Self-Driving Technologies
Self-driving technology is the use of artificial intelligence (AI) to automate various aspects of driving, such as steering, controlling speed, and changing lanes. Self-driving vehicles are also known as autonomous cars or driverless cars. This technology has the potential to revolutionize the supply chain management process by allowing for more efficient transportation of goods and materials (Yigitcanlar et al., 2019). Companies such as Tesla, Google, and others have already made serious headway in terms of advancing self-driving technology in the marketplace.
The introduction of self-driving technology into the trucking and transportation industries could drastically reduce costs and improve productivity. Trucks and other forms of transport could be programmed to travel safer, faster, and more efficiently than with human drivers. This would reduce the amount of fuel consumed, thus saving companies significant money on fuel costs. Automated vehicles could also be designed with the latest safety technologies, further reducing the risk of accidents.
The ethical implications of autonomous vehicles are a cause for concern, as the technology raises questions about issues such as liability and safety (Liu et al., 2019). Autonomous vehicles may also put human drivers out of work, leading to a lack of job security and economic disruption. It is essential that governments and companies take measures to ensure that self-driving technology is implemented in a responsible and ethical manner.
Types of self-driving technology include full self-driving (FSD), basic self-driving (BSD), and partial self-driving (PSD). FSD is the most advanced form of self-driving technology and allows vehicles to navigate roads without any human input. BSD enables vehicles to operate autonomously under certain conditions, while PSD requires some level of human input to control the vehicle.
References
Liu, P., Yang, R., & Xu, Z. (2019). Public acceptance of fully automated driving: Effects
of social trust and risk/benefit perceptions.Risk Analysis,39(2), 326-341.
Yigitcanlar, T., Wilson, M., & Kamruzzaman, M. (2019). Disruptive impacts of
automated driving systems on the built environment and land use: An urban planners perspective.Journal of open innovation: Technology, market, and complexity,5(2), 24.
