路面条件和驾驶使用智能手机的行为分析 外文翻译

1、毕业设计外文资料翻译题 目: 路面条件和驾驶使用智能手机的行为分析 院系名称：设计艺术学院 专业班级： 工设F1202 学生姓名： 学 号： 指导教师： 教师职称： 地 点: 18号楼产品设计工作室（I） 指导教师评语： 签名： 年 月 日附件1：外文资料翻译译文路面条件和驾驶使用智能手机的行为分析在发展中国家的道路质量不是很好，迅速恶化，这使得系统可以实时检测道路违规行为，可以使用户安全驾驶。此外，车队运营商想确保他们的客户安全的出行。这可以通过跟踪和维护一个历史的驱动程序的驱动模式，此外，个人还想评估他们的驾驶风格，并成为安全的驱动程序可实施的另一个原因。在这方面，一个具有成本效益的解决方

2、案，用于检测道路以及跟踪驱动程序使用智能手机的驾驶行为已被开发。该解决方案依赖于安装在智能手机上的传感器，从而使其成本有效。虽然该系统不使用商业级传感器，但我们的准确数字表明，该解决方案是足够好的商业部署。基于智能手机的一些非常流行的方法，使用通过传感器收集的数据，用于用户的活动检测各种环境（室内定位 3 ，交通检测使用智能手机收集数据是一种很有前途的替代,因为它的低成本和易于使用的特点。本文提出了一种非侵入式的方法，使用传感器目前在智能手机上，其中大部分人都是预期的进行，因此不需要任何专业就可以在车辆或在路边安装的硬件。在这里,我们已经扩展了各种以前的研究，以提高基于使用的加速度计，全球定位

3、系统和算法用于交通和道路异常的地磁传感器读数检测。具体的操作是在确定制动事件经常刹车指示-拥挤的交通状况和公路路面的异常特征。使用驾驶行为分析，包括硬加速/减速，刹车，转弯，频繁的车道变化等，我们提出了一种方法，以获得驾驶分数考虑到大量的参数，这使得它成为比以往任何工作更准确。因为，每天在道路上行驶的通勤者，这使得这一解决方案更为重要且意义重大。一个Android应用程序（s-road协助，已经在谷歌Play商店）来从各种传感器如加速度计的智能手机，目前的重力数据采集，磁强计和定位（GPS）传感器。应用程序支持的驱动模式检测，并自动启动自己收集和接收器的数据服务器。驱动方式检测算法通过计算根使

4、用三轴加速度计平均值在手机应用设备上每一次改变方向的平方加速，而我们将每一秒重新定位在数据窗口的算法，重新定位传感器的值，所有的窗口都需要照顾的传感器的影响，如果它停留的时间较长，例如，如果用户占用了一个呼叫。一个完整的工作系统，上面提到的方法并提出了解决方案已经在开发中。安卓应用“s-road协助”上可以找到谷歌Play商店。目标是帮助提高驾驶经验，协助用户提高驾驶技能。这是由分析引擎检测后对记录数据的分析，这就回到了智能手机的使用同一个应用程序生产，为即将到来的事件的通知警报，沿路线提醒。大数据分析关键是克服在数据采集过程中所做的检测。进一步强调，解决不再需要专门的硬件，只需要在智能手机上

5、，它不会限制在车辆里面（可在任意方向或位置），从而使解决方案具有成本效益。该技术的系统将有一定的帮助避免很多道路伤亡和提高驾驶。在这里，智能手机作为一种资产记录数据，以查明的被通知用户的道路异常从而把智能手机变成一个“保护电话”。外文资料摘自：3 H. Lu, W. Pan, N. D. Lane, T. Choudhury, and A. T. Campbell, Soundsense: Scalable sound sensing for people-centric applications on mobile phones, In Proc. of the 7th Int. Conf.

6、 on Mobile systems, MobiSys09.附件2：外文原文Road surface conditions and Driving Behavior analysis using SmartphonesIn developing countries road quality is not very good and deteriorates quickly, this makes a system which can detect road irregularities and notify user regarding risky driving in real-time,

7、valuable. Moreover, fleet operators would like to ensure safe trips to their customers. This can be accomplished by tracking and maintaining a history of the driving patterns of the drivers employed by them. Further, individuals would also like to assess their driving style and become safe drivers.

8、In this regard, a cost effective solution for detecting road artifacts as well as tracking the driving behavior of drivers using smartphones has been developed. The solution relies on the sensors installed on the smarphone only thereby making it cost effective. Although the system does not use comme

9、rcial grade sensors, yet our accuracy numbers indicate that the solution is good enough for commercial deployment.Few of the very popular methods based on smartphones, use data collected through sensors for user activity detection in various environments (Indoor localization 3, traffic detection Usi

10、ng smartphone to collect data is a promising alternative because of its low cost and easy to use features in addition to its potentially wide population coverage as probe devices. This paper proposes a non-intrusive method that uses sensors present on smartphones, which most of the people are expect

11、ed to carry, thus obviating the need for any specialized hardware to be installed in vehicle or on the roadside. Here, we have extended various prior studies to improve the algorithms based on using accelerometer, GPS and magnetometer sensor readings for traffic and road anomaly detection. Specific

12、interest is in identifying braking events - frequent braking indicates congested traffic conditions and anomalies on the roads to characterize the type of road. Using driving behavior analysis including hard acceleration/deceleration, braking, turns, frequent lane changes, etc. we propose a way to d

13、erive driving score considering multitudes of parameters, which makes it to be more accurate than any prior work. Since, we have a large number of commuters who daily travel on road, that makes this solution even more important and significant.An android application (S-Road Assist, already on Google

14、 Play Store) is developed to collect data from various sensors present in smartphone like accelerometer, gravity, magnetometer and location (GPS) sensor. Application does support the driving mode detection and automatically launches itself to collect and sink data to servers. Driving mode detection

15、algorithm makes use of 3-axis accelerometer values by calculating root mean square acceleration on the device applied every time the phone changes orientation, whereas we apply the re-orientation algorithm over a data window, say every 1 sec. Re-orienting sensor values over all the windows takes car

16、e of the impact on the sensor, if it stays for a longer duration, for instance if user takes up a call.A complete working system with above mentioned approaches and proposed solution is already in development. Android application named “S-Road Assist” can be found on Google Play Store. The objective

17、 is to help improve driving experience and assist users in improving driving skills. Events (road artifacts), that are detected by the analytics engine after the analysis of recorded data, are pushed back to the smartphone using the same application for producing notification alerts of upcoming even

18、ts along the route in advance. Big data analysis or crowdsourcing is the key to overcome the compromises made during data collection, preprocessing or detection. To emphasize further, solution obviates the need for any specialized hardware and relies only on the smartphone, which may or may not be docked inside the vehicle (can be in any arbitrary orientation or position), thereby making the proposed solution cost-effective. Building a system