Summary
An autonomous vehicle can perform necessary functions by itself without human support. It can sense the surroundings and uses its automated system to respond depending on the external factors. They come with a unique aspect referred to as the adaptive cruise control that supports the maintenance of a safe speed to enhance safety. These vehicles are usually fitted with sensors that determine the speed and other functions, such as the application of brakes, particularly when approaching an object (Crayton & Meier, 2017). Actuators in the vehicles process the information and send the necessary instructions to ensure a timely response, including acceleration or steering. Moreover, automated vehicles can respond to traffic signals as well as non-vehicular activities. This paper recommends that senior managers and executives consider automated vehicles as part of the company’s portfolio of development projects and internal research in the next budget cycle.
Characteristics of the Technology
The autonomous vehicles’ key features are meant to promote safety, support enhancement, and improve the driving experience. They include Automatic Emergency Braking System (AEBS), Adaptive Cruise Control (ACC), lane control, Object or Collision Avoidance System (CAS), Vehicle-to-Vehicle (V2V) Communication, street sign recognition, and Light Detection and Ranging (LIDAR). Lane control supports the ability to maintain a lane by monitoring distances to adjacent vehicles, road edges, and lane markers (Haboucha et al., 2017). It uses the global positioning system (GPS) to support the pinpointing of locations. ACC is a safety feature that helps maintain a safe distance to limit the risk of collision. Sensors fitted on the body of the vehicles influence the speed depending on the conditions of the road.
AEBS is another feature that promotes safety since it supports automatic stops to avoid collisions. The LIDAR feature support object identification and distance determination to enable safe movement of the vehicle. Road and aerial vehicles are usually fitted with standalone mountable units to enhance distance estimation. Street sign recognition refers to a software program with the ability to identify road signs and process sensor data. This enables autonomous vehicles to obey road signs and other important markings. Vehicle-to-Vehicle (V2V) Communication enables autonomous vehicles to work together to promote safety. It entails the ability to exchange information to explain the position of the surrounding vehicles and speed (Rosique et al., 2019). This helps improve the environment, reduce traffic congestion, and avoid crushes.
The ability of these vehicles to communicate wirelessly could be a breakthrough in the improvement of road safety. The technology makes it possible for vehicles to send and receive Omni-directional messages at a frequency as high as ten times in a second and attain an all-around awareness of the proximity of other vehicles. The message can be interpreted by other vehicles with the software to gain awareness of potential crash threats before they occur (Haboucha et al., 2017). Moreover, the technology can use audible, tactile, and visual alerts to warn the vehicle driving system to respond to a threat. Messages can help detect dangers emerging from unfavorable weather, terrain, and traffic. Features and capabilities of autonomous cars can play a role in the improvement of safety, justifying the expansion of research and development.
Interactions among the Selected Environments, People, Processes, and Technology
The automation of vehicles tends to alter the need for cognitive systems of a driver. With an increasing level of automation, the system tends to replace the active role of the human as the decision-maker. This implies that certain activities such as steering maneuvers are not necessarily fitted in automated vehicles since they are replaced by other skills such as monitoring. There is a need for restructuring and modifying mental models to promote the acceptance and safety of autonomous vehicles to ensure that they align with the human ability to process information as well as conform to needs and expectations (Rasouli & Tsotsos, 2020). Various emotional and cognitive dimensions should be considered during the development of automated vehicles. The reliability of these systems depends on the quality of interactions with humans. Humans will be required to fix errors using technical systems in case of a malfunction or breakdown.
Automation has brought a change in the functions of technical systems altering the capabilities and roles of humans. For instance, in aerial vehicles, autopilots or flight management systems have taken over the roles of the cockpit crew. Pilots are no longer involved in the active manual control, but they are now responsible for the monitoring and programming of the aircraft. The new function has boosted safety while lowering the psychological effect such as reduced activation of attentiveness (Faisal et al., 2019). The elimination of direct control and guidance of humans is associated with loss of cognitive and manual capabilities, excessive or insufficient trust in automation, and challenges in supporting considerable system awareness and situation (Faisal et al., 2019). Autonomous vehicles are likely to find a wide application in the air, marine, and ground operations because they come with many benefits, including reduced accidents, lower transportation costs, and better lane capacity.
Errors made by drivers are the leading causes of crashes, and the introduction of automated vehicles can play a role in eliminating dangerous behaviors. The increasing number of self-driven cars implies that an extensive reduction in traffic deaths is likely to be attained. This would also come with a massive drop in the emission of harmful gases that causes environmental degradation. The traffic congestion problem would also reduce as the number of autonomous vehicles increases (Janai et al., 2020). Elimination of inappropriate behaviors on the road, such as stopping on the road, would help reduce the amount of time that vehicles spend on the road and influence the reduction of emissions while promoting fuel economy.
Vulnerabilities or Risks Linked to the Adoption of Autonomous Vehicles
The technological advancement linked to autonomous vehicles has increased the exposure to potential cyber-attacks. The improved communication between infrastructure and vehicles increases the risk of attack by malicious hackers who could be interested in compromising the system. Autonomous driving and improved connectivity functions present a major threat to the technology. The artificial intelligence (AI) system of these vehicles usually works full-time to estimate their speed, detect other vehicles, recognize road signs, as well as plan their movement exposing them to unintended threats in case of any failure (Haboucha et al., 2017). Attackers can easily disrupt or interfere with the functioning of the system to steal data or cause damage. Criminals can add stickers on the road signs or paint on the road to confuse their systems to execute their malicious attacks. Altering the road signs can affect the ability of the system to recognize objects properly and eventually behave abnormally.
Data protection is paramount in autonomous vehicles because it can cause irreversible reputational damage, and it is necessary to ensure that it is prevented. The management needs to consider and address cybersecurity requirements to enhance the protection of diverse categories of data from damage and theft, particularly information systems, intellectual property, personal information, and sensitive data (Flämig, 2016). This can improve the ability of the company to defend itself, avoid data breaches and hinder cybercriminals. The rising application of cloud services such as Web Services and Amazon is increasing both the residual and inherent risk. This increases the risk of a successful data breach and cyber-attack since cybercriminal is becoming more sophisticated (Faisal et al., 2019). The company must understand that cyber threats can arise from different levels, and it is necessary to educate staff regarding social engineering scams such as ransomware and phishing attacks. The rising cases of cybercrime imply that the organization needs to take issues of cybersecurity more seriously since they present issues that cannot be ignored.
The risks and vulnerability of autonomous vehicles can be linked to the documented history of car hacking. Since these vehicles operate by sending signals and messages, they are prone to cyber-attack since malicious people can interfere with the system and access critical information or influence the vehicle to behave abnormally. This is likely to compromise the automotive component and affect the success of the technology. Cybercriminals could be interested in hijacking the system to cause a crash implying that it can be used as a terrorist weapon. Likely wireless attacks may include misconfigured clients, rogue access point attack programs, Media Access Control (MAC) identity spoof, probing and discovery tools, static Wired Equivalent Privacy (WEP) cracking programs, wardriving, and worshipping.
Benefits and Costs Linked to the Adoption of Autonomous Vehicle Technology
Securing this technology will not be an easy task because of its high vulnerabilities. It will require the organization to invest in a cloud-assisted framework to enhance defense against malware. The solution will offer security functions, including diverse mechanisms to protect malware information databases and help eliminate storage restricted challenges in the vehicle networks (Faisal et al., 2019). It would be important to establish effective measures to counteract attack techniques, including malware infections. Efforts should be made to hinder malware from infecting the cloud-based solutions, as well as ensure that the system remains secure.
Large-layered solutions paying attention to critical areas such as preventive techniques and measures, real-time intrusion detection, and ongoing monitoring of the system will be necessary to avoid attacks. Moreover, the company will need to assess potential solutions by involving a response team and supporting the gathering and analysis of critical information regarding respective attacks (Rojas-Rueda et al., 2020). This means that effective measures need to be taken to ensure effective identification, protection, detection, recovery, as well as response to attacks.
Autonomous vehicles are faced with many cybersecurity challenges linked to artificial intelligence for both intentional and unintentional hardware and software vulnerability. The increasing application of AI technologies has worsened the problem following the malevolent exploitation and threat actors since they tend to expand the attack landscape and introduce new vulnerabilities. The organization needs to be prepared for unintentional harm emerging from malfunctioning, limitations, as well as the inappropriate design of AI models (Meyer et al., 2017). This implies that the cost of securing autonomous vehicles will be high since they are exposed to serious risks. Moreover, the risk of cybersecurity can compromise the safety of pedestrians, passengers, related infrastructure, and other road users. This implies that it would be important to investigate potential vulnerabilities that arise following the usage of AI.
It will be difficult to hinder terrorists and criminals from using this technology since attacks can be executed from diverse perspectives. For instance, the technology is vulnerable to adversarial machine learning techniques, including poisoning or evasion attacks. Unfortunately, the threat model could involve a facial recognition system and spoofing the pattern. Evasion attacks tend to change the output by manipulating the data fed. Poisoning attacks affect the training process to influence a malfunction that gives an advantage to attackers (Schwarting et al., 2018). Moreover, the technology is vulnerable to challenges affecting connection mechanisms, controls, and physical sensors. Some of these challenges include sensor saturation, spoofing, jamming, or blinding. Malicious people can jam or blind sensors to access these vehicles and execute their mission.
Hijackers can manipulate the communication system to influence the wrong interpretation of signs and messages. Since autonomous vehicles are fed with a large amount of data, attacks on them can expose sensitive information to the risk of loss or access to unauthorized people (Schwarting et al., 2018). Safety-critical and resilient systems should be designed while considering the risk based on the attackers’ perspectives. Technologies that depend on machine learning (ML) algorithms are more vulnerable since they are expected to behave in a certain way. Spoofing or engineered manipulation of data can influence unexpected responses from sensors. Coping with these issues will necessitate new tooling and support of the safe application of AI.
Autonomous vehicles present an emerging technology that is likely to bring beneficial changes in the transportation sector. It has the potential to reduce travel time and cost, improve safety and convenience, and eliminate human errors. The senior managers should consider approving and funding its pilot implementations based on their benefits. However, they need to handle challenges associated with its adoption, particularly cybersecurity issues, since it is vulnerable to diverse attacks.
References
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