By employing a well orchestrated combination of Internet of Things sensors

By employing a well orchestrated combination of

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By employing a well-orchestrated combination of Internet of Things sensors, facial recognition software, and artificial intelligence, Uber will be able to ensure that the Uber driver is indeed the qualified driver, allow for deeper vetting, and make more opportunities to do more frequent criminal and driving record background checks. Furthermore, these technologies can enhance safety measures that protect both drivers and riders alike, such as reducing reckless, distracted, and impaired driving. Finally, with the use of these technologies, Uber can rest assured that their riders are safe from driver created price surge manipulations. Uber reported $457 million in research and development expenses for its self-driving unit, Advanced Technologies Group (ATG), during their initial public offering. That was up from $384 million in 2017, approximately a 19% increase, and $230 million in 2016. Overall, Uber spent over $1.1 billion into ATG, or roughly 30% of its entire research and development budget
Uber and Its Safety Strategy 13 (Bloomberg, 2019). Uber vehemently believes autonomous driving and flying car initiatives is a key part of its existing business. The new technologies will not only provide the company with a stronger economic moat but it will provide a safer environment for the roads, decreasing human- error derived accidents. 37,461 people were killed driving on United States roadways alone in 2016 and more than 90% of crashes are estimated to be caused by some form of human error. With autonomous vehicles, there is no need to worry about reckless driving, fatigued driving, or impaired driving. Moreover, given autonomous vehicles will eliminate human drivers in its entirety, there is no need to worry about driver background checks or manipulated price surges. However, unlike the other technologies discussed in this paper, such as IoT sensors, facial recognition, artificial intelligence, machine learning, and deep learning, autonomous vehicles are not ready to be commercially deployed as of today, and thus timing is an important variable in this equation. Public perception, regulation, legislation, and of course the technology itself are all factors in the deployment time of autonomous vehicles. According to automakers and the large OEMs, we are two to ten years away from seeing fully autonomous Stage 5 vehicles. Paradoxically, safety is the biggest variable and issue here as it is seen in “Pittsburgh, [Uber’s autonomous vehicles] been in multiple accidents and have been filmed driving the wrong way on city roads. During their time in San Francisco, the vehicles seemed to completely ignore bike lanes, creating risk for cyclists, and ran a number or red lights” (Paris Marx, 2018). Furthermore, it was recorded that “a driver had to take over from Uber’s autonomous system an average of 10 times for every eight miles driven and the distance between ‘bad experiences’ when the car did something it wasn’t supposed to was actually getting worse” (Medium, 2018).

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