[雙語(yǔ)翻譯]無(wú)人駕駛外文翻譯--正面和負(fù)面信息對(duì)消費(fèi)者乘坐無(wú)人駕駛車輛的意愿的影響（中英文）

1、6100 英文單詞， 英文單詞，3.4 萬(wàn)英文字符，中文 萬(wàn)英文字符，中文 10500 字文獻(xiàn)出處： 文獻(xiàn)出處：Anania E C, Rice S, Walters N W, et al. The effects of positive and negative information on consumers’ willingness to ride in a driverless vehicle[J]. Transport pol

2、icy, 2018, 72: 218-224.The effects of positive and negative information on consumers’ willingness to ride in a driverless vehicleEmily C. Anania, Stephen Rice, Nathan W. Walters, Matthew Pierce, Scott R. Winter, Mattie

3、N. MilnerAbstractObjectives: As automation becomes more prevalent in the transportation industry, driverless vehicles are appearing more frequently in the news. However safe or efficient these vehicles are touted to be,

4、media portrayal has the potential to dramatically affect consumer perceptions. Consumer perceptions will ultimately determine the success or failure of driverless vehicles, and potentially drive policy changes as driverl

5、ess vehicles become more common.Methods: The purpose of this research was to investigate the effects of different types of information (positive or negative) on consumer perceptions of driverless vehicles, and how indivi

6、duals of different genders and nationalities may feel influenced differently by the same information. The current research used a two-study approach.Results: In the first study, results showed that individuals are more w

7、illing to ride in driverless vehicles after hearing positive information about them, and less willing to ride after hearing negative information about the vehicles. In study two, our results were similar to those found i

8、n study one regarding information type and willingness to ride. Additionally, there was a main effect of nationality because Indians were significantly more willing to ride in driverless vehicles compared to Americans. T

9、his main effect of nationality was qualified by significant interactions between nationality and gender as well as nationality and information type.Conclusions: Overall, Indian females were the group with highest willing

10、ness to ride scores. The paper concludes with a discussion of the findings, theoretical contributions, practical applications, development of policy, and potential avenues of future study.Keywords: Driverless vehicles ；P

11、ublic perceptions ；Media portrayal1. IntroductionDriverless vehicles are currently gaining momentum not only in industry, but also in the public forum. The media coverage on driverless transportation, especially cars, ha

12、s increased over the past decades. A growing number of news outlets are not only focusing on companies developing automated driverless technologies, but also consumer experiences with driverless cars. For example, recent

13、 news articles have reported self-driving cars with topics ranging from the technology advancements for self-driving vehicles (Davis, 2018), to the benefits of self-driving vehicles for the elderly and those with disabil

14、ities (Halsey, 2017), and of course to the catastrophic – a self-driving car killing a pedestrian (Wakabayashi, 2018). Driverless cars are frequently reported on as new developments happen, whether they are technological

15、 developments, social developments, or policy developments. However, not all development is positive. With the increasing capability of driverless technologies (i.e. automated braking systems, lane change assist) there i

16、s also a certain level of accountability – companies’ products are expected to work transportation.1.2. Driverless carsIn the automobile industry, simple automation, such as automatic windows or seat control technology,

17、has advanced at a rather sustainable rate for consumer expectation and acceptance. While some complaints existed around these automated functions during the introductory phase, the technology had a minimal effect on the

18、owner's interaction with the vehicle and soon became more desirable than past manual operations. However, now a majority of new cars perform various high-level automated tasks, including collision avoidance and autom

19、atic route planning for the operator. Modern cars use sensor technology to indicate when to apply brakes or merge lanes to avoid a potential accident (Waldrop, 2015). These functions are vastly different from a simple au

20、tomated window and have greater possibility to affect the driver's situational awareness, dramatically changing the way people interact with automobiles. Currently, we are reaching a new and unusual era of automated

21、 technology in vehicles and how we traverse our city and state highways. Soon, human operators will share the roads with driverless automobiles; therefore, it is critical that we understand consumers' perceptions tow

22、ard these vehicles while interacting with them from behind the wheel and across the lane.A few companies have successfully incorporated the technology needed for personally owned vehicles to operate as highly automated v

23、ehicles. For example, Waymo, the self-driving car formerly known as the Google Car, has driven over two million miles utilizing varying degrees of autonomy (levels: 3-5; Google, 2016). In addition, Uber (privately owned

24、vehicles used for a taxi service) now allows customers to order driverless cars rather than having a traditional human driver (Chafkin, 2016). In Pittsburgh, where the debut of this technology has been introduced, summon

25、ing a driverless Uber comes equipped with a safety feature: a human fail-safe component (Levandowski and Kalanick, 2016). Per law, and as an emotional comfort to passengers, the operator inside the driverless Uber is tas

26、ked to remain vigilant and with their hands on the wheel in case any failures occur during the vehicle's autonomous operation.Chinese tech company, Baidu, has declared its intentions to commercialize driverless trans

27、portation by 2018 and mass production of its technology by 2020 (Autonomous Driving Unit, 2017). The company plans to succeed in this matter through open-sourcing its technology for autonomous driving to the public throu

28、ghout the year, releasing the most basic technology first, followed by the most advanced technology at the end of the year (Clover, and Fei Ju, 2017). Furthermore, by 2040, autonomous vehicles are expected to grow global

29、ly by 25% (West, 2016).In 2016, the Virginia Tech Transportation Institute released a report, sponsored by Google Inc. detailing the crash rates of self-driving vehicles versus traditional driven vehicles (Blanco et al.,

30、 2016). The modality of driver is difficult to compare because, as Blanco et al. (2016) mention, different states have different crash reporting requirements, and many crashes (in all states) are unreported. However, whe

31、n comparing the Google Self-Driving Car project and Second Strategic Highway Research Program (SHRP 2), the Self-Driving Car had lower accident rates in all “l(fā)evels” of accident severity (Level 1 being the most severe).

32、Estimated crash rates for the Self-Driving Car in Level 1, 2, and 3 accidents were 1.6, 1.6, and 5.6 per million miles driven, respectively. Estimated crash rates for SHRP 2 in Level 1, 2, and 3 accidents were 5.5, 5.5,

33、and 9.2 per million miles driven, respectively. Mishaps related to driverless vehicles (not specifically Google-related) include the death of a pedestrian in the March 2018 Uber crash (Wakabayashi, 2018), and the death o