How Soon Will Self-Driving Trucks take over?
Auto, Aviation & Transportation
For most businesses in the world to thrive, the supply chain plays a very critical role. To ensure a successful supply chain, transportation needs to be streamlined. Even though good roads need to be in place for a streamlined process, good infrastructure cannot tackle several challenges. There needs to be a technological change in the trucking industry to combat the problems. Auto drive or autonomous driving is one of the technologies that need to be implemented so as to ensure this success. Currently, autonomous driving in trucks is in its early stages of development and has a great potential to influence the industry majorly.
The main objects of the thesis are to explore autonomous driving technology in the trucking industry and the benefits that its implementation can bring about. Additionally, to look into the major challenges and limitations associated with implementing this technology into a practical solution. To achieve the set goals, information and data were gathered from studies, research papers, and articles then analyzed thoroughly.
General Introduction
From 1908 when Ford started the mass production of vehicles (Bowden & Lamond, 2015), there have been numerous changes in the characteristics of mass production of vehicles. There have been a lot of changes in the vehicles themselves, and the recent one is in the autonomous vehicle industry. The changes have seen improvement in motor vehicles’ safety, connectivity, and efficiency. Most car manufacturers are trying to develop autonomous technology as fast as possible so that the vehicles can drive themselves in the near future.
Moreover, self-driving vehicles can be defined as those that operate without human intervention in the form of acceleration, steering and braking. The autonomous technology was conceived in the 1920s, but its implementation came in the 2010s officially introduced to the market (Rosenband, 2017). Most vehicle manufacturers have been trying to implement the technology in their vehicles. There are following questions:
- Firstly, what is the efficiency of autonomous vehicles over the traditional trucks in the transport system?
- Secondly, what is the major impact of implementing autonomous driving in trucks in terms of cost and other factors in the transport system?
- Lastly, what are the main consequences of implementing the technology?
Research Problem
At the time of writing this research, the laws and regulations of both EU and the USA were considered. There are several challenges in the transport system, including strict working and driving limits for the drivers set by the laws and regulations. The laws help improve the safety of the drivers and other road users, eliminate unfair competition and improve traffic safety. Drivers are majorly limited to 9 to 11 hours of work per day which is done twice a week.
Additionally, they have to take a minimum of 11 hours of rest every day (LeMay & Keller, 2019). From the data above, we see the maximum driving time allowed; thus, they can only work for up to 55 to 70 per cent of their work time, considering additional factors like loading and unloading of the goods. During peak seasons, it becomes difficult for the firms to deliver all the goods in the required time. This is a major problem in the transportation industry; thus, there is a need for an innovative solution to be implemented. Haring extra drivers may not help solve the problem; hence automation is needed.
The other problem is errors caused by drivers leading to accidents on the roads (Sullman et al., 2017). Safety is a vital concern. Thus, laws are put in place to be followed to the letter to avoid accidents that may cause significant losses.
Definition and Explanation of Key Terminology
3D- Three dimension
AV- Autonomous Vehicle
Lidar- Light Detection and Ranging
Radar- Radio detection and Ranging
GPS – Global Positioning System
Hypothesis (Theory)
Brief Overview of Theoretical Foundations Utilized in the Research Study
The research was purely qualitative, meaning data was collected from several publications, academic documents. Additionally, with in-depth analysis, empirical data was used to explain the benefits and theories of autonomous vehicles. The theoretical hypothesis was presented and analyzed to come to a conclusion that helped in the discussion.
Brief Overview of Literature Reviewed, Discussed and Applied
Data were mainly collected through reading journals, publications videos, and academic papers on the topic. Legal and regulatory documents were also looked into to provide appropriate data for analysis. The research is thus considered a synthesis and appraisal of primary research papers using a clear methodology in both selection of studies and search strategy. Available literature was also analyzed so as to answer the questions above.
Methods
This section mainly deals with the methods used to conduct the study and how they used to answer the research questions. The methods are presented below with little theoretical background of the methods.
Study Method and Study Design
The research involved the use of a literature study which was used to get some knowledge that was vital for an empirical study. Similar projects were looked up in this phase of the study since few found studies on the effects of autonomous technology in the transport industry. This motivated an empirical approach to the research.
Explanation of Sample to Be Used in the study
The samples to be used for this thesis were data found in academic papers published before. Additionally, some data was gathered from government databases which are updated which the current information on the topic at hand. The sample data was well distributed so as to eliminate bias in the research. Only data from reputable sources was used to eliminate the possibility of sampling errors that may be passed down from external sources.
Description and Justification of Analytical Techniques Applied
Data was used to create models and analyzed to extract insights that were used to support the discussion and, consequently, the conclusion of this research. The technique used for the analysis of the data was purely qualitative. The method used was a descriptive analysis (Kemp et al., 2018) by manipulating and interpreting raw data from the sources to help give a valuable insight into the research questions. The method needed to be used since it helps present the raw data in a meaningful manner.
Assumptions and Implied Limitations of Study Method and Design
Several assumptions were considered when conducting the research. First, the data is treated as accurate from the sources, which is an assumption made in this thesis. Additionally, the viability of external sources to collect and analyze the data was also an assumption. There was also a limitation to access a practical solution of the technology application; thus, published data were used. The data obtained is available on the internet and only focuses on the European Union and the United States of America; thus, other parts of the globe was not considered.
Findings
These sections will deal with the findings from the data analysis obtained above.
Brief Overview of Research
The thesis aimed to identify how the implementation of autonomous technology can help to improve transportation systems in terms of logistics. The research is limited to the transport of goods in the European Union and the United States of America. This is due to the set infrastructure which can adequately support the implementation of the technology in the trucking industry (Fritschy & Spinler, 2019). The main questions set to be answered were:
- Firstly, what is the efficiency of autonomous vehicles over the traditional trucks in the transport system?
- Secondly, what is the major impact of implementing autonomous driving in trucks in terms of cost and other factors in the transport system?
- Lastly, what are the main consequences of implementing the technology?
Results of the Method of Study and Any Unplanned or Unexpected Situations that Occurred
This subchapter will present the results found on the implementation of autonomous driving in the trucking industry. The driverless technology allows the vehicle to plan its route using satellite technology through GPS (Höyhtyä & Martio, 2020). Data on the terrain and road types are also available for the truck’s system to make the most suitable navigational decision. Additionally, an autonomous vehicle can figure out the variables of its environment in the situation analysis. Furthermore, this is done using different sensors such as LIDAR (Azizi & Tarshizi, 2016), radar and cameras. As a result, the vehicle can also maintain driving stability when navigating bends during speed changes by using a motion planning system. However, if the systems detect any anomaly, it uses acceleration, braking or steering to return to stability.
There are several challenges associated with the trucking industry. To start with, there is a driver shortage. According to the American Trucking Association, from 2017 to 2026, the industry expects to get 900,000 more drivers, which average to 89,750 persons per year to keep up with the demand, and in 2005, there was a shortage of 20,000 drivers, but the industry recovered during the 2008 recession due to low transportation volume (The Voice of America’s Trucking Industry, n.d.). .
The other problem is there are many traffic accidents that involve trucks. According to the National Highway Traffic Safety Administration, most traffic accidents were related to the driver that there are over 2,189,000 crashes that happened all over the country (NATIONAL HIGHWAY TRAFFIC SAFETY ADMINISTRATION, 2018).
Most sources have indicated that reasons why most of these crashes are associated with drivers are:
- Drivers internal and external distractions from the road.
- Nonperformance errors like sleep
- Performance errors like poor control.
- Poor Decision
Out of the above reasons, Recognition errors comes first with $1%, that is 845,00 cases, followed by poor decision-making errors at 33% and Performance errors at 11% having 684,000 and 210,00 cases, respectively (NATIONAL HIGHWAY TRAFFIC SAFETY ADMINISTRATION, 2018).
Brief Descriptive Analysis
From the data above, it can be seen that there are problems in the trucking industry that can be overcome using technology. First is the limited time which drivers can take on the road. Drivers are limited to around 11 hours per day of work, limiting their movements. Additionally, there is an increase in the rates of a driver shortage, and it doesn’t seem to be slowing. Lastly, the rate of accidents caused by driver errors is very high, costing the industry billions of dollars in losses.
Reliability and Validity of the Analysis
The data analysis above can be used confidently since the raw data was taken from reputable sources, often accurate. The validity of the source data was checked using other sources by cross-referencing to check if it matches, which raises its reliability for this research.
Explanation of the Hypothesis and Precise and Exact Data
There are several challenges associated with the trucking industry. To start with, there is a driver shortage. According to the American Trucking Association, from 2017 to 2026, the industry expects to get 900,000 more drivers, which average to 89,750 persons per year to keep up with the demand, in addition, in 2005, there was a shortage of 20,000 drivers, but the industry recovered during the 2008 recession due to low transportation volume, but from 2011, the shortage trend has shot up, increasing every year, as shown below (The Voice of America’s Trucking Industry, n.d.):
The other problem is the rate of accidents that involve trucks.
According to the National Highway Traffic Safety Administration, there are 94% of the reasons for traffic accidents were related to the driver. A sample of 5,470 crashes was used to conclude, which represented over 2,189,000 crashes that happened all over the country, as shown below (NATIONAL HIGHWAY TRAFFIC SAFETY ADMINISTRATION, 2018):
| Reason | Value in thousands | % |
| Drivers | 2,046 | 94 |
| Vehicles | 44 | 2 |
| Unknown | 52 | 2 |
| Environmental Factors | 47 | 2 |
| Total | 2,189 | 100 |
Discussion
Brief Overview of Material
The research covered the proposed questions above successfully. Data collected from several sources were analyzed and the results tabulated as shown above. The findings gave valuable insight to help answer the questions posed for this research. Data was obtained from journals, government databases, academic papers, videos and articles related to the subject.
Full Discussion of Findings (Results) and Implications
From the analysis above, the potential of autonomous technology implementation to improve efficiency in the trucking industry is significant. If the technology is implemented, a driver can virtually increase productivity by 50%. There will also be increased road safety thanks to modern technology, which has been proven to work in cars. Many studies have shown that there will be even more improvement in road safety when the technology is rolled out since computers process data way faster than a hum being. However, the cost of operations is uncertain due to the low testing rates of the technology on the trucks. Studies have shown generally that implementation of autonomous technology can save fuel cost but have not been conclusively defined. In this uncertainty, it’s difficult to predict if autonomous technology will lower the operation cost.
How Soon Will Self-Driving Trucks take over?
This research has also found out that several challenges need to be overcome first before implementing a practical solution of autonomous driving in trucks. It has not undergone enough testing to conclude on its safety and efficiency thus cannot be reliable without human intervention in one way or the other.
Full Discussion of Research Analysis of Findings
An autonomous truck should know its surroundings, balance itself, and choose the optimum route for the trip. The vehicle does the functions above by using several sensors, processing the data received from the sensor, and deciding the best course of action. The control using and the sensors that are required to perform the above functions include
- Radar- Radar means “Radio Detecting and Ranging”. It uses radio waves and their reflections to estimate the location of an object based on speed and angle. It is merely after environmental conditions (Ptak & Konarzewski, 2015).
- Lidar- Light Detection and Ranging. It is similar to radar in terms of functionality but uses laser pulses to locate an object. The technology’s accuracy is higher than radar since the laser bounces of objects millions of times a second to generate a 3D model. Unfortunately, it has a short detection range and is extremely expensive (Lu et al., 2020).
- Cameras are practically the ‘eyes’ of an autonomous vehicle providing vision. It helps to detect traffic lights and street sights. The major disadvantage is that they cannot detect non-illuminating objects and are heavily affected by environmental conditions (Varga et al., 2015).
Full Discussion of Hypothesis and Findings
At the beginning of the research, three questions were posed to be answered. The first is on the efficiency of autonomous trucks over traditional ones. The research has concluded that autonomous trucks promise more efficiency by using technology, thus increasing productivity. The cost of running an autonomous truck is not well documented, and fewer tests have been done on it. Furthermore, the impact on the overall cost is not conclusively known, thus requiring more tests and research on the sector. The research has concluded that implementing such a solution would be positive as it would increase drivers’ productivity up to 50%. Additionally, there is an increase in safety on the road since most vehicles are self-aware.
Conclusion
Summary of Academic Study
The research study has concluded that more research needs to be done on the cost of implementing and running an autonomous truck. Additionally, several advantages such as increased productivity and improved safety are also associated with its implementation.
Implications of Academic Study
The study will help future research on autonomous driving in the transport industry. The findings can be used to make an informed decision for the companies that want to implement the technology in trucks. The study will also help provide more knowledge on this sector that has not been widely and significantly researched.
Limitations of the Theory or Method of Research
The research was limited since it used only theoretical data and could not get actual real-time data for analysis. However, the data used in the research is valid, and the recommendations are valuable.
Recommendations or Suggestions of Future Academic Study
In conclusion, this research was done when little had been done on the autonomous space; thus, data availability was limited. Autonomous cars have been tested for quite some time, but there is limited research on trucks. It might be because it is a new area subject in the industry. In addition, the research has not stated the operating cost of an autonomous truck because there is no commercialized version of such a vehicle that may have provided the needed data. Furthermore, the advantages associated with an autonomous truck have been based on data from autonomous cars, which is relevant, but there is room for some difference. Lastly, the research can be a guide to help develop autonomous trucks in the future. Consumers, manufacturers and infrastructure managers can use it for public roads. Moreover, it can educate the public on matters related to this technology.
How Soon Will Self-Driving Trucks take over? Written by Yinzheng Wang
References for How Soon Will Self-Driving Trucks take over?
Azizi, M., & Tarshizi, E. (2016, October). Autonomous control and navigation of a lab-scale underground mining haul truck using LiDAR sensor and triangulation-feasibility study. In 2016 IEEE Industry Applications Society Annual Meeting (pp. 1-6).
Bowden, B., & Lamond, D. (Eds.). (2015). Management History: Its Global Past & Present. IAP.
Fritschy, C., & Spinler, S. (2019). The impact of autonomous trucks on business models in the automotive and logistics industry–a Delphi-based scenario study. Technological Forecasting and Social Change, 148, 119736.
Höyhtyä, M., & Martio, J. (2020). Integrated Satellite–Terrestrial Connectivity for Autonomous Ships: Survey and Future Research Directions. Remote Sensing, 12(15), 2507.
Kemp, S. E., Ng, M., Hollowood, T., & Hort, J. (2018). Introduction to descriptive analysis. Descriptive analysis in sensory evaluation, 1.
How Soon Will Self-Driving Trucks take over?
LeMay, S., & Keller, S. B. (2019). Fifty years inside the minds of truck drivers. International Journal of Physical Distribution & Logistics Management, 49(6), 626–643.
Lu, X., Ai, Y., & Tian, B. (2020). Real-Time Mine Road Boundary Detection and Tracking for Autonomous Truck. Sensors, 20(4), 1121.
NATIONAL HIGHWAY TRAFFIC SAFETY ADMINISTRATION. (2018). NHTSA. NHTSA. https://www.nhtsa.gov/
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Rosenband, D. L. (2017). Inside Waymo’s self-driving car: My favorite transistors. 2017 Symposium on VLSI Circuits (pp. C20-C22).
How Soon Will Self-Driving Trucks take over?
Sullman, M. J., Stephens, A. N., & Pajo, K. (2017). Transport company safety climate—the impact on truck driver behavior and crash involvement. Traffic injury prevention, 18(3), 306-311.
The Voice of America’s Trucking Industry. (n.d.). Www.trucking.org https://www.trucking.org/
Varga, R., Costea, A., & Nedevschi, S. (2015, September 1). Improved autonomous load handling with stereo cameras. In 2015 IEEE International Conference on Intelligent Computer Communication and Processing (ICCP) (pp. 251-256).
How Soon Will Self-Driving Trucks take over?
How Soon Will Self-Driving Trucks take over?
