English:
[0001] Lane Departure Warning (LDW) systems provide a way for vehicle drivers to adjust their driving behavior if a lane departure of a vehicle is imminent. An important feature of such systems may be providing output indicating an impending lane departure so that the driver can take corrective action. Current systems may include actuation of mechanisms that provide output to all vehicle occupants, other than the vehicle driver.
[0002] Figure 1 is a block diagram of an example system for including a wearable device providing output indicating a possible lane departure.
[0004] Figure 1 illustrates a system 100 including a wearable device 140 communicatively coupled to a vehicle 101 computing device 105. The computing device 105 is programmed to receive collected data 115, from one or more data collectors 110, e.g., vehicle 101 sensors, concerning various metrics related to the vehicle 101. For example, the metrics may include a velocity of the vehicle 101, vehicle 101 acceleration and/or deceleration, data related to vehicle 101 path or steering including Time-to-Lane Crossing (TLC) , biometric data related to a vehicle 101 operator, e.g., heart rate, respiration, pupil dilation, body temperature, state of consciousness, etc. Further examples of such metrics may include measurements of vehicle systems and components (e.g. a steering system, a powertrain system, a brake system, internal sensing, external sensing, etc.). The computing device 105 may be programmed to collect data 115 from the vehicle 101 in which it is installed, sometimes referred to as a host vehicle 101, and/or may be programmed to collect data 115 about a second vehicle 101, e.g., a target vehicle.
[0005] The computing device 105 is generally programmed for communications on a controller area network (CAN) bus or the like. The computing device 105 may also have a connection to an onboard diagnostics connector (OBD-II). Via the CAN bus, OBD-II, and/or other wired or wireless mechanisms, the computing device 105 may transmit messages to various devices in a vehicle and/or receive messages from the various devices, e.g., controllers, actuators, sensors, etc., including data collectors 110. Alternatively or additionally, in cases where the computing device 105 actually comprises multiple devices, the CAN bus or the like may be used for communications between devices represented as the computing device 105 in this disclosure. In addition, the computing device 105 may be programmed for communicating with the network 120, which, as described below, may include various wired and/or wireless networking technologies, e.g., cellular, Bluetooth, wired and/or wireless packet networks, etc.
[0006] The data store 106 may be of any known type, e.g., hard disk drives, solid-state drives, servers, or any volatile or non- volatile media. The data store 106 may store the collected data 115 sent from the data collectors 110.
[0007] A LDW controller 107, as is known, is included in the vehicle 101 and includes a processor and a memory, the processor storing instructions to determine whether the vehicle 101 is about to leave a current lane and, upon such determination, to actuate one or more vehicle mechanisms without driver intervention, e.g., braking, steering, throttle, etc. Further, the LDW controller 107 may include or be connected to an output mechanism to indicate a potential lane departure, e.g., sounds and/or visual indicators provided via the vehicle 101 HMI.
[0008] Data collectors 110 may include a variety of devices. For example, various controllers in a vehicle may operate as data collectors 110 to provide data 115 via the CAN bus, e.g., data 115 relating to vehicle speed, acceleration, system and/or component functionality, etc., of any number of vehicles 101, including the host vehicle and/or the target vehicle. Further, sensors or the like, global positioning system (GPS) equipment, etc., could be included in a vehicle and configured as data collectors 110 to provide data directly to the computer 105, e.g., via a wired or wireless connection. Sensor data collectors 110 could include mechanisms such as RADAR, LIDAR, sonar, etc. sensors that could be deployed to measure a distance between the vehicle 101 and other vehicles or objects. Yet other data collectors 110 could include cameras, breathalyzers, motion detectors, etc., i.e., data collectors 110 to provide data 115 for evaluating a condition or state of a vehicle 101 operator.
[0009] Collected data 115 may include a variety of data collected in a vehicle 101. Examples of collected data 115 are provided above, and moreover, data 115 is generally collected using one or more data collectors 110, and may additionally include data calculated therefrom in the computer 105, and/or at the server 125. In general, collected data 115 may include any data that may be gathered by the data collectors 110 and/or computed from such data. The computing device 105 may use the collected data 115 to develop a lane departure profile for the occupant. The lane departure profile may incorporate the occupant's driving habits and characteristics, e.g., age, experience driving, etc., as well as the collected data 115, e.g. road condition, movement of the vehicle 101, etc. The lane departure profile may be used by the LDW controller 107 to detect an imminent lane departure. The computing device 105 may create several lane departure profiles for different occupants and store them in the data store 106.
[0010] The system 100 may further include a network 120 connected to a server 125 and a data store 130. The computer 105 may further be programmed to communicate with one or more remote sites such as the server 125, via a network 120, such remote site possibly including a data store 130. The network 120 represents one or more mechanisms by which a vehicle computer 105 may communicate with a remote server 125. Accordingly, the network 120 may be one or more of various wired or wireless communication mechanisms, including any desired combination of wired (e.g., cable and fiber) and/or wireless (e.g., cellular, wireless, satellite, microwave, and radio frequency) communication mechanisms and any desired network topology (or topologies when multiple communication mechanisms are utilized). Exemplary communication networks include wireless communication networks (e.g., using Bluetooth, IEEE 802.11, etc.), local area networks (LAN) and/or wide area networks (WAN), including the Internet, providing data communication services.
[0011] The server 125 may be programmed to determine an appropriate action for one or more vehicles 101, and to provide direction to the computer 105 to proceed accordingly. The server 125 may be one or more computer servers, each generally including at least one processor and at least one memory, the memory storing instructions executable by the processor, including instructions for carrying out various steps and processes described herein. The server 125 may include or be communicatively coupled to a data store 130 for storing collected data 115, records relating to potential incidents generated as described herein, lane departure profiles, etc. Further, the server 125 may store information related to particular vehicle 101 and additionally one or more other vehicles 101 operating in a geographic area, traffic conditions, weather conditions, etc., within a geographic area, with respect to a particular road, city, etc. The server 125 could be programmed to provide alerts to a particular vehicle 101 and/or other vehicles 101.
[0012] A wearable device 140 may be any one of a variety of computing devices including a processor and a memory, as well as communication capabilities that is programmed to be worn on a driver's body. For example, the wearable device 140 may be a watch, a smart watch, a vibrating apparatus, etc. that includes capabilities for wireless communications using IEEE 802.11, Bluetooth, and/or cellular communications protocols. Further, the wearable device 140 may use such communications capabilities to communicate via the network 120 and also directly with a vehicle computer 105, e.g., using Bluetooth.
[0013] The system 100 may include a user device 150. The user device 150 may be any one of a variety of computing devices including a processor and a memory, e.g., a smartphone, a tablet, a personal digital assistant, etc. the user device 150 may use the network 120 to communicate with the vehicle computer 105 and the wearable device 140.
[0014] Figure 2 illustrates an example process 200 employing the wearable device 140 in conjunction with the LDW controller 107. The process 200 begins in a block 202, in which the computing device 105 identifies a vehicle 101 occupant. The computing device 105 may identify the driver in one or more manners, e.g., receiving data from one or more of the wearable device 140, the user device 150, and/or the data collectors 110. For example, as is known, the occupant could be identified using image recognition techniques in the computer 105 using data 115 from a camera data collector 110, a user identity could be input via a device 140 of a vehicle 101 HMI, or the device 140 could identify the user / occupant via collected biometric data, e.g., a fingerprint, etc. Upon identifying the occupant, the computing device 105 may retrieve from its data store 106 information about the occupant, including age, size, driving skill level, preferred mechanisms for receiving information (e.g., haptic, audio, etc.), lane departure profile, etc.
[0015] Next, in a block 205, the computing device 105 collects data 115 from the data collectors 110. The data 115 may include lane marker position, vehicle 101 position relative to the lane markers, vehicle 101 velocity, forward trajectory of the vehicle 101, lateral movement of the vehicle 101, etc.
[0016] Next, in a block 210, the computing device 105 determines the time to lane crossing (TLC) of the vehicle 101. The TLC is a measure of an estimated time for the vehicle 101 to cross from the present lane to another lane. The TLC is obtained from the vehicle LDW controller 107.
[0017] Next, in a block 215, the computing device 105 calculates a lane negotiation characteristic (LNC). The LNC is a measure of driver lane behavior based on lane offset information from the data collectors 110. The computing device 105 may use the wearable device 140 and the user device 150 to calculate the LNC in a distributed computing process depending on the amount of computing resources available on each device. The LNC learning system characterizes the driver lane behavior based on lane offset information obtained from the vehicle vision system. The mean and variance of the vehicle offset position is recursively computed in real-time to obtain a personalized driver LNC.
[0018] Next, in a block 220, the computing device 105 determines whether the TLC is lower than a first threshold. The first threshold may be adjusted based on the information retrieved about the occupant in the block 202, e.g., a novice driver may have a lower threshold to account for increased reaction time, a skilled driver may have a higher threshold, etc. further, the threshold may be in addition to, and set lower than, a threshold used in presently existing lane departure warning systems 107, as mentioned above. The threshold may also be determined at least in part on the vehicle speed, road conditions, current weather, and/or other data 115 collected by the data collectors 110. The first threshold may also be determined at least in part from at least one of the lane departure profiles stored in the data store 106 and/or the server 125. If the TLC is not below the first threshold, the process 200 returns to the block 205 to collect more data 115.
[0019] In the block 225, the computing device 105 determines whether the LNC is greater than a second threshold value. The first and second thresholds are generally established to cause actuation of a mechanism in the wearable device 140 before the conventional lane departure warning controller 107 triggers an action, e.g., control of one or more vehicle 101 components and/or actuating a vehicle 101 warning mechanism. The second threshold may be determined at least in part from at least one of the lane departure profiles stored in the data store 106 and/or the server 125. If the LNC is not greater than the threshold value, the process 200 returns to the block 205 to collect more data 115. Otherwise, the process 200 continues in a block 230.
[0020] In the block 230, the computing device 105 provides an instruction to the wearable device 140 to actuate one or more output mechanisms. The output mechanisms may include haptic output, e.g. a vibration, audio output, and/or visual output, e.g. flashing lights, flashing colors, etc. Based on the information from the block 205, the one or more output mechanism may be selected according to the occupant. For example, an occupant who is hard of hearing may have a stronger vibration output, while another occupant may prefer a visual output. Advantageously, the computing device 105 may be programmed, e.g., including setting the threshold of the block 220, to cause actuation of the wearable device output prior to an alert, warning, or evasive action implemented by a conventional lane negotiation controller 107, e.g., a system that provides an indication of, or reacts to, an imminent collision by actuating vehicle lights, sounds, brakes, etc. Thus, the driver of the vehicle 101 in the context of the present system 100 can take evasive and/or avoidance action earlier, and in a safer manner, than in presently existing systems.
[0021] Next, in the block 235, the computing device 105 determines whether the lane detection warning controller 107 detects an imminent lane departure, i.e., the vehicle 101 will move from the current lane to another lane in the next few seconds. The LDW system uses the data 115 to determine whether the vehicle 101 is about to exit the lane. If the LDW controller 107 does not detect an imminent departure, the process 200 ends. Otherwise, the process 200 proceeds to a block 240.
[0022] In the block 240, the computing device 105 activates one or more second output mechanism, i.e. a vehicle alert, using the LDW controller 107 and the process 200 ends. The second output mechanisms may include, e.g., a vibrating steering wheel, an alarm through the vehicle speakers, a flashing light on the dashboard, etc.
[0024] Computing devices generally each include instructions executable by one or more computing devices such as those identified above, and for carrying out blocks or steps of processes described above. Computer-executable instructions may be compiled or interpreted from computer programs created using a variety of programming languages and/or technologies, including, without limitation, and either alone or in combination, JavaTM, C, C++, Visual Basic, Java Script, Perl, HTML, etc. In general, a processor (e.g., a microprocessor) receives instructions, e.g., from a memory, a computer-readable medium, etc., and executes these instructions, thereby performing one or more processes, including one or more of the processes described herein. Such instructions and other data may be stored and transmitted using a variety of computer-readable media. A file in the computing device is generally a collection of data stored on a computer readable medium, such as a storage medium, a random access memory, etc.
[0025] A computer-readable medium includes any medium that participates in providing data (e.g., instructions), which may be read by a computer. Such a medium may take many forms, including, but not limited to, non-volatile media, volatile media, etc. Non- volatile media include, for example, optical or magnetic disks and other persistent memory. Volatile media include dynamic random access memory (DRAM), which typically constitutes a main memory. Common forms of computer-readable media include, for example, a floppy disk, a flexible disk, hard disk, magnetic tape, any other magnetic medium, a CD-ROM, DVD, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, a RAM, a PROM, an EPROM, a FLASH-EEPROM, any other memory chip or cartridge, or any other medium from which a computer can read.
[0026] With regard to the media, processes, systems, methods, etc. described herein, it should be understood that, although the steps of such processes, etc. have been described as occurring according to a certain ordered sequence, such processes could be practiced with the described steps performed in an order other than the order described herein. It further should be understood that certain steps could be performed simultaneously, that other steps could be added, or that certain steps described herein could be omitted. For example, in the process 200, one or more of the steps could be omitted, or the steps could be executed in a different order than shown in Figure 2. In other words, the descriptions of systems and/or processes herein are provided for the purpose of illustrating certain embodiments, and should in no way be construed so as to limit the disclosed subject matter.
[0027] Accordingly, it is to be understood that the present disclosure, including the above description and the accompanying figures and below claims, is intended to be illustrative and not restrictive. Many embodiments and applications other than the examples provided would be apparent to those of skill in the art upon reading the above description. The scope of the invention should be determined, not with reference to the above description, but should instead be determined with reference to claims appended hereto and/or included in a non-provisional patent application based hereon, along with the full scope of equivalents to which such claims are entitled. It is anticipated and intended that future developments will occur in the arts discussed herein, and that the disclosed systems and methods will be incorporated into such future embodiments. In sum, it should be understood that the disclosed subject matter is capable of modification and variation.

中文:
[0001]如果车辆即将偏离车道,则偏离车道警告(Lane Departure Warning,LDW)系统为车辆驾驶员提供调整其驾驶行为的方法。该系统的重要特征是可以提供指示即将偏离车道的输出,使得驾驶员可以采取校正措施。现有的系统可以包括向车辆驾驶员在内的所有车辆乘员提供输出的致动机构。
[0002]图1是包括可穿戴设备的示例系统的框图,该可穿戴设备提供指示可能偏离车道的输出;
[0004]图1示出了包括可穿戴设备140的系统100,可穿戴设备140可通信地连接到车辆101的计算设备105。计算设备105编程为从一个或多个数据收集器110(例如车辆101的传感器)接收关于车辆101的各种度量的收集的数据115。例如,度量可以包括车辆101的速度、车辆101加速度和/或减速度、与车辆101的路径或转向有关的数据(包括车道交错时间(Time-to-Lane Crossing,TLC))、关于车辆101的驾驶员的生物数据(例如心率、呼吸、散瞳、体温、意识状态等)。这种度量的其他示例可以包括对车辆系统和部件(例如转向系统、动力传动系统、制动系统、内部感测部件、外部感测部件等)的测量。计算设备105可以编程为收集来自内部安装有计算设备105的车辆101(有时称为主车辆101)的数据115,和/或可以编程为收集关于第二车辆101(例如目标车辆)的数据115。
[0005]计算设备105通常编程以在控制器局域网(controller area network,CAN)总线等上进行通信。计算设备105还可以连接到车载诊断连接器(OBD-II)。计算设备105可以通过CAN总线、OBD-II、和/或其他有线或无线机构向车辆中的各种设备发送消息和/或从包括数据收集器110的各种设备(例如控制器、致动器、传感器等)接收消息。可替代地或额外地,在计算设备105实际上包括多个设备的情况下,CAN总线等可以用于在本公开中表示为计算设备105的设备之间的通信。另外,计算设备105可以编程为与网络120通信,如下所述网络120可以包括各种有线和/或无线网络技术,例如蜂窝、蓝牙、有线和/或无线分组网络等。
[0006]数据存储装置106可以是任何已知的类型,例如硬盘驱动器、固态驱动器、服务器、或任何易失性或非易失性介质。数据存储装置106可以存储从数据收集器110发送的收集的数据115。
[0007]如已知的,车辆101中包括LDW控制器107,并且LDW控制器107包括处理器和存储器,处理器存储指令以确定车辆101是否将要离开当前车道,并且当如此确定时在没有驾驶员干预的情况下致动一个或多个车辆机构(例如制动、转向、节气门等)。此外,LDW控制器107可以包括或连接到输出机构,来(例如,通过车辆101的人机界面(Human-machineinterface,HMI)提供的声音和/或视觉指示符)指示潜在的偏离车道。
[0008]数据收集器110可以包括各种设备。例如,车辆中的各种控制器可以用作数据收集器110,以通过CAN总线提供数据115(例如,与任何数量的车辆101的车辆速度、加速度、系统、和/或部件功能等有关的数据115,车辆101包括主车辆和/或目标车辆)。此外,传感器等、全球定位系统(global positioning system,GPS)设备等可以被包括在车辆中,并且配置成数据收集器110以例如通过有线或无线连接而直接向计算机105提供数据。传感器数据收集器110可以包括机构(例如雷达、激光雷达、声纳等传感器),传感器可以被部署以测量车辆101与其他车辆或物体之间的距离。其他数据收集器110可以包括摄像机、呼吸测醉器(breathalyzer)、运动检测器等,即数据收集器110提供用于评估车辆101的驾驶员的情况或状态的数据115。
[0009]收集的数据115可以包括在车辆101中收集的各种数据。上面提供了收集的数据115的示例,并且此外,通常使用一个或多个数据收集器110来收集数据115,并且数据115可以另外包括在计算机105和/或服务器125中计算的数据。通常,收集的数据115可以包括可以由数据收集器110收集、和/或根据这些数据进行计算的任何数据。计算设备105可以使用收集的数据115来为乘员生成偏离车道配置文件。偏离车道配置文件可以包括乘员的驾驶习惯和特征(例如年龄、驾驶经验等),以及收集的数据115(例如路况、车辆101的移动等)。LDW控制器107可以使用偏离车道配置文件来检测即将发生的偏离车道。计算设备105可以为不同的乘员创建不同的偏离车道配置文件,并将它们存储在数据存储装置106中。
[0010]系统100还可以包括数据存储装置130和连接到服务器125的网络120。计算机105还可以编程为通过网络120与可能包括数据存储装置130的一个或多个远程站点(例如服务器125)通信。网络120表示车辆计算机105可以与远程服务器125通信所使用的一个或多个机构。因此,网络120可以是各种有线或无线通信机构中的一个或多个,包括有线(例如电缆和光纤)和/或无线(例如蜂窝、无线、卫星、微波、和射频)通信机构和任何所需的网络拓扑(或当使用多个通信机构时的多个拓扑)的任何所需的组合。示例性通信网络包括提供数据通信服务的无线通信网络(例如使用蓝牙、IEEE 802.11等)、局域网(local area network,LAN)、和/或包括因特网的广域网(wide area network,WAN)。
[0011]服务器125可以编程为确定一个或多个车辆101的适当动作,并且向计算机105提供指示以依此执行。服务器125可以是一个或多个计算机服务器,每个计算机服务器通常包括至少一个处理器和至少一个存储器,存储器存储可由处理器执行的指令,包括用于执行本文描述的各种步骤和过程的指令。服务器125可以包括或可通信地连接到数据存储装置130,数据存储装置130用于存储收集的数据115、关于如本文所述而生成的潜在事故的记录、偏离车道配置文件等。此外,服务器125可以存储关于特定车辆101以及额外的在地理区域内运行的一个或多个其他车辆101、相对于特定道路、城市等的地理区域内的交通状况、天气状况等的信息。服务器125可以编程为向特定车辆101和/或其他车辆101提供警报。
[0012]可穿戴设备140可以是编程为穿戴在驾驶员身体上的各种计算设备中的任何一种,计算设备包括处理器和存储器、以及通信能力。例如,可穿戴设备140可以是包括使用IEEE 802.11、蓝牙、和/或蜂窝通信协议进行无线通信的能力的手表、智能手表、振动装置等。此外,可穿戴设备140可以使用这种通信能力来通过网络120进行通信,并且还可以例如使用蓝牙直接与车辆计算机105进行通信。
[0013]系统100可以包括用户设备150。用户设备150可以是包括处理器和存储器的各种计算设备(例如智能手机、平板电脑、个人数字助理等)中的任何一种,用户设备150可以使用网络120来与车辆计算机105和可穿戴设备140进行通信。
[0014]图2示出了结合LDW控制器107使用可穿戴设备140的示例过程200。过程200始于框202,在框202中计算设备105识别车辆101的乘员。计算设备105可以以一种或多种方式(例如从可穿戴设备140、用户设备150、和/或数据收集器110中的一个或多个接收数据)来识别驾驶员。例如,如已知的,在使用来自摄像机数据收集器110的数据115的计算机105中,可以使用图像识别技术来识别乘员,可以通过车辆101的HMI的设备140输入用户身份,或者设备140可以通过收集的生物识别数据(例如指纹等)识别用户/乘员。在识别乘员时,计算设备105可以从其数据存储装置106中检索关于乘员的信息,包括年龄、尺寸、驾驶技术水平、接收信息的优选机制(例如触觉、音频等)、偏离车道配置文件等。
[0015]接下来在框205中,计算设备105收集来自数据收集器110的数据115。数据115可以包括车道标记位置、车辆101相对于车道标记的位置、车辆101的速度、车辆101向前的轨迹、车辆101的横向移动等。
[0016]接下来在框210中,计算设备105确定车辆101的车道跨越时间(time to lanecrossing,TLC)。TLC是车辆101从当前车道跨越到另一车道的预计时间的度量。TLC从车辆LDW控制器107获得。
[0017]接下来在框215中,计算设备105计算车道通过特征(lane negotiationcharacteristic,LNC)。LNC是根据来自数据收集器110的车道偏移信息对驾驶员车道行为的度量。计算设备105可以使用可穿戴设备140和用户设备150,以根据每个设备上可用的计算资源的量而在分布式计算处理中计算LNC。LNC学习系统根据从车辆视觉系统获得的车道偏移信息来表征驾驶员车道行为。实时递归地计算车辆偏移位置的均值和方差以获得个性化驾驶员LNC。
[0018]接下来在框220中,计算设备105确定TLC是否低于第一阈值。可以根据在方框202中检索到的关于乘员的信息而调整第一阈值,例如,新手驾驶员可以具有较低的阈值以考虑增加的反应时间,而熟练的驾驶员可以具有较高的阈值等。此外,该阈值可以不同于(并且设定为低于)如上所述的当前现有的偏离车道警告系统107中使用的阈值。该阈值还可以根据由数据收集器110收集的车辆速度、道路状况、当前天气、和/或其他数据115而至少部分地确定。第一阈值还可以根据存储在数据存储装置106和/或服务器125中的至少一个偏离车道配置文件,而至少部分地确定。如果TLC不低于第一阈值,则过程200返回到框205以收集更多的数据115。否则,过程200在框225中继续。
[0019]在框225中,计算设备105确定LNC是否大于第二阈值。通常建立第一阈值和第二阈值,以在常规的偏离车道警告控制器107触发动作(例如,控制一个或多个车辆101的部件和/或致动车辆101的警告机构)之前致动可穿戴设备140中的机构。第二阈值可以根据存储在数据存储装置106和/或服务器125中的至少一个偏离车道配置文件而至少部分地确定。如果LNC不大于阈值,则过程200返回到框205以收集更多数据115。否则,过程200在框230中继续。
[0020]在框230中,计算设备105向可穿戴设备140提供致动一个或多个输出机构的指令。输出机构可以包括触觉输出(例如振动、音频输出)和/或视觉输出(例如闪光灯、闪烁的颜色等)。可以根据来自框205的信息来根据乘员选择一个或多个输出机构。例如,听力困难的乘员可能具有更强的振动输出,而另一乘员可能更喜欢视觉输出。优选地,计算设备105可以被编程(例如包括设置框220的阈值)以在由常规的车道通过控制器107(例如通过致动车灯、声音、制动等来对即将发生的碰撞提供指示或做出反应的系统)执行警报、警告、或回避动作之前致动可穿戴设备输出。因此在本系统100的情境中,车辆101的驾驶员可以比当前存在的系统更早地和更安全地采取回避和/或避免动作。
[0021]接下来在框235中,计算设备105确定车道检测警告控制器107是否检测到即将到来的偏离车道(即车辆101将在接下来的几秒内从当前车道移动到另一车道)。LDW系统使用数据115来确定车辆101是否即将离开车道。如果LDW控制器107未检测到即将离开,则过程200结束。否则,过程200进行到框240。
[0022]在框240中,计算设备105使用LDW控制器107激活一个或多个第二输出机构(即车辆警报)并且过程200结束。第二输出机构可以包括例如振动方向盘、通过车辆扬声器发出的警报、仪表板上的闪光灯等。
[0024]计算设备通常各自包括可由一个或多个计算设备(例如上述确定的那些计算设备)执行的指令,并且执行上述过程的框或步骤。计算机可执行指令可以由使用各种编程语言和/或技术(包括但不限于单独使用或组合使用的JavaTM、C、C++、Visual Basic、JavaScript、Perl、HTML等)创建的计算机程序进行编译或解释。通常,处理器(例如微处理器)例如从存储器、计算机可读介质等处接收指令并执行这些指令,从而执行包括本文所述的一个或多个步骤的一个或多个过程。可以使用各种计算机可读介质来存储并发送这种指令和其它数据。计算设备中的文件通常是存储在计算机可读介质上的数据集合,例如存储装置介质、随机存取存储器等。
[0025]计算机可读介质包括参与提供可由计算机读取的数据(例如指令)的任何介质。这种介质可以采取许多形式,包括但不限于非易失性介质、易失性介质等。非易失性介质包括例如光盘或磁盘、以及其它持久存储器。易失性介质包括通常构成主存储器的动态随机存取存储器(DRAM)。计算机可读介质的常见形式包括例如软盘、可折叠磁盘、硬盘、磁带、任何其它磁介质、CD-ROM、DVD、任何其它光学介质、穿孔卡片、纸带、任何其它设有孔的物理介质、RAM、PROM(可编程只读存储器)、EPROM(可擦可编程只读存储器)、闪存EEPROM(电可擦可编程只读存储器)、任何其它存储器芯片或盒式磁盘、或计算机可读取的任何其它介质。
[0026]关于本文所述的媒介、过程、系统、方法等应当理解,尽管已经将这些过程的步骤等描述为根据某个有序序列发生,但是可以使用除本文所述的顺序之外的顺序来执行描述的步骤来实施这些过程。还应当理解,可以同时执行某些步骤,可以添加其它步骤,或者可以省略本文描述的某些步骤。例如,在过程200中,可以省略一个或多个步骤,或者可以以与图2所示不同的顺序执行步骤。换言之,出于说明某些实施例的目的而提供本文的系统和/或过程描述,并且不应将其解释为限制所公开的主题。
[0027]因此应当理解,包括上述描述和附图和以下权利要求的本公开旨在说明性而非限制。在阅读上述描述之后,除所提供的实施例之外的许多实施例和应用对本领域技术人员而言将是显而易见的。本发明获得权利的范围不应该是参考上述描述确定,而应该参照所附权利要求和基于本发明的非临时专利申请中包括的权利要求、以及这些权利要求的等同物的全部范围来确定。可以预计和意在将在本文讨论的领域中发生的未来的发展,并且所公开的系统和方法将并入到未来的实施例中。总之应该理解,所公开的主题能够修改和变化。