-driving taxi — were considered. For these scenarios, the factors of spatial influence of AV on the urban form were combined into three groups of principles: the impact on urbanization; the impact on road infrastructure; the influence at the local level. The question of how the introduction of AV will affect the density and structure of the city, as well as the quality of human life. The problem of early obsolescence of infrastructure projects has been raised. Priority of an integrated approach is shown when choosing a strategy for further development.

Automated, electric, or both? Investigating the effects of transportation and technology scenarios on metropolitan greenhouse gas emissions

The anticipated impacts of automated transportation are numerous. Of importance are the effects on energy consumption and greenhouse gas (GHG) emissions. Can automation promote low carbon transportation? Another question often raised relates to the appropriateness of current tools. Will existing tools become irrelevant in the face of the disruptive changes or can we extend the capabilities of our models to broadly capture the effects of automated transportation? This study presents an approach to estimate fuel-cycle GHG emissions, integrated within an activity-based travel demand model for the Greater Toronto and Hamilton Area. The model chain is used to evaluate different shares of automated vehicles (AV), and the effects of electrification of AVs. Daily operating GHG emissions were estimated at 29,000 t in CO2eq with 96% attributed to private vehicles and 4% to transit. While sharing a minor portion of emissions, the public transit system carries 32% of daily passenger kilometers traveled. When accounting for fuel-cycle emissions, the daily total was estimated to be over 36,000 t for private vehicles. With the introduction of AVs, higher vehicle kilometers travelled (3.6%–5.4%) and GHG emissions (2.5%) are expected. However, electrification of AVs can reduce regional GHG emissions (5%), and emission intensities of all vehicles (11%).