Market domain overview.

Market Domain Overview: Driverless Transportation-for-Hire in UberStudent’s Name
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Market Domain Overview: Driverless Transportation-for-Hire in UberWhile Uber’s driverless transportation-for-hire as a market domain is predicted to be commercially available in the early 2020s, they will initially be costly, mounting thousands of dollars in costs per annum for additional machinery, maintenance, equipment, and advanced mapping services (Thekdi & Joshi, 2016). The operational constraints of these vehicles will result in access anxiety, i.e., fear of not able to reach expected endpoints. Given the encounter with past car technologies like automatic airbags and transmissions, attaining affordable and reliable driverless transportation-for-hire business will possibly take up to 30 years. Several factors, both inhibiting and enabling factors can contribute to the dissolution or obsolescence of notable companies in the driverless transportation-for-hire business. Nevertheless, these factors can offer an opportunity for change and innovation to advance in this market domain.
Inhibiting Factors
The first factor that can contribute to the obsolescence of companies in this market domain is the technical element. Many technical concerns should be handled before driverless vehicle transportation for hire hit the roads and markets. One of the issues is safety. Driverless vehicles can misread harmless pools of water as dips and reduce speed unnecessarily. Similarly, most of the self-driving vehicles are baffled by bad weather.

Heavy rainfall can affect its sensors, and snow makes it challenging for cars to read road lanes. Errors made by Google Maps can also confuse the cars. Companies can become obsolete because the self-driving cars have minor technological dilemmas that could pose threats when these cars are in motion. These vehicles cannot see, predict, and interpret the actions of human cyclist, drivers, and pedestrians, and cannot even communicate with them (Fagnant & Kockelman, 2015). The situations like when there is a need to give a whizzing ambulance room cannot be handled by the self-driving cars, posing threats to the lives of those using the road. It can be said that the challenge seen when doing the self-driving transportation-for-hire is that there is lack of complex social contact which these cars, like robots, cannot perform.
Further, the cost of executing the business is another inhibiting factor. The self-driving cars require three types of functions at minimum. These include the lidar, which is used for a clear 3-D viewing, radar, which senses objects and their speed at far places, and cameras for detail and color. The lidar is particularly expensive as one setup for one vehicle can be $75,000 (Bagloee, Tavana, Asadi & Oliver, 2016). What is more, over the last three years, traditional car manufacturers and tech firms have intensely expedited the advancement of sensors, software, and artificial intelligence, which are components required make vehicles more on their own. The amount of capital and intelligence being directed towards undertaking these concerns is overwhelming. For instance, Google has a small group of computer scientists and engineers operating on its self-driving vehicle and has also established an enormous physical experimentation presence for experimenting driverless cars in Atwater’s former U.S. Air Force base. Uber also outsourced at least 40 of Carnegie Mellon’s best roboticists, placing them to operate on self-driving cars. Again, communications titans and chip producers are also playing in the field (Fagnant & Kockelman, 2015). Nvidia and Qualcomm displayed massive research investments to offer mobile computing programs for driverless cars. Similarly, while creating a system that can be produced and executed at scale efficiently is possible, the maintenance of the hardware is a challenge.
Subsequently, another inhibiting factor is that the transportation department may need to structure rules before it understands the level of safety that driverless vehicles have. This is a political obstacle as opposed to technical. Before driverless vehicles can hit the market or roads, policymakers will have to authorize them for application. They will need to question the safety of these cars, and the answer to this question is not yet known. The US drivers are involved in deadly accidents at a rate of nearly one in every 150 million kilometers driven. Google’s self-driving cars only covered a distance of about 3 million kilometers from 2009 to 2015, a distance which is not nearly sufficient to make rigorous statistical inferences about safety (Bagloee et al. 2016). It would take several decades to reach the hundreds of millions of kilometers required to justify safety.
The policy as an inhibiting factor comes with a plethora of implications. The firms aiming at capturing part of this emerging market should understand that self-driving vehicles, unlike the Internet, are joining a heavily controlled sector. The complexity of the transport industry is proved by the NHTSA’s reaction to Google, showing the difficulties of entering the automotive sphere (Fagnant & Kockelman, 2015). Only the government can offer the regulations of the road, and this process can eliminate the whole industry from the business in several ways. The policymakers might pause while struggling with unfamiliar and new questions about the operations of the self-driving cars. Also, well-meant, but badly implemented regulations could act as a stalemate to the advancement. For example, California’s Motor Vehicles Departments recently published regulations on driverless cars that infuriated many within the innovation space by calling for a special license to run the self-driving cars and also that vehicles keep a steering wheel.
Enabling Factors and How Uber can benefit from Driverless Transportation
The consumer acceptance is also viewed as an enabling factor. Several consumers have speculated whether individuals will have to trust the driverless vehicles with regards to their safety. It appears increasingly transparent that consumers will react positively to the emerging business and they will be ready to pay for it (Fagnant & Kockelman, 2015). Consumers often cite factors, such as offering more safety than human drivers, new technology attraction, the possibility of being productive or comfortable while in the vehicle, and absence of driving stress. Another enabling factor is that the emerging business can eliminate car ownership and sustain the incidence of car hire. Driverless vehicles eliminate the need to pay for the drivers’ talent and time. They are, therefore considered cheaper to hire and may ultimately discourage car ownership and capture its money spent by people on buying personal cars. The notion of driverless car transportation for hire is comparable to car-sharing that is seen as a flourishing business model. The hiring business model using the driverless car can foster ride and car haring schedules because they can serve multiple people on demand (Bagloee et al. 2016). The emerging business model is also relatively cheaper than the car-sharing or ownership. For example, it discards cost elements like parking and annual maintenance and fixed costs, and offer more convenience. The rate of reduction in car ownership from 2.1 to 1.2 cars in every household implies that people will be willing to go for the driverless car transportation for hire, thereby boosting the market for this business model (Fagnant & Kockelman, 2015).
On the other hand, Uber can benefit from the driverless transport as the business model will save space and improve the operational efficiency associated with the charges on parking slots. The cars do not need to be parked with a lot of space remaining between each car for the driver to move the vehicle later. It will be possible to save on the parking costs because of the much reduced (15% less) space required by these cars (Bagloee et al. 2016). The business efficiency will also be achieved because the vehicles consumer less fuel and have enhanced traffic flow. The cars are associated with less road congestion and road accidents due to human mistakes. The comfort and convenience brought by the vehicles to passengers can attract a massive pool of customers, which add to the level of profitability and revenues to Uber.
Impacts of the Factors on Opportunities for Change and Innovation
The technological concerns associated with the driverless car transport for hire has led companies like Uber and University of Arizona to collaborate and form a partnership to foster self-driving cars. The partnership has aimed at creating more sophisticated optical safety and mapping technology. In a bid to drive self-driving car attempts, Uber acquired both mapping assets and startup deCarta from Microsoft. The factors like safety and policies related to driverless vehicles also have impacts on new ways of enhancing safety and environmental protection (Bagloee et al. 2016). In this light, federal oversight organizations are concentrating on driverless car safety. The U.S. Environmental Protection Agency (EPA) and NHTSA need to review methods by which driverless car application can lessen greenhouse gas emission. Some of the innovated ways of increasing fuel efficiency to lessen pollution include platooning, elevating driving efficiency, reducing vehicle weight, and alleviating congestion. Elevating driving efficiency, for instance, can reduce emissions up to 60 percent (Thekdi & Joshi, 2016). The cars can be controlled to lessen heavy-footed driving. In sum, regulations to improve driverless cars application needs to work harmoniously with federal legislation to speed up zero and low emission cars. Finally, Uber will also need to offset the challenge of bad weather, which confuses the vehicle, by performing software enhancement to lidar technology (Thekdi & Joshi, 2016). The improvement should focus on the light sensing and range to avoid unnecessary accidents. In the same vein, the vehicles can be designed in a way that human interventions during harsh weather or environmental conditions to minimize the accidents and technical failures.
References
Bagloee, S. A., Tavana, M., Asadi, M., & Oliver, T. (2016). Autonomous vehicles: challenges, opportunities, and future implications for transportation policies. Journal of Modern Transportation, 24(4), 284-303.
Fagnant, D. J., & Kockelman, K. (2015). Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations. Transportation Research Part A: Policy and Practice, 77, 167-181.
Thekdi, S. A., & Joshi, N. N. (2016). Risk-based vulnerability assessment for transportation infrastructure performance. International Journal of Critical Infrastructures, 12(3), 229-247.