Desktop version

Home arrow Engineering

  • Increase font
  • Decrease font

<<   CONTENTS   >>

Using Computer Simulations for Quantifying Impact of Infrastructure Changes for Autonomous Vehicles

Table of Contents:

ABSTRACT Interest in autonomous vehicles (AVs) has geared up globally in recent years with a focus on safety and low fuel consumption. Several industries and research organizations are collaborating on the development of AVs. Apart from this, government transportation agencies around the world are involved in bringing AVs on roads by performing various physical and virtual tests. An essential aspect of deploying AVs is increasing passengers' safety. It is highly advisable to validate the AVs with the current road infrastructure and propose efficient AV-friendly road infrastructure, including several lanes, lane widths, and bus bay designs. This chapter considers the bus bay design structure specific to Singapore for validation and proposing new bus bay designs. Two new bus bay designs are proposed and compared with existing bus bay designs. The comparison is performed based on queue length, bus arrival, and bus exit time. We have implemented the simulation using an integrated simulator developed in-house by combining a virtual test drive (VTD) and a robot operating system (ROS). We deployed real traffic data from the Ang Mo Kio (AMK) district area in Singapore for this purpose. The results show that the queue length developed in the proposed bus bay designs are shorter than the existing bus bay design, especially in high traffic density scenarios.


Autonomous vehicles (AVs) are a highly anticipated innovation in the intelligent transportation industry, and we see a significant surge in its growth. Scientists, engineers, and policy-makers around the world are working together to reduce the gap where AV becomes a reality to overcome urban mobility challenges. Urban mobility and associated congestion is a recognized, yet increasingly complex, challenge. The transportation and infrastructure industries are collaborating and defining their investment priorities to adapt to the upcoming advancements of AVs. We are also seeing AVs as a forthcoming option for improving the first-mile and last-mile connectivity. The micro transportation model, such as shared e-scooters and e-bikes, has received mixed reviews in terms of safety and comfort from both users and non-users of the service. As all these involved industries race to make AVs a reality, we will witness significant infrastructure changes in urban cities adapting to a driverless future. The technological advances in a virtual simulation, internet of things (IoT), artificial intelligence (AI), and machine learning linked with city transportation has created myriad opportunities for cities to overcome these challenges [1]. In this chapter, we present computer simulations for quantifying the impact of infrastructure change for using AVs in an urban city, in a virtual environment. The chapter also discusses different designs of infrastructure and their impact on traffic flow where AV is a part of that traffic. We have investigated the effect of infrastructure changes in an urban city, with emphasis on crowded locations like bus stops near train stations.

The rest of this chapter is organized as follows. In Section 5.2, we discuss the literature related to our study. In Section 5.3, we describe the simulation environment where we discuss various software and the method we used for simulation. In Section 5.4, we propose different bus bay designs, which also contain the proposed designs and the method adopted to simulate them. This section also provides the assumptions made for simulations. In Section 5.5, we discuss the results where we analyze the performance of designs based on the performance indicator of queue length with different parameters. Finally, in Section 5.6, we provide a summary and ideas for future research.

Literature Review

Advances in the field of robotics, high performing computational capabilities, and the ability to analyze large sets of data are promising signs for a future with AVs as a mainstream reality. We also see AVs as an upcoming option for improving first-mile and last-mile connectivity [1]. On the other hand, there are significant advances in the multidisciplinary field of intelligent transportation systems (ITS) combined with information technology [2]. A large proportion of the existing work on intelligent transportation systems focuses on assisting human drivers in avoiding collisions at intersections [3, 4]. However, there are also studies with AVs as an upcoming option for improving first-mile and last-mile connectivity, which comes with the potential for safer travel and reduced congestion [5, 6]. As the involved industries race to make AVs a reality, we will witness significant infrastructure changes in urban cities.

Meyer et al. [7] explore various scenarios of potential autonomous mobility-based transportation concerning accessibility. The authors conclude with the possibility of a significant increase in accessibility from AVs. The research also mentions that depending on the magnitude of gained capacity from AVs, an equivalent of 15 years of infrastructure investment may be required. Hence, stakeholders must gain a realistic view of changes to be able to make informed decisions. This brings up an underlying question, are our cities ready for AVs? For example, Hobert et al. [8] discuss the use of vehicle-to- everything (V2X) technology with AVs and how the enhanced infrastructure and combination of both technologies (V2X and AV) will be required to increase safety and traffic efficiency. There is also research underway on AV path planning [9,10] that focuses on the shortest path algorithm and path planning in unknown environments.

There have been studies on implementing future technologies in a simulation environment to test their viability and experiment with different test conditions.

Developing a city model has its roots in traditional city mapping [11], and combining digital data with spatial and temporal knowledge provides endless simulation possibilities. Wang et al. [12] have used the virtual environment to model the road surfaces to allow easier road distance calculation. Donikian [13] has developed a model of the virtual urban environment to study the realistic behavior of car drivers and pedestrians and their interaction with each other. Virtual models for transportation sectors have focused on different aspects, ranging from traditional transportation modeling to covering some particular topics like building textures [14] and lightings [15].

With regard to the impact of AVs on urban areas, studies show that AVs require less headway distance and less lane width than conventional vehicles because of their high precision in driving. In this way, AVs can enhance the capacity of road infrastructure [16], providing a better opportunity for green and public spaces. On the other hand, the high traffic density due to less headway distance and lane width may create problems for the safety and comfort of pedestrians and cyclists, especially in mixed traffic conditions [17]. A research article by Gavanas [18] concludes that urban changes are crucial for preparing for a future with AVs.

From the literature review, we found that there is research underway on quantifying different aspects of AV research and development. But there is a gap in the details of urban changes required for AVs. With this study, we attempt to fulfill the literature gap of specific infrastructure that could be adopted for integrating AVs into existing traffic environments.

In our study, we have performed simulations using a combination of simulation environment software. The AV simulation software provides the capability of deploying different sensors such as virtual light detection and ranging (LIDAR) with physics-based rendering, vision sensors, and inertial measurement units. These sensor measurements help the vehicles to interpret the surrounding environment used for localization, perception, path planning, and collision avoidance, etc. Traffic simulation enables populating vehicles based on real traffic conditions. The integration of traffic data into the simulation helps to test and analyze different scenarios to quantify the impact of AV in cases like J-walking, valet parking, and last-mile transportation. The simulation results help in expressing the challenges involved in adapting an urban city for AVs in its environment.

<<   CONTENTS   >>

Related topics