Autonomous Vehicle Perception Sensor Data, Motion Planning and Object Recognition Algorithms, and Virtual Simulation Modeling Tools in Smart Sustainable Intelligent Transportation Systems.

• Author: Bratu, Sofia

1. Introduction

   Real-time navigation and mapping tools, operational autonomy, and efficient maneuvering typify self-driving car technologies. The purpose of my systematic review is to examine the recently published literature on smart sustainable intelligent transportation systems and integrate the insights it configures on autonomous vehicle perception sensor data, motion planning and object recognition algorithms, and virtual simulation modeling tools. By analyzing the most recent (2019-2022) and significant (Web of Science, Scopus, and ProQuest) sources, my paper has attempted to prove that road traffic management and monitoring necessitate vehicle localization and networking among autonomous driving systems. The actuality and novelty of this study are articulated by addressing autonomous driving perception algorithms, spatial recognition technologies, and virtual reality modeling tools (Lazaroiu, 2018; Nica, 2017; Rogers and Zvarikova, 2021), that is an emerging topic involving much interest. My research problem is whether real-time data and image processing tools, monitoring and predictive capabilities, and algorithm-driven sensing devices optimize transportation analytics.

   In this review, prior findings have been cumulated indicating that object localization algorithms, edge computing techniques, and autonomous vehicle perception sensors (Lazaroiu et al, 2020; Pocol et al, 2022; Valaskova et al., 2022a, b) improve navigation accuracy. The identified gaps advance modeling and simulation tools, smart infrastructure sensors, and cognitive computing systems. My main objective is to indicate that urban sensing technologies develop on machine learning algorithms, predictive modeling techniques, and movement and behavior tracking tools. This systematic review contributes to the literature on deep learning-based computer vision algorithms, sensing technologies, and ambient sound processing, together with data visualization and traffic flow prediction tools (Andronie et al, 2021a, b; Pop et al., 2021), by clarifying that virtual simulation tools, urban sensor networks, and spatio-temporal fusion algorithms (Konhausner et al, 2021; Mircica, 2020; Poliak et al, 2021a, b) optimize autonomous driving technologies.

2. Theoretical Overview of the Main Concepts

   Autonomous driving systems will optimize traffic throughput, perception systems, vehicular networks and communications, and road safety through dynamic decision-making mechanisms. Autonomous driving technologies leverage virtual modeling and simulation tools and real-time Internet of Things-sensing data collection. Vehicular communication technologies integrate visual perception algorithms, geospatial mapping tools, and spatial data analytics. Object detection technologies and autonomous vehicle planning algorithms decrease collisions. The manuscript is organized as following: theoretical overview (section 2), methodology (section 3), object detection technologies and autonomous vehicle planning algorithms (section 4), virtual simulation tools, urban sensor networks, and spatio-temporal fusion algorithms (section 5), remote sensing technologies, deep learning-based computer vision algorithms, and autonomous mobility systems (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 "smart sustainable intelligent transportation systems" + "autonomous vehicle perception sensor data," "motion planning and object recognition algorithms," and "virtual simulation modeling tools." The search terms were determined as being the most employed words or phrases across the analyzed literature. As I inspected research published between 2019 and 2022, only 91 articles satisfied the eligibility criteria. By removing controversial findings, outcomes unsubstantiated by replication, too imprecise material, or having similar titles, I decided upon 16, 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: AXIS, Dedoose, MMAT, and SRDR (Figures 1-6).

4. Object Detection Technologies and Autonomous Vehicle Planning Algorithms

   Autonomous driving perception algorithms, spatial recognition technologies, and virtual reality modeling tools (Ren et al., 2022; Shannon et al., 2021) are pivotal in urban mobility data analytics. Urban sensing technologies develop on machine learning algorithms, predictive modeling techniques, and movement and behavior tracking tools. A road environment comprising without exception connected autonomous vehicles may result in a significant decrease in injuries. Object localization algorithms, edge computing techniques, and autonomous vehicle perception sensors improve navigation accuracy.

   Urban cloud data, object detection and tracking algorithms, autonomous vehicle perception systems, virtual simulation tools, and remote sensor networks (Kanagaraj et al., 2021; Mutz et al., 2021) are instrumental in routing and spatial computing technologies. Self-driving car localization is required in scene perception, updating maps, object detection and tracking, and route planning. Computer vision techniques can assist in image processing leveraged in lane detection.

   Spatial cognition and predictive control algorithms and sensing and computing technologies (Duleba et al., 2021; Li et al., 2021) shape networked urban environments. Object detection technologies and autonomous vehicle planning algorithms decrease collisions. Intelligent transportation systems can ensure cooperative driving safety and...