Connected and Autonomous Transport Systems: Deep Learning-based Sensing Technologies, Data-driven Mobilities, and Intelligent Vehicular Networks.

• Author: Robinson, Rachel
• Position: Report

1. Introduction

   Autonomous vehicles are associated with enhanced road safety, mobility, and convenience throughout intelligent vehicular networks. (Di et al., 2020) Self-driving car deployment will materialize by an integration of heterogeneous traffic modes serving distinct travel needs of the users. (Cugurullo et al., 2020) More significant designations of situational awareness will further perceptions of vehicle accountability. (Bigman et al., 2019) The amplified social access to smart mobility technologies may affect traffic jam savings. (Cohena and Hopkins, 2019) Autonomous vehicle technologies can carry out the environmental and social upsides of large-scale vehicle sharing. (Clayton et al., 2020)

2. Conceptual Framework and Literature Review

   Connected and autonomous transport systems may be in conflict with human-driven vehicles. (Di et al., 2020) Groundbreaking transportation technologies may considerably impact the traffic systems and vehicle ownership. (Menon et al., 2019) Shared autonomous vehicle acceptance and adoption may minimize travel time utilization upsides, related productivity gains can be restricted to long-distance trips (Singleton, 2019), and can constitute a more significant mechanism than a carbon tax in relation to vehicle travel. (Jones and Leibowicz, 2019) As the user is released from chauffeuring, self-driving cars can become a fashionable way of both personal and professional dwelling. (Bissell et al., 2018) Seen as intentional agents capable of grasping and determining their objectives (Bratu, 2019; Kliestik et al., 2018; Kral et al., 2019; Mircica, 2019; Radulescu, 2019), self-driving cars will be ascribed moral accountability (Bigman et al., 2019) while simultaneously cutting down the accident rate as a result of eliminating the human factor. (Alonso et al., 2020)

3. Methodology and Empirical Analysis

   Using and replicating data from Accenture, AUVSI, Behaviour & Attitudes, Black & Veatch, Capgemini, eMarketer, Kennedys, Morning Consult, Perkins Coie, SAE, Statista, and YouGov, I performed analyses and made estimates regarding deep learning-based sensing technologies, data-driven mobilities, and intelligent vehicular networks. Data were analyzed using structural equation modeling.

4. Results and Discussion

   Connected and networked driving technologies will be instrumental in carrying out smart and sustainable urban mobility proposals. (Golbabaei et al., 2020) By providing high-resolution object data, LiDAR sensors are pivotal in smart transportation systems. (Tang et al., 2020a) The joint performance of hardware and software systems is carried out by use of multi-sensor fusion, big data-driven algorithmic decision-making, and machine learning technologies (Dubman, 2019; Kliestik et al., 2020; Mihaila, 2018; Peters et al., 2020), including cutting-edge context-aware scene analysis to single out exemplary driving approaches throughout intelligent vehicular networks. (Zhang et al., 2020) Users having particular personality traits tend to constitute antecedents for the acceptance and adoption of autonomous vehicles integrated into the data-driven traffic system. (Qu et al., 2020) Sustainable and smart urban transport systems should be adopted in shared mobility services. (Narayanan et al., 2020) (Tables 1-13)

   Pedestrians may not determine whether the car they are interacting with is traditional or autonomous, resulting in stress or adjusted crossing decisions. (Palmeiro et al., 2018) The rise of self-driving cars leads to ambivalence in the user behavior and in the influence of the vehicle maker on autonomous driving design. (Di et al., 2020) Self-driving car users may undertake tasks entailing relevant cognitive labor. (Bissell et al., 2018) Safety, comfort, fuel economy, and low emissions are feasible when users are used to the vehicle design of the autonomous cars. (Tang et al., 2020b) Shared self-driving cars are a pivotal component of on-demand mobility services throughout intelligent vehicular networks. (Golbabaei et al., 2020)

   Self-driving cars may considerably enhance the quality of smart transportation systems, particularly by cutting down the amount of stops, traffic snarl, and delay time. (Rezaei and Caulfield, 2021) Rainy weather, the distance between a vehicle and another road user, and the self-driving car velocity influence LiDAR detection performance in intelligent vehicular networks. (Tang et al., 2020a)...