Autonomous Vehicle Interaction Control Software, Urban Sensing and Computing Technologies, and Trajectory Planning and Route Detection Algorithms in Networked Transport Systems.

• Author: Ljungholm, Doina Popescu

1. Introduction

   Big geospatial data analytics, simulation modeling tools, and spatial computing technology are pivotal in intelligent transportation infrastructures. The purpose of my systematic review is to examine the recently published literature on networked transport systems and integrate the insights it configures on autonomous vehicle interaction control software, urban sensing and computing technologies, and trajectory planning and route detection algorithms. By analyzing the most recent (2021-2022) and significant (Web of Science, Scopus, and ProQuest) sources, my paper has attempted to prove that dynamic route optimization, geospatial data mining tools, and sensor fusion algorithms (Andronie et al., 2021a, b, c; Kovacova et al., 2022) articulate networked digital infrastructures. The actuality and novelty of this study are articulated by addressing extended reality-based digital services, motion planning and obstacle avoidance algorithms, virtual mapping tools, and sensing and computing technologies, that is an emerging topic involving much interest. My research problem is whether image recognition technologies, mobility data processing and multi-sensor environmental data fusion tools, and spatial computing algorithms enhance travel safety.

   In this review, prior findings have been cumulated indicating that autonomous vehicle routing and navigation (Bacalu, 2021; Kral et al, 2020; Pelau et al, 2021; Poliak et al, 2021) necessitate wireless sensor data mining tools and geospatial mapping technologies. The identified gaps advance spatial simulation algorithms, augmented analytics tools, and real-world connected vehicle data. My main objective is to indicate that intelligent transportation infrastructures develop on mobility simulation and geospatial data visualization tools and blockchain and extended reality technologies. This systematic review contributes to the literature on spatial data visualization tools and multi-sensor remote sensing data (Carter, 2022; Lazaroiu, 2018; Pocol et al., 2022; Popescu et al., 2018) by clarifying that geospatial mapping technologies (Gasparin and Schinckus, 2022; Mircica, 2020; Popescu, 2018; Vatamanescu et al, 2020) integrate motion planning tools, Internet of Things sensing infrastructures, and perception and prediction dynamics.

2. Theoretical Overview of the Main Concepts

   Geospatial mapping tools, urban computing algorithms, and urban Internet of Things-sensing tools are instrumental in connected vehicle technologies. Deep learning-based routing and navigating decisions, real-time predictive analytics, geospatial mapping tools, and sensor-based traffic flow data shape big data-driven transportation planning and engineering. Deep learning-based ambient sound processing tools integrate virtual data analytics and computer vision algorithms. The manuscript is organized as following: theoretical overview (section 2), methodology (section 3), deep learning-based routing and navigating decisions, real-time predictive analytics, geospatial mapping tools, and sensor-based traffic flow data (section 4), spatial simulation algorithms, augmented analytics tools, and real-world connected vehicle data (section 5), real-time data visualization and environment perception tools, augmented and virtual technologies, and algorithm-driven sensing devices (section 6), discussion (section 7), synopsis of the main research outcomes (section 8), conclusions (section 9), limitations, implications, and further directions of research (section 10).

3. Methodology

   Throughout April 2022, I performed a quantitative literature review of the Web of Science, Scopus, and ProQuest databases, with search terms including "networked transport systems" + "autonomous vehicle interaction control software," "urban sensing and computing technologies," and "trajectory planning and route detection algorithms." The search terms were determined as being the most employed words or phrases across the analyzed literature. As I inspected research published between 2021 and 2022, only 93 articles satisfied the eligibility criteria. By eliminating controversial findings, outcomes unsubstantiated by replication, too imprecise material, or having similar titles, I decided upon 13, generally empirical, sources (Tables 1 and 2). Extracting and inspecting publicly accessible files (scholarly sources) as evidence, before the research began no institutional ethics approval was required. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AMSTAR, Distiller SR, MMAT, and ROBIS (Figures 1-6).

4. Deep Learning-based Routing and Navigating Decisions, Geospatial Mapping Tools, and Sensor-based Traffic Flow Data

   Extended reality-based digital services, motion planning and obstacle avoidance algorithms, virtual mapping tools, and sensing and computing technologies (Penmetsa et al., 2021; Yang et al., 2021) configure urban transportation systems. Data collection modules gather sensing and vehicle status data by use of onboard sensors in autonomous driving systems. Users' perceptions of self-driving and technology-assisted vehicles are shaped by deleterious incidents that jeopardize the safety of road users.

   Big geospatial data analytics, computer vision algorithms, and smart spatial planning tools (Duleba et al., 2021; Shannon et al., 2021) assist autonomous vehicle routing and navigation. Deep learning-based routing and navigating decisions, real-time predictive analytics, geospatial mapping tools, and sensor-based traffic flow data shape big data-driven transportation planning and engineering. By harnessing cameras, lasers, and radar that supervise the inconstant driving environment, advanced driver-assistance systems can evaluate a coherent feed of external data in relation to vehicle surroundings.

   Predictive simulations, data visualization tools, road environment...