Autonomous Vehicles Overview

Introduction

The autonomous automobile concept is a novel technology that is being utilized in the production of automobiles. The technology will be installed in cars and light trucks to decrease traffic incidents, energy consumption, and environmental and noise contamination. The approach technology is performed and handled will also alter as a result of technological progress. For instance, the car will be able to operate without the assistance of a human driver. With the advancement of technology, a car will produce its own rational choices and choose the most efficient route to take. Thus, autonomous vehicles will alter the transportation system by navigating without human supervision. Because of a mix of hardware and software that executes the program over the device, the system works well. When the two components are combined, the whole machine will have a collaborative automated functioning.

Research Questions

The study seeks to evaluate the issues surrounding autonomous vehicles guided by the research questions below:

1. What are the constraints experienced within the use of the technology?

   1. Under this aspect, technological use and application are explored, including the availability and compatibility of the technology within the target markets.
2. What are the security concerns surrounding the usage of autonomous vehicles?

   1. Security concerns include the increased cyber fraud activities, which pose a considerable threat to the autonomous given the ill motives of particular individuals. Under this case, the aspect will evaluate the regulatory measures and the risks the security concern poses to users.
3. What are the mitigation factors which should be undertaken to reduce the inefficiencies experienced?

   1. The question seeks to reduce the risks and hazards of the sector majorly caused by the technological aspect. In many cases, safety concerns are the primary barrier to development, and hence actions should be taken in order to address the issue.

Methods

As the autonomous vehicle industry emerged only recently and developed for several years, there may be no sufficient data to evaluate the technology comprehensively. It may be necessary to assess each case individually as the database provides a relatively small sample. Therefore, case study may be one of the most efficient evaluation methods in terms of autonomous vehicle technology. It may also be vital to monitor as much data as possible and introduce specific frameworks in order to analyze it. As the industry is new, significant unpredictability rates and risks may occur. Consequently, it may not be possible to predict technological problems, yet it is critical to collect such data and utilize it in order to evaluate and improve the technology.

Several procedures are performed to validate autonomous vehicle technology. The most straightforward technique is to enable the vehicle to complete the given job at various levels of complexity, increasing its capacity to prevent system mistakes, decision failures, correct routing, and collision prevention. It may be accomplished via a thorough analysis of algorithms. Lists of primary functionality test cases are chosen and organized into various testing procedures, which are subsequently generalized as driving task groupings based on the analysis of autonomous driving capabilities (Fagnant & Kockelman, 2015). Autonomous driving capabilities may be assessed by evaluating particular driving task sets. Independent driving activities are tested in a variety of simulation and real-world settings. All driving tasks are ultimately assessed with various task difficulty properties and diverse environment difficulties using a formal evaluation procedure that includes the testing approach, recordings, assessment, and completion validation. The system conducts multiple activities according to the developers’ and producers’ instructions. That is, the system will run and make choices following the installed software. For example, automated cars can identify and remove traffic jams utilizing GPS and radar data thanks to advanced technology. As a result, the vehicle will seek pathways with the fewest traffic congestion and reach its destination through other fast-moving highways.

Technological Issues in the Autonomous vehicles

Overcoming technical obstacles isn’t enough for self-driving cars to become widespread. Obtaining popular backing is also critical. In order to utilize and purchase autonomous vehicles, individuals must feel secure driving in such vehicles. Even though more than 40% of consumers across the globe would be prepared to either utilize completely autonomous vehicles or semi-autonomous ones, they still have certain crucial reservations (Statista, 2021). Approximately more than half of consumers are concerned regarding autonomous vehicle security. Thus, more than a third are unsure if the technology required to run driverless vehicles is sophisticated enough. Moreover, the technology behind fully autonomous vehicles is very complicated.

The operators of the ad-hoc vehicle network maintain consistency, guaranteeing that the data gathering is safe. They also ensure that wireless connectivity networks are established and stay dependable for the duration of the vehicle’s functioning (Bonnefon, Shariff & Rahwan, 2016). In addition, the technique guarantees that the cars that use the system are examined for speed, giving real-time information to the computing device that is linked. Finally, vehicle ad-hoc connections may serve as a backup in a technical problem or an unforeseen situation.

Cybersecurity and other Security Concerns

All cars that function on public highways are susceptible to a set of restrictions imposed by both the federal and municipal administrations. The National Highway Traffic Safety Administration (NHTSA) of the Department of Transportation (DOT) has issued car safety rules previously, and the government has issued driver’s licenses. The department also has set traffic rules and car insurance requirements for all automobiles (Canis, 2019). Nevertheless, developing appropriate software and algorithms that comply with state laws becomes increasingly challenging for all autonomous car creators. Because the automobile is self-driving, no personal monitoring or management is required throughout its operation. As a result, the system uses collision avoidance features to assist in preventing inevitable foreseeable consequences. Several car crashes, for example, may be prevented since reckless driving errors cause them.

Automated cars are capable of operating in a variety of settings. They might, for instance, adapt military applications as a novel mode of transportation. Some vehicles include a vehicle occupant detection technique and a viper technology that detects individual bodily temperature, allowing various decision-making methods. This factor alone may involve changes in speed depending on the environment and accessibility. Vehicle architects are familiar with the surroundings of autonomous artificial intelligence. As a result, depending on the methods, they will work with structuralized aprioristic understanding.

Cybersecurity Threats

Autonomous vehicles, on the other hand, necessitate more robust protection to protect the mechanism. There should also be secure methods and the ability to choose how to execute them efficiently. As technologies develop, so do cyber-attacks, which grow more complex with time. As a result, it’s critical to minimize all of the dangers and flaws that may jeopardize the technology’s effectiveness throughout discovery. In addition, the procedure will improve the technology’s authenticity when it comes to autonomous car computer networks (Canis, 2019). Finally, autonomous cars may be utilized for malicious purposes such as bodily harm, intimidation, and surveillance because of their capacity to operate in various settings. As a result, it’s critical to make sure the vehicle’s structural framework and layout are built to withstand assaults.

Furthermore, the autonomous automobile software system developer ought to be aware of the recommended techniques for defending against all types of cyber threats. He has to concentrate on making sure that the best activities are implemented and implemented on the automobile. Without appropriate vulnerability analysis techniques, risk analyses, and well-known security measures, disasters may occur. Cyber-attacks on autonomous systems may result in the deletion or compromise of original programming (Canis, 2019). Manipulating the system’s necessary directives may make it harder to feed the program.

Regulation

In any business, there aren’t enough standards and rules to cover up an entire autonomous technology. Current car safety regulations presume the presence of a human driver who can take control in a crisis. Contamination crashes and human fatalities are just a few of the significant transportation drawbacks. In principle, autonomous automobile technology will continue to be seen as containing unique answers to issues. Technological innovation alleviates congestion problems, which wastes time and consumes a lot of gasoline. As a result, one should think about the efficiency of the methods of travel as well as their growing accessibility. As a result, it should be evaluated and compared to its mobility in various settings.

Risks and Vulnerabilities

Since the system is human-made, certain risk assaults develop substantially on autonomous car-linked network systems. Nevertheless, one of the most critical activities and structures for any linked independent vehicle program is to improve security measures. For example, since most machine learning techniques include faulty human efforts, the power to defend the infrastructure becomes critical. The integrating state structure will assist in the mitigation of security risks (Canis, 2019). Then it will provide advice and instructions for accessing the government directives requirements, regulations, and guidelines’ possible consequences. Before driverless cars (AVs) can be extensively used, they must undergo rigorous testing. Aside from routine testing, external and internal security problems such as hijacking, malfunction, and so on may happen at any moment. Furthermore, cybercriminals may attack self-driving cars after they are entirely implemented to gain access to the motorist’s intimate details or, worse, take charge of the vessel’s direction or propulsion.

Mitigation of the Concerns

Cybercriminals become more advanced and complex nearly every day, necessitating safe protocols and procedures in linked networks. The cyber-physical framework may be utilized to make important choices that aid in identifying flaws in the crucial autonomous vehicle (Haboucha, Ishaq & Shiftan, 2017). The cyber-physical infrastructure has the potential to upgrade the effectiveness of integrated system software that detects issues. It also locates the root of the problem in the platform and discloses the flaw, thus reducing the risks. However, changes to the program may harm the structure or disable the secure coding mechanism, causing the whole system to fail. Consequently, establishing necessary and safe frameworks with the capacity to detect vulnerabilities leads to significant infrastructure remedies.

Furthermore, there is a significant danger connected with self-driving cars. That is issues in computation or decision-making that may lead to an accident, damage to property, or redirection. A well-designed and simulated application should be executed in the systems (Kongnso, 2015). The issue may be caused by the system’s use of incompatible operating systems. To solve the problem, autonomous car developers should guarantee that all algorithms are thoroughly tested before being put into service to ensure they are compatible with various vehicle models within the industry. Furthermore, without appropriate monitoring devices, autonomous automobile technologies may culminate in unfortunate occurrences and severe risks.

Limitations or Special Considerations

Enabling the plan to achieve the desired objectives within the autonomous vehicle sector will include setting limitations to the evaluation models implicated. For example, sections such as open roads should not be applicable if other users are present in the area, given the prevalence of the vulnerabilities highlighted within the technology usage, hence the need to use a concealed site where damage may be contained. It may be highly beneficial to introduce testing environments, which simulate realistic conditions to conduct comprehensive testing with minimum risks. In addition, the test should include safety elements such as fire extinguishers and ambulances in case any incident arises. It may be essential to minimize human involvement and utilize existing evaluation methods to meet safety requirements. Finally, real data monitoring through various sensors and video recorders may significantly contribute to the evaluation process. Such methods may provide sufficient data regarding occurring accidents. However, gaining access to such data may considerably interfere with privacy rights. Therefore, legal frameworks and user agreements should be adjusted to address the above-mentioned privacy issues.

Time Frames

The suggested timeline for the test is approximately three months, allowing full navigation of the automobiles through an open environment and traffic area to monitor the pattern and approaches it adopts in the two different settings. Further to this, the test regarding cyberattacks may take longer to effectively adopt apparent effects and recommendations, according to the observation shown below.

Conclusion

Before using any new technology, it is crucial to examine the dangers and other security considerations involved. As a result, it is critical to guarantee that architectural design is protected against exploitation. Since exploitation impacts a company’s purpose and data security, autonomous cars will evolve as the next generation of high-tech transportation in this scenario. As a result, understanding the risks connected with technology is critical to avoiding major technological problems arising from cyberattacks.

References

Bonnefon, J., Shariff, A., & Rahwan, I. (2016). The social dilemma of autonomous vehicles. Science, 352(6293), 1573-1576.

Canis, B. (2019). Issues in autonomous vehicle testing and deployment (No. R45985). Congressional Research Service.

Fagnant, D., & Kockelman, K. (2015). Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations. Transportation Research Part A: Policy and Practice, 77, 167-181.

Haboucha, C., Ishaq, R., & Shiftan, Y. (2017). User preferences regarding autonomous vehicles. Transportation Research Part C: Emerging Technologies, 78, 37-49.

Kongnso, F. J. (2015). Best practices to minimize data security breaches for increased business performance. 

Statista. (2021). Global autonomous car market. Statista.