Current Barriers in the year 2022 to Autonomous Vehicles as a viable method of transport for a/I (Research Paper Sample)

Current Barriers in The Year 2022 To Autonomous Vehicles as A Viable Method of Transport for All Name Student Number Module Code Course Name Words: 2904 Abstract In essence, autonomy denotes the ability and freedom to control one's affairs. In technology, the term autonomy is used to define the ability of a machine to perform its core functions with little to no human input. When applied in vehicles, an autonomous vehicle can be defined as a vehicle that incorporates numerous technologies for sensing and mapping out its environment, enabling it to safely maneuver around the environment with little to no human input. Regardless of this understanding, SAE International defined six levels ranging from "no automation" to the highest level, "full automation" [10]. In between, four levels range from assisted driving, partial automation, and conditional automation, where autonomous driving on specific roads and under special circumstances is experienced. The fourth level is 'high automation,' followed by the last level, full automation [10]. Even though enormous strides have been made in the field of autonomous vehicles, there exist numerous challenges and barriers ranging from technical and social to legislative issues. These challenges and barriers impede the use of autonomous vehicles as a viable method of transport for all. With this background in mind, this paper ventured to discuss these barriers in depth. The paper also integrated the technology adoption curve and discussed how a fully autonomous technology aligns with Europe's sustainable development goals. Technology Adoption Curve Tech giants such as Google, Tesla, and Mercedes-Benz have been testing fully autonomous vehicles. However, the actual purchase and use of fully autonomous vehicles are expected to commence within the next few years. Like any technology, market penetration of fully autonomous vehicles takes time, and it is usually one of the primary factors considered a barrier to large-scale adoption. The technology adoption curve offers a framework that can demonstrate how individuals from different backgrounds respond to innovative technological products in terms of demographics and psychological aspects [9]. Innovators are the first group in technology adoption. These are people willing to take risks and experiment with new technological products [9]. Such individuals depict the energy to take on new responsibilities and have a high level of enthusiasm for new experiences. In this case, the innovators consist of individuals eager to test the abilities of autonomous vehicles, and they can even volunteer to beta-test these vehicles. This was predicted to commence in the 2020s [8]. Early adopters closely follow the innovators. They depict similar characteristics to innovators as they are enthusiasts and enjoy new experiences. This group is also comfortable taking risks and loves to keep up with trending technology. One aspect distinguishing early adopters from innovators is that they must develop a strong opinion before trying a new technological product [9]. When applied in the case of an autonomous vehicle, early adopters can be equated to individuals who will quickly adopt autonomous vehicles once they are commercially available for purchase and relevant authorities have published their support for using these vehicles. Early majorities are the third group in the adoption curve. They present a unique way of reasoning because they consider efficiency and benefits more important than the popularity of new technology. As a result, they present a more logical and practical lens that is highly data-driven before a decision to adopt a technological product is undertaken [9]. They can be equated to people who venture into reputable websites to look for reviews, talk to early adopters of autonomous vehicles, and seek information before deciding to purchase. The late majority follows the early majority and portrays similar characteristics to those of the early majority. The late majorities heavily rely on a data-driven manner of reasoning. Despite being logical, they also tend to be very cautious and uncomfortable taking risks [9]. Such individuals wait to see how the technology being adopted affects the current state of affairs. They adopt innovative technologies, in this case, autonomous vehicles, only after they are convinced of the results. Laggards are the last group of people to adopt innovative technology. This group presents a highly conservative way of thinking and likes to hold on to already established products, posing a very high resistance to change [9]. Typically, they can only interact with very close friends and family members regarding the wants and expectations of new innovative technologies. This group depicts their unwillingness to change their established way of life. They easily give up on adopting innovative technologies if any mishap is experienced during the adoption. This group would be the last to adopt autonomous vehicles. The forecast revealed that it would take up to 2050s for the market penetration of autonomous vehicles to reach 80 -100% [8]. Barriers & SDG Industry Matrix SDG Industry Matrix Before looking at technical barriers, this section discussed how autonomous vehicles align with Europe's sustainable development goals. This discussion was done in relation to the SDG industry matrix for transportation. One aspect was that autonomous vehicles align with SDG 9, which supports resilient infrastructure and promotes sustainable industrialization and innovation [5]. Integrating technologies for sensing and computing to enable an autonomous vehicle lies within the lines of innovation and carries the potential of mitigating congestion. These technologies' potential benefits regarding reducing road fatalities are well aligned with SDG 11, which seeks to make cities and human settlements inclusive, safer, and sustainable [5]. In addition, autonomous vehicles are highly integrated with other emergent technologies like lithium batteries. These emergent technologies are highly associated with the reduction of carbon emissions. Therefore, autonomous vehicles align with SDG 13, which seeks to take urgent action to combat climate change and its impacts [5]. Technical Barriers Despite the potential that autonomous vehicles hold, current technical barriers hinder their deployment and use as a viable transport method for all. Strides made in data storage, computing, and aspects of precise positioning using accurate GPS systems offer robust operation [3]. Even so, one aspect that brings in technical difficulties is sensing. Major sensing technologies include cameras, LiDAR, radar, and ultrasonic sensors. Cameras are fitted in various car areas to offer real-time visual input of the adjacent car's surroundings [3]. LiDAR, conversely, makes use of light in the form of a pulsed laser to provide a ranging mechanism for objects around the car [3]. Radar, on the other hand, is used to detect objects, providing a distance measurement and speed on a real-time basis. Radar uses radio waves, and its principal operation largely depends on how these radio waves are reflected once they hit objects [3]. Current frequency ranges for radar use in autonomous vehicles include 24, 74, 77, and 79 GHz. Ultrasonic sensors are normally installed all around the car. These are significant in identifying objects close to the vehicle, supporting autonomous driving during low speeds. All these technologies are integrated, and inputs are processed using powerful onboard computer systems that run powerful operating software. Processed inputs are used to control and maneuver the vehicle in real-time [3]. The technologies used in autonomous vehicles have some shortcomings. For example, a camera's visibility is highly affected during bad weather conditions, such as when it rains or when fog is present [13]. While LiDAR shows great promise, issues such as range, negative impacts of reflective light sources, blizzards, morning fog, and car vibrations are attributed to fatal flaws in this technology [13]. Aspects such as interference, multipath propagation, and sensitivity are some challenges experienced with radar applications in autonomous vehicles. In addition, ultrasonic sensors lack the needed resolution to detect a small object, especially when the speed of an autonomous vehicle increases. Even though integrating all these systems might provide some form of robustness where one technology could potentially address the shortcomings of another, these shortcomings offer a real challenge, especially through the lens of safety [13]. The real test of these technologies comes during the deployment of the vehicles in a complex environment like intricate road conditions. Road conditions vary and are unpredictable with respect to geographical locations and terrain [13]. Complex traffic conditions also present an enormous challenge. In reality, autonomous vehicles will have to co-exist with conventional vehicles that depend on human input. Such an environment brings in the factor of unpredictability, especially when the movement of pedestrians is also taken into account [13]. Radar interference further complicates the deployment scenario, as a question arises as to whether an autonomous vehicle can distinguish between the transmitted and reflected signals, considering that thousands of vehicles using this technology have been deployed. Even when a high range of radio frequencies is allocated for this use, it is highly unlikely that they would be sufficient for all manufactured vehicles on the road. An intelligent transport system that incorporates internet of vehicle (IOV) architecture can solve many of these problems [2]. As highlighted by Huawei, this model involves the massive interconnection of vehicles to vehicles, vehicles to pedestrians, vehicles to infrastructure such as toll systems, cameras, and traffic lights, and vehicles to networks. ...