1. Introduction
With the development of technology, more and more engineers are making changes to the driving systems of cars. The autonomous driving technology is one of the future development directions. The autonomous driving is meaningful and helpful for drivers to avoid most traffic accidents and navigation problems. Therefore, the perfection of autonomous driving technology depends on the modeling and data collection, these procedures and technology advantages and will automatically change the car driving the trend. The advent of autonomous driving technology, a revolutionary innovation in the automotive industry, is reshaping the way people perceive and experience transportation. This technology, which leverages advanced engineering, artificial intelligence, and extensive data analysis, enables vehicles to navigate and operate without human intervention [1]. The development of autonomous driving technology is a testament to the rapid progress of technological advancements, marking a significant leap from traditional manual driving to a future where cars drive themselves. The benefits of this technology are manifold. It promises to drastically reduce traffic accidents, a majority of which are caused by human error, thereby enhancing road safety. Furthermore, it can optimize traffic flow and mitigate congestion by enabling vehicles to communicate and coordinate their movements seamlessly. This technology also holds the potential to revolutionize mobility for those unable to drive, such as the elderly or disabled, provides them with newfound independence. However, the journey towards fully autonomous driving is not without its challenges, including handling unpredictable driving situations and addressing legal and ethical issues. Despite these hurdles, the future development of autonomous driving technology is poised to transform the transportation landscape, making it a focal point in the evolution of smart cities and sustainable living.
This paper mainly discusses the current development of autonomous driving and challenges in its developing, such as the input and application architecture of autonomous driving, in many car companies and research LABS that have planned to study autonomous driving. As people stand on the cusp of this exciting new era, it is imperative to continue refining and perfecting this technology, paving the way for a safer, more efficient, and more inclusive transportation system.
2. Current States of Autonomous Driving
The current state of autonomous driving technology is at an exciting stage of development. According to a report by Allied Market Research, the global autonomous vehicle market is expected to reach a value of $556.67 billion by 2026, with a compound annual growth rate (CAGR) of 39.47% from 2019 to 2026 [2]. This significant growth can be attributed to the rising demand for advanced safety features and the need to reduce road accidents caused by human error.
Companies such as Tesla, Waymo, and General Motors are at the forefront of the autonomous driving revolution. As of 2020, Tesla’s Autopilot system has been installed in over 1.2 million vehicles, accumulating data from billions of miles of real-world driving. Waymo, a subsidiary of Alphabet Inc., has logged over 20 million miles in autonomous mode during its testing phase [3]. General Motors’ Cruise, an autonomous vehicle startup, has partnered with major companies like Honda and SoftBank to accelerate the development and deployment of autonomous vehicles.
The advancements in artificial intelligence (AI) and deep learning have played a crucial role in enhancing the capabilities of autonomous driving systems. These technologies enable vehicles to process vast amounts of data in real-time, making informed decisions and accurately perceiving their surroundings. Concurrently, the integration of advanced sensors, including LiDAR, radar, and cameras, provides vehicles with a comprehensive view of their environment, essential for making safe and reliable driving decisions [2].
However, there are challenges that need to be overcome for widespread adoption. Regulatory frameworks and policies governing autonomous driving need to be established and updated to ensure safety, liability, and ethical considerations. Data privacy and cybersecurity are also significant concerns that must be addressed to protect sensitive information and prevent unauthorized access or malicious attacks on autonomous vehicles [4].
Despite these challenges, autonomous driving technology continues to show immense promise. With each passing year, the capabilities of autonomous vehicles are multiplying, bringing us closer to a future of safer, more efficient transportation [5]. Additionally, the potential benefits are significant – autonomous driving has the potential to reduce traffic congestion, decrease carbon emissions, and improve accessibility for individuals who are unable to drive themselves [6].
In summary, the current state of autonomous driving is characterized by rapid advancement and immense potential. The market is projected to grow substantially in the coming years, driven by the demand for enhanced safety features and a desire to reduce accidents caused by human error. With the integration of AI, deep learning, and advanced sensor technologies, autonomous vehicles are becoming more capable and reliable. However, there are regulatory, privacy, and cybersecurity challenges that need to be addressed for widespread adoption. Despite these obstacles, the future of autonomous driving is promising, holding the potential to revolutionize transportation and reshape our daily lives.
3. Challenges and future development of Autonomous Driving
3.1. Problems and challenges in the developing
Autonomous driving is essentially a combination of artificial intelligence technology and traditional transportation, is the application of computer technology in the field of transportation, according to which autonomous driving can be divided into vehicle part and computer part, and in order to realize the necessary information perception of autonomous driving, sensors are also added. At present, the endurance of electric vehicles is still a soft spot, and now the endurance of electric vehicles is very high on the surface, but if you consider the driving mode, air conditioning, road conditions and climate and other factors, its endurance will be greatly reduced. Autonomous cars are equipped with a large number of sensors to compensate and verify each other. Audi used car cameras, millimeter-wave radar, Lidar, ultrasonic radar and other equipment. Now it mainly relies on Lidar in China, but the price of tens of thousands of dollars leads to the current impossible to achieve the mass production of low - and medium-grade vehicles.
Autonomous driving represents a technological breakthrough in transportation, promising improved road safety, increased mobility, and reduced congestion. However, several challenges need to be addressed for its successful development and widespread adoption. One of the primary challenges is the complex nature of the autonomous driving system itself. Developing algorithms and technologies that can accurately perceive and interpret the environment, make real-time decisions, and adapt to unpredictable situations is a complex task. Machine learning and artificial intelligence play a crucial role in improving the capabilities of autonomous vehicles, but further advancements are necessary to ensure safe and reliable autonomous driving in various scenarios [7].
Another significant challenge is the regulatory environment surrounding autonomous driving. As autonomous vehicles become more prevalent on the roads, policymakers and regulatory bodies need to establish comprehensive guidelines to ensure safety, liability, and ethical considerations are met. These regulations should address issues such as the legal framework for autonomous vehicles, liability in case of accidents, data privacy, cybersecurity, and insurance requirements. The development of harmonized international standards is also crucial to enable the interoperability of autonomous driving systems across different regions [8].
Additionally, public acceptance and trust in autonomous vehicles pose a challenge to their future development. Many individuals still have concerns about the safety and reliability of autonomous driving technology. Accidents involving autonomous vehicles during the testing phase have attracted significant media attention, leading to apprehension among potential users [9]. To overcome this challenge, extensive testing and validation processes are necessary to demonstrate the safety and reliability of autonomous driving technology. Public awareness campaigns that highlight the potential benefits and address common concerns can also contribute to building trust and acceptance [9].
3.2. Future prospect of autonomous driving
Looking ahead, the future development of autonomous driving holds significant potential. Advancements in connectivity, sensor technology, and artificial intelligence are expected to enhance the capabilities of autonomous vehicles further. The integration of V2X (vehicle-to-everything) communication systems will enable vehicles to communicate with each other and with infrastructure, improving safety and efficiency. Moreover, the use of advanced sensors, such as Lidar, radar, and cameras, will enhance the perception and object detection capabilities of autonomous vehicles [7].
In a word, although autonomous driving faces challenges related to technology, regulation, and public acceptance, its future development looks promising. Continued investments in research and development, collaboration between technology companies and automotive manufacturers, and the establishment of clear regulatory frameworks are essential for the progress of autonomous driving technology [10]. With concerted efforts, autonomous driving has the potential to revolutionize transportation, making it safer, more efficient, and more accessible for all [10].
4. Conclusion
Autonomous driving is an exceptionally promising and thrilling technological breakthrough that holds the potential to completely revolutionize the travel methods. The possibilities it presents, such as heightened road safety, reduction of traffic congestion, and increased fuel efficiency, are undeniably significant. However, prior to achieving widespread acceptance of this revolutionary technology, an array of obstacles must be overcome. One of the foremost challenges is ensuring the safety of autonomous vehicles in complex and unpredictable real-world scenarios. While AI-powered systems have made impressive strides in recognizing and reacting to various situations, there is still a need to refine their decision-making processes to handle exceptional cases and edge conditions effectively. Additionally, regulatory frameworks and legal considerations demand thorough examination to address liability and responsibility issues in case of accidents involving autonomous vehicles.
As the pursuit of research and development persists, it can be anticipated that remarkable progress toward the advancement of autonomous driving, propel people into a future marked by a more sustainable, convenient, and efficient transportation framework. The fusion of machine learning, sensor technology, and connectivity will be instrumental in creating vehicles that can communicate with each other and with traffic infrastructure, further enhancing traffic flow and safety. Consequently, the ongoing enhancement and fine-tuning of autonomous driving systems are of the utmost importance, guaranteeing their dependability and smooth integration into our day-to-day lives. Regular software updates and iterative improvements based on real-world data will be crucial to address emerging challenges and accommodate evolving user needs.
By effectively tackling and surpassing these challenges, people can lay the foundation for a transportation landscape that is not only technologically sophisticated but also environmentally mindful and economically feasible. As it moves forward, collaboration among technology companies, automakers, policymakers, and society at large will play a pivotal role in shaping the future of autonomous driving and redefining the way we perceive mobility.
References
[1]. The technology, NHTSA presents Automated Driving Systems: A Vision for Safety. Section 1: Voluntary Guidance for Automated. https://www.nhtsa.gov/sites/nhtsa.gov/files/documents/13069aads2.0_090617_v9a_tag.pdf.
[2]. E. Yurtsever, J. Lambert, A. Carballo and K. Takeda, A Survey of Autonomous Driving: Common Practices and Emerging Technologies, in IEEE Access, vol. 8, pp. 58443-58469, 2020, doi: 10.1109/ACCESS.2020.2983149.
[3]. J. Levinson et al., Towards fully autonomous driving: Systems and algorithms, 2011 IEEE Intelligent Vehicles Symposium (IV), Baden-Baden, Germany, 2011, pp. 163-168, doi: 10.1109/IVS.2011.5940562.
[4]. I. Yaqoob, L. U. Khan, S. M. A. Kazmi, M. Imran, N. Guizani and C. S. Hong, Autonomous Driving Cars in Smart Cities: Recent Advances, Requirements, and Challenges, in IEEE Network, vol. 34, no. 1, pp. 174-181, January/February 2020, doi: 10.1109/MNET.2019.1900120.
[5]. Zablocki, É., Ben-Younes, H., Pérez, P. et al. Explainability of Deep Vision-Based Autonomous Driving Systems: Review and Challenges. Int J Comput Vis 130, 2425–2452 (2022). https://doi.org/10.1007/s11263-022-01657-x.
[6]. S. Teng et al., Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives, in IEEE Transactions on Intelligent Vehicles, vol. 8, no. 6, pp. 3692-3711, June 2023, doi: 10.1109/TIV.2023.3274536.
[7]. L. Liu et al., Computing Systems for Autonomous Driving: State of the Art and Challenges, in IEEE Internet of Things Journal, vol. 8, no. 8, pp. 6469-6486, 15 April15, 2021, doi: 10.1109/JIOT.2020.3043716.
[8]. K. Muhammad, A. Ullah, J. Lloret, J. D. Ser and V. H. C. de Albuquerque, Deep Learning for Safe Autonomous Driving: Current Challenges and Future Directions, in IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 7, pp. 4316-4336, July 2021, doi: 10.1109/TITS.2020.3032227.
[9]. Othman, K. Public acceptance and perception of autonomous vehicles: a comprehensive review. AI Ethics 1, 355–387 (2021). https://doi.org/10.1007/s43681-021-00041-8.
[10]. Bagloee, S.A., Tavana, M., Asadi, M. et al. Autonomous vehicles: challenges, opportunities, and future implications for transportation policies. J. Mod. Transport. 24, 284–303 (2016). https://doi.org/10.1007/s40534-016-0117-3.
Cite this article
Yuan,K. (2024). Analysis of the current development and future prospect of autonomous driving. Applied and Computational Engineering,45,147-151.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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References
[1]. The technology, NHTSA presents Automated Driving Systems: A Vision for Safety. Section 1: Voluntary Guidance for Automated. https://www.nhtsa.gov/sites/nhtsa.gov/files/documents/13069aads2.0_090617_v9a_tag.pdf.
[2]. E. Yurtsever, J. Lambert, A. Carballo and K. Takeda, A Survey of Autonomous Driving: Common Practices and Emerging Technologies, in IEEE Access, vol. 8, pp. 58443-58469, 2020, doi: 10.1109/ACCESS.2020.2983149.
[3]. J. Levinson et al., Towards fully autonomous driving: Systems and algorithms, 2011 IEEE Intelligent Vehicles Symposium (IV), Baden-Baden, Germany, 2011, pp. 163-168, doi: 10.1109/IVS.2011.5940562.
[4]. I. Yaqoob, L. U. Khan, S. M. A. Kazmi, M. Imran, N. Guizani and C. S. Hong, Autonomous Driving Cars in Smart Cities: Recent Advances, Requirements, and Challenges, in IEEE Network, vol. 34, no. 1, pp. 174-181, January/February 2020, doi: 10.1109/MNET.2019.1900120.
[5]. Zablocki, É., Ben-Younes, H., Pérez, P. et al. Explainability of Deep Vision-Based Autonomous Driving Systems: Review and Challenges. Int J Comput Vis 130, 2425–2452 (2022). https://doi.org/10.1007/s11263-022-01657-x.
[6]. S. Teng et al., Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives, in IEEE Transactions on Intelligent Vehicles, vol. 8, no. 6, pp. 3692-3711, June 2023, doi: 10.1109/TIV.2023.3274536.
[7]. L. Liu et al., Computing Systems for Autonomous Driving: State of the Art and Challenges, in IEEE Internet of Things Journal, vol. 8, no. 8, pp. 6469-6486, 15 April15, 2021, doi: 10.1109/JIOT.2020.3043716.
[8]. K. Muhammad, A. Ullah, J. Lloret, J. D. Ser and V. H. C. de Albuquerque, Deep Learning for Safe Autonomous Driving: Current Challenges and Future Directions, in IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 7, pp. 4316-4336, July 2021, doi: 10.1109/TITS.2020.3032227.
[9]. Othman, K. Public acceptance and perception of autonomous vehicles: a comprehensive review. AI Ethics 1, 355–387 (2021). https://doi.org/10.1007/s43681-021-00041-8.
[10]. Bagloee, S.A., Tavana, M., Asadi, M. et al. Autonomous vehicles: challenges, opportunities, and future implications for transportation policies. J. Mod. Transport. 24, 284–303 (2016). https://doi.org/10.1007/s40534-016-0117-3.