201202667 六、發明說明： 【發明所屬之技術領域】 本發明係關於用於顯示道路或路徑資訊之類型的數位地 圖’且更特定言之’係關於一種用於判定諸如由渡船運輸 之機動車輛之被護送物件的障礙物穿越排程、穿越時間及/ 、 或位置之方法。 【先前技術】 個人導航裝置利用與來自GPS之準確定位資料或其他資 料流組合的數位地圖。數位地圖亦可由個人電腦、行動裝 置及其他系統存取。此等裝置已針對許多應用（諸如，汽 車司機之導航輔助）而開發。此等導航系統之效用固有地 取决於儲存於其|己憶體中或經由一合適資料庫連接（諸 如，無線信號、纜線 '電話線等）以其他方式存取之數位 地圖的準確度。 圖1所示之導航系統1 〇包括一顯示螢幕丨2，其將一儲存 之數位地圖之一部分描繪為道路丨4之一網。一能夠存取一 具備GPS功能之導航裝置1〇之行進者接著可大致上接近於 或關於一特定道路14或該道路之區段而位於該數位地圖 . 上。一些具備GPS功能之導航裝置1〇(如T〇mT〇m Nv • (www.t〇mt〇m.C〇m)製造的若干型號之導航裝置）亦可經組 態為探測器而以規則（或有時不規則）間隔被動地產生地理 位置置測點。此等記錄有時被稱為軌跡資料且包含以（例 如）兩秒的間隔記錄之一序列地理編碼位置。當然，其他 合適裝置可用以產生軌跡資料’該等裝置包括手持型裝201202667 VI. Description of the Invention: [Technical Field of the Invention] The present invention relates to a digital map of the type used to display road or route information 'and more specifically' relates to a motor vehicle for determining transportation such as by a ferry. The method by which the obstacle of the escorted object traverses the schedule, the transit time, and/or the position. [Prior Art] A personal navigation device utilizes a digital map combined with accurate positioning data from GPS or other data streams. Digital maps can also be accessed by personal computers, mobile devices and other systems. These devices have been developed for many applications, such as navigation aids for car drivers. The utility of such navigation systems inherently depends on the accuracy of the digital maps stored in their memory or otherwise accessed via a suitable database connection (e.g., wireless signals, cable 'telephone lines, etc.). The navigation system 1 shown in Fig. 1 includes a display screen 2 which depicts a portion of a stored digital map as a network of roads 4. A traveler capable of accessing a GPS enabled navigation device 1 can then be located substantially close to or on a particular road 14 or a section of the road located on the digital map. Some GPS-enabled navigation devices (such as T〇mT〇m Nv • (www.t〇mt〇mC〇m) of several types of navigation devices) can also be configured as detectors with rules (or Sometimes irregular) intervals passively generate geographic location points. These records are sometimes referred to as trajectory data and contain a sequence of geocoded locations recorded at (e.g., two second intervals). Of course, other suitable devices can be used to generate trajectory data'
S 149348.doc 201202667 置、打動電話及其類似者。因此，軌跡資料可被描述為關 於一車輛（或一攜帶探測器之人）之移動的資訊之一集合，P 其含有標記時間之地理位置（xyz座標）且亦可能含有中繼資 料（車輛速度、接收器類型、車輛類型等）。 已知為了逐漸增加地產生及/或更新數位地圖而收集探 測器量測。如此產生之執跡資料可經由無線（例如，蜂巢 式）傳輸、經由網際網路上載或藉由其他習知方法在運行 中（on-the-fly)或隨後傳輸至一收集服務或其他地圖資料分 析服務。網際網路上載可經同步以與導航裝置使用者可作 為一服務獲得之數位地圖更新一同發生。根據軌跡資料之 集合，可推斷道路幾何形狀且可藉由適當分析方法導出其 他特徵及屬性。 ^ 一延長時間段中的來自橫穿一數位地圖之一特定部分的 複數個探測器之軌跡資料之典型集合可含有上億個離散資 料點，每一者經地理編碼且標記有時間。隨著時間收集之 奴測跡線可根據匹配於該數位地圖之一公用區域之探測跡 線分組或集束，且接著重疊以便由地圖資料庫編輯器解 «睪。編輯器使用各種數學及統計技術判定或推斷道路幾何 形狀’計算速度分佈圖、加速度分佈圖、行進方向、高 度’偵測道路網之變化以比較兩個道路網，以及許多其他 規格。 數位地圖提供者繼續努力以改良及更新其地圖。舉例而 ° 不準確之資料可能不適用於回應於一導航查詢計算最 佳路線’或將其他可靠資訊提供給一行進者。一數位地圖 149348.doc 201202667 中所含之不準確或不完整資訊可導致不良或錯誤的導航指 令且導致不當的導航決策。 ‘航決策必須考慮給行進造成障礙物的地形之自然特 徵。舉例而t· ’河流表示對車輛行進以及行人及腳踏車行 進之約束。通常，河流僅可借助於輪渡、橋樑或隨道來穿 越。橋樑、輪渡或隨道之存在或不存在構成將記錄於一數 位地圖中之重要細節。同樣地，車輛歷史上穿越一障礙物S 149348.doc 201202667 Set, call and similar. Thus, the trajectory data can be described as a collection of information about the movement of a vehicle (or a person carrying a detector), which contains the geographical location of the marked time (xyz coordinates) and may also contain relay data (vehicle speed) , receiver type, vehicle type, etc.). It is known to collect probe measurements in order to incrementally generate and/or update digital maps. The resulting profile data may be transmitted over the air (eg, cellular), uploaded via the Internet, or otherwise (on-the-fly) or subsequently transmitted to a collection service or other map material. Analysis services. The internet upload can be synchronized to occur with a digital map update that the navigation device user can obtain as a service. Based on the set of trajectory data, road geometry can be inferred and other features and attributes can be derived by appropriate analysis methods. ^ A typical set of trajectory data from a plurality of detectors traversing a particular portion of a digital map over an extended period of time may contain hundreds of millions of discrete data points, each geocoded and time stamped. The slave traces collected over time can be grouped or bundled based on probe traces that match a common area of the digit map, and then overlapped to be interpreted by the map repository editor. The editor uses various mathematical and statistical techniques to determine or infer the road geometry 'calculate velocity profile, acceleration profile, direction of travel, altitude' to detect changes in the road network to compare two road networks, and many other specifications. Digital map providers continue to work to improve and update their maps. For example, ° inaccurate data may not be used to calculate the best route in response to a navigation query' or to provide other reliable information to a traveler. A digital map 149348.doc Inaccurate or incomplete information contained in 201202667 can result in bad or incorrect navigation instructions and lead to improper navigation decisions. ‘Aircraft decisions must consider the natural characteristics of the terrain that creates obstacles for travel. For example, the t· 'river indicates the constraints on the travel of the vehicle and the pedestrians and bicycles. Usually, rivers can only be worn by means of ferries, bridges or accompanying tracks. The presence or absence of a bridge, ferry or accompanying path constitutes an important detail that will be recorded in a digital map. Similarly, the vehicle has historically crossed an obstacle
之一重要細節 在一些障礙物特徵（其不僅包括水體障礙物，而且包括 鐵路道口及許多其他障礙物類型）之情況下通常可能使 車辆或其可移動物#聚集且接著週期性地在€送下將該 等物件運輸;k越該障礙物至另—側。對於穿越水體障礙物 之機動車輛，渡船可按—完全可預測之時間排程運輸一組 機動車輛橫越該水體障礙物。其他障礙物穿越情形可由熟 習此項技術者辨識，在該等情形中，車輛或其他物件以二 規則循環方式在護送下移動橫越該障礙物。 稭由簡單地匹配軌跡與一預先存在之水地圖或數位道路 地圖來偵測障礙物穿越位置（諸如，輪渡穿越）在該數位地 圖不忐首先辨識一輪渡穿越時無效。該偵測在該數位地圖 (例如，水地圖）不夠準確時同樣無效。已提出用於使用基 於距離量測而叢集許多軌跡之高計算費用方法來偵測多組 移動中物件及軌跡之技術。舉例而言，在 the VLDB Endowment，第一卷第一期（2〇〇8 年 8 月），會 期：Spatial and Motion Data，第 1068_1〇8〇頁（ISSN:215〇- s 149348.doc 201202667 ’）之Η· Jeung等人之著作「〇_ 〇fh Trajectory Databases」中提出一種此類方法。然而，該等 方法展示為在尋找護送（亦即，尋找已在—起移動一時間 段之物件群組）上為低效率的。此外，先前技術中可用的 技術未利用特定類型之障礙物穿越（諸如，渡船及其他受 控穿越位置）之重複日常性質來偵測此等意義明確且可能 最不複雜之類型的護送且接著外推出發時間表及穿越頻 率。 因此，此項技術中需要尋找歷史軌跡資料以識別特定類 型之障礙物穿越（諸如，渡船及其類似者），其中被護送物 件（諸如，車輛）被週期性且可預測地護航越過一障礙物。 【發明内容】 本發明係關於用於使用歷史軌跡資料判定所護送物件之 障礙物穿越資訊之方法。該方法能夠關於障礙物穿越排 私、穿越時間及/或出發位置而評估收集之軌跡資料。本 發明之方法包括一數位地圖，其具有彼此由一障礙物所分 開之第一路段及第二路段。該第一路段與該第二路段之間 的分隔距離包含一障礙物弧長。記錄來自穿越該第一路段 與該第二路段之間的該障礙物之複數個物件的隨著時間之 軌跡°每一軌跡包含循序的地理位置及時間戳記資料。將 所記錄的具有類似地理及方向性質之軌跡集束在一起。量 測該集束之密度作為位置及時間記錄之一函數。根據本發 明’可接著基於該集束之軌跡密度之量測的變化來判定所 遵送物件之障礙物穿越資訊。該障礙物穿越資訊包括下列 149348.doc 201202667 各者中之至少-者：-出發位置、-穿越時間表、一穿越 頻率、一穿越行進時間及一穿越速度。 本發明之原理可用以（例如）在一數位地圖中當前不存在 ^ 障礙物的情況下，有效地定位障礙物道口，諸如輪渡道 口、鐵路道α及特定升降橋道口。此外，f越排程或時間 表可自軌跡密度之量測的變化以及穿越頻率資訊及穿越時 間細節導出，其全部可對導航及路線選擇用途具有極高價 值。 【實施方式】 本發明之此等及其他特徵及優點將在結合以下實施方式 及隨附圖式考慮時變得更容易瞭解。 參看諸圖’其中相同數字在若干視圖中始終指示相同或 對應部分，本發明係關於由導航系統使用之數位地圖，以 及其他地圖應用，該等地圖應用可包括經由具備網際網路 功能之電腦、個人數位助理（PDA)、蜂巢式電話、可攜式 導航裝置、嵌人式（in_dash)車载導航系、、统及其類似者可見 的彼等地圖應用。 圖2以高度簡化之形式描繪一水體障礙物，其呈河流、 • 湖泊或海洋16之形式。此水體障礙物16僅為一類型之交通 障礙物之一實例；非水體類型之障礙物亦預期在障礙2之 寬廣定義内且可包括山谷、鐵路道口、建設/檢修項目， 及週期性地阻止或限制機動車輛或其他所護送物件沿著一 車行道或其他行進路徑自由流動的其他特徵。第一路段U 及第二路段20包含一數位地圖令所含之總道路網〗4之路 149348.doc 201202667 段，但其特別位於障礙物16之鄰接側。通常，路段18、2〇 可為能夠支援車輛交通流之類型，然而本發明之原理同樣 可適用於腳踏車及行人路徑，以及可沿著其運輸任何物件 之其他行進路徑。汽車22經展示為進入輪渡24以橫穿障礙 物16到達第二路段2〇。輪渡24表示汽車。成群組地或以護 送形式橫越障礙物16之一運輸模式。然而，輪渡24僅 於障礙物穿越情況下的一實一例。在一些情況下，機動車 輛22可依靠自身動力穿越一障礙物。舉例而言，在予橋的 情況下，車輛22將在吊橋升起時聚集在路段18、2〇申之一 者上，且接著在吊橋放下時依靠自身動力作為護送（至少 在最初時）前進橫越障礙物16。 圖3表示來自一例示性數位地圖之描繪，其中第一路段 18及第二路段2〇彼此由障礙物16所分開，在此例子中，該 障礙物為河流。虛線24在此實例中表示一輪渡穿越。第一 路段18與第二路段20之間的分隔距離包含一障礙物弧長 (L)。在此情況下，該障礙物穿越之終點％表示輪渡出發 位置，其分別連接至第一路段18及第二路段2〇。因此車 輛在終點26上船及自輪渡24上岸。隨時間過去，可使用 (例如）上述之探測記錄技術收集來自穿越障礙物16之複數 個車輛22的軌跡28 ^作為一簡化實例，圖4展示疊加在圖3 之數位地圖影像上的軌跡資料28之一取樣。此等執跡“可 表不自第一路段18穿越至第二路段2〇，或相反地自第二路 段2〇穿越至第一路段丨8之車輔。 對本發明而言，將具有類似地理及方向性質之軌跡28集 149348.doc 201202667One important detail is that in the case of some obstacle features, including not only water body obstacles, but also railroad crossings and many other obstacle types, it is often possible for the vehicle or its movable object to aggregate and then periodically at € Send the items to transport; k the obstacle to the other side. For motor vehicles that traverse waterborne obstacles, the ferry can transport a group of motor vehicles across the body of the water at a fully predictable time schedule. Other obstacle crossing situations may be recognized by those skilled in the art, in which case the vehicle or other item moves across the obstacle under escort in a two-cycle fashion. The straw is ineffective by simply matching the trajectory with a pre-existing water map or digital road map to detect obstacle crossing locations (such as ferry crossings) when the digital map first identifies a ferry crossing. This detection is also invalid when the digital map (for example, a water map) is not accurate enough. Techniques have been proposed for detecting multiple sets of moving objects and trajectories using a high computational cost method of clustering many trajectories based on distance measurement. For example, in the VLDB Endowment, Volume 1 Phase 1 (August 8 August), Session: Spatial and Motion Data, Pages 1068_1〇8〇 (ISSN: 215〇-s 149348.doc 201202667 ') Η · Jeung et al.'s book "〇_〇fh Trajectory Databases" proposes such a method. However, these methods are shown to be inefficient in finding escorts (i.e., looking for groups of objects that have been moving for a period of time). Moreover, the techniques available in the prior art do not utilize the repetitive everyday nature of certain types of obstacle crossings, such as ferries and other controlled crossing locations, to detect such ambiguous types that are clear and perhaps the least complex and then Launch schedule and crossover frequency. Therefore, there is a need in the art to find historical trajectory data to identify specific types of obstacle crossings (such as ferries and the like) in which escorted objects, such as vehicles, are periodically and predictably escorted across an obstacle. . SUMMARY OF THE INVENTION The present invention is directed to a method for determining obstacle traversal information for an escorted object using historical trajectory data. The method can evaluate the collected trajectory data as the obstacle passes through the smuggling, transit time, and/or departure location. The method of the present invention includes a digital map having a first road segment and a second road segment separated from each other by an obstacle. The separation distance between the first road segment and the second road segment includes an obstacle arc length. A track of time over a plurality of objects from the obstacle between the first road segment and the second road segment is recorded. Each track includes sequential geographic location and time stamp data. The recorded trajectories with similar geographic and directional properties are bundled together. The density of the bundle is measured as a function of position and time recording. The obstacle traversal information of the conforming object can then be determined in accordance with the variation of the measured trajectory density of the bundle according to the present invention. The obstacle crossing information includes at least one of the following: 149 348.doc 201202667: - starting position, - crossing schedule, a crossing frequency, a crossing travel time, and a crossing speed. The principles of the present invention can be used to effectively locate obstacle crossings, such as ferry crossings, railway lanes a, and specific lifting bridge crossings, for example, in the absence of an obstacle in a digital map. In addition, the more schedules or schedules of f can be derived from measurements of track density and derived from crossover frequency information and transit time details, all of which are extremely valuable for navigation and route selection purposes. [Embodiment] These and other features and advantages of the present invention will become more readily apparent in the light of the <RTIgt; Referring to the drawings, wherein like numerals refer to the same or corresponding parts throughout the several views, the present invention relates to digital maps used by navigation systems, and other map applications, which may include computers via Internet-enabled functions, Personal digital assistants (PDAs), cellular phones, portable navigation devices, in-dash car navigation systems, and their similar map applications. Figure 2 depicts a water body obstruction in the form of a river, lake or ocean 16 in a highly simplified form. This water body obstacle 16 is only one example of a type of traffic obstacle; non-aqueous type obstacles are also expected to be within the broad definition of barrier 2 and may include valleys, railroad crossings, construction/overhaul items, and periodic blocking Or other features that restrict the free flow of a motor vehicle or other escorted item along a roadway or other path of travel. The first road segment U and the second road segment 20 contain a road map of the total road network _4 149348.doc 201202667 segment, but it is particularly located on the adjacent side of the obstacle 16. Typically, the sections 18, 2A can be of a type that can support vehicle traffic flow, although the principles of the present invention are equally applicable to bicycles and pedestrian paths, as well as other travel paths along which any item can be transported. The car 22 is shown entering the ferry 24 to traverse the obstacle 16 to the second section 2〇. Ferry 24 represents a car. A mode of transport across one of the obstacles 16 in groups or in escort form. However, the ferry 24 is only a real case in the case of obstacle crossing. In some cases, motor vehicle 22 may rely on its own power to traverse an obstacle. For example, in the case of a pre-bridge, the vehicle 22 will gather on one of the sections 18, 2 when the suspension bridge is raised, and then rely on its own power as an escort (at least initially) when the suspension bridge is lowered. Cross the obstacle 16. Figure 3 shows a depiction from an exemplary digital map in which the first road segment 18 and the second road segment 2 are separated from each other by an obstacle 16, which in this example is a river. The dashed line 24 represents a ferry crossing in this example. The separation distance between the first road segment 18 and the second road segment 20 includes an obstacle arc length (L). In this case, the end point of the obstacle crossing indicates the ferry departure position, which is connected to the first section 18 and the second section 2, respectively. Therefore, the vehicle boarded at the terminal 26 and landed on the ferry 24. Over time, the trajectory 28 from a plurality of vehicles 22 crossing the obstacle 16 can be collected using, for example, the above-described probe recording technique as a simplified example, and FIG. 4 shows trajectory data 28 superimposed on the digital map image of FIG. One of the samples. Such manifestations "may not pass from the first road segment 18 to the second road segment 2〇, or conversely from the second road segment 2〇 to the first road segment 丨8. For the purposes of the present invention, there will be similar geography And the nature of the direction of the trajectory 28 set 149348.doc 201202667
使用一預先存在之幾何形狀作為藉由氐配軌跡28與一預先 存在之數位地圖而選擇之一集束來實現。在又一方法中， 一合適之平均演算法可用以導出該等集束28，。不管所選擇 之特定技術如何，該等集束28ι表示隨後將被分析以用於穿 越貧訊之特徵。更具體言之，對應於一障礙物穿越之該等 集束之部分或長度可加以適當標記。一旦經標記，集束28, 内所含（所表示）之該等個別軌跡28亦可用呈屬性形式之障 礙物穿越資訊標記。 如先Θ所說明’每一執跡包含地理位置及時間戳記記錄 之一序列’其表示物件22(諸如，一車輛）沿著車行道14之 移動。軌跡28表示每一車輛22之此等位置及時間記錄之跡 線。集束28'又表示具有類似地理及方向性質之軌跡28隨時 間之集合。圖6A展示一來自數位地圖之例示性區段，其中 149348.doc -9- 201202667 水體障礙物16使在一大型水體特徵之相對側上的第一路段 18與第二路段20分離。若在單-時間片段(例如，時間tl) 中檢視該等執跡集束Μ,，則每一各別轨跡Μ所表示的地理 位置記錄之集合有時可將某物呈現為資料點之一叢集或 ^此處展不為聚集在各別路段1 8、20之出發點或終點26 周圍此A纟會象徵汽車在其登上位於水體障礙物丄6之相對 側的渡身。時在時間t2 ’如圖6B所示，各別渡船將開往對 岸。該等分組資料點指示車輛22(未圖示）在其由該等各別 渡船運輸時的個別位置記錄。在此等圖中，沿著該等軌跡 市束28之任何位置由字母s表示，障礙物穿越之距離或弧 長（含於集束28,内）由字母L表示。 圖6C表示在時間t2(圖6B)之後的時間片段〇，且展示軌 跡集束28’中所含之各別資料點向著障礙物16之另一側漸進 地移動更多。圖6D取自時間t4 ’此時兩艘渡船彼此經過且 其各別貧料點看上去非常接近。圖6E及圖6F描繪〖5及“之 各別時間片段，展示了所護送物件22之進一步移動。在圖 6G中，该等渡船已到達對岸且車輛22開始上岸。在時間 t8，如圖6H所示，該等車輛已離開各別渡船碼頭，且已開 始沿著各種路段駛離。因此，可展示先前實例中所描述之 海上輪渡24以展現一極典型行為，因為其將同時處於類似 位置之大量車輛22聚合，而在其他時間，輪渡路線上沒有 或僅存在少量車輛22 ^此外，輪渡穿越以及許多其他類型 之障礙物穿越系統在諸多日子中常常展示重複行為，從而 使此行為在執跡28經多曰累積後更明顯。另一方面，在習 149348.doc -10- 201202667 知車行道上’即使考慮了重複交通堵塞，移動中車輛22在 時間及地點上更加分散。 許多類型之障礙物穿越之重複行為可藉由量測集束沈 之軌跡密度作為位置及時間之—函數來描述。術語「密 度」在此指代集束28,中所含的在一特定位置⑷及時間⑴ 之個別軌跡28資料點之數目。或者，術語「密度」可指代 在一特定位置⑷及時間⑴之個別跡線28之數目。圖7展示 對應於圖6A至圖6H中之實例的樣本位置_時間（s，〇圖。在 此根據一灰度階圖例從強度上來描繪軌跡密度。換言之， Μ ^ -肖$位置及冑間之f料點或軌跡之數目大於 5〇(—針對此實例任意選擇之數字）時，指示一最大強度或 白色。相反地，當在一特定點8在一特定時間【沿著障^物 16之弧長不含有資料點或執跡資料時，指示一黑色強度。 因此，在圖6Α至圖6Η中所表示的該等時間片段中之^ 一 者，咼強度跡線點將出現以與所示之構成資料點之叢集或 群組-致。如此處可見，汽車22或多或少不變地到達及離 開第一路段18及第二路段20,如對所有時間⑴都實質平滑 之灰度值所表示。然而，輪渡穿越為清楚可辨別的，且在 一些時間點沒有汽車22且在其他時間段可識別所有汽車。 此為特定時間片段之黑色強度影像與白色強度影像之間的 顯著差異。若該強度圖係經過—整天時間外推，則_, 較大交通量在顯露最高強度之日子期間出⑨。在進入/退 出位置26時’車輛緩慢行驶且因此解釋鄰近障礙物穿越之 終點26的所有時間t之較大強度。然％，請注意，若密度 £ 149348.doc 201202667 係自跡線之數目而非資 點_所有時= 鄰近障礙物穿越之终 圖，可直接識別一護送穿：、不明顯。根據该強度 (亦即，出發排程'穿越頻;=穿以及一穿越時間表 ^ _ 貝半及甚至穿越行進時間）。舉例 =二Γ中之行進時間可取為_之間或"與-之 、、.跨度。此為輪渡離開—個路段與輪渡到達障礙 物16之相對側上之另—路段之__。 現參看圖8及圖9,可繪製隨著時間的集束密度之變化之 ㈣圖以向數位地圖編輯器顯示重要資訊。圖8表示自沿 耆一例不性車行道（諸如，可在道路網"中之一主要或次 要道路上發現之車行道）行進之車輛收集的資料4束密 度通常在交通高岭時間期間逐漸增加，且在較少行進時間 期間逐漸穩定至較低密度。然而，圖9表示沿著集束28ι之 例不性位置’自該圖可容易觀測到急劇的車輛突增。該 等車輛突增包含在一天中週期性地出現之猛烈且重複之密 度變化。自零至實質上大於零之此等急劇密度變化指示 (諸如）先前實例中所描述之渡船的護送穿越。藉由分析集 束28'之軌跡密度隨著時間之變化的週期頻率，可進行有用 分析以用於增強一數位地圖。 如藉由比較圖8與圖9可見，道路（在該道路上移動令物 件（亦即，車輛）密度在一天中缓慢改變）主要具有低頻率分 夏’此意謂著時間序列面線之軌跡密度緩慢改變。此甚至 對曰常的重複堵塞情況成立。然而，集束28,（其中一特定 位置處之車輛密度在一天中幾次突然自零變至實質上大於 149348.doc •12· 201202667 零（亦即，車輛突增））將具有至少一相當大的頻率分量。一 時間序列的此類型之頻率分析之結果可用如圖1〇A至1〇c 所示的頻率圖或振幅頻譜視覺化。振幅頻譜中之頻率峰值 (近似0 Hz之DC分量除外）將與障礙物穿越頻率一致。因 此，圖10A表示一開放路段上之軌跡資料之一集合，展示 了很小至不明顯的頻率峰值。然而，可在—下降點或終點 26處識別近似〇.1及〇·2 Hz之頻率峰值，如圖1〇B所示。圖 10C表示沿著障礙物穿越之一（多個）點，自該圖可觀測到 振幅頻譜中之大頻率峰值。基於頻率分佈之形狀，接著可 做出有關集束28之該（該等）點處之軌跡之輪渡穿越特性的 決策。 可設計各種準則以進行此決策。作為一實例，其在圖u 中以圖形描繪，所使用之準則c可為兩個最高頻率峰值之 間的比率，如公式所表示： C=A(Vl) / A(v〇) 其中ν〇=振幅頻譜中之最高峰之頻率 ν!=振幅頻譜中之第二最高峰之頻率，及 A(v)=頻率v在振幅頻譜中之振幅的絕對值。 使用此方法’具有平滑交通增加之道路應具有在0且在 任何情況下實質上小於丨之範圍内的準則^值。然而，輪渡 穿越及其他障礙物穿越方法(所護送物件22經由該等穿越 方法移動）應具有遠大於且更接近值i之準則c值。可設定 值C之一臨限值以判定沿著一對應於障礙物穿越之集束之 位置。在此實例中’選擇一臨限值〇3且該臨限值由圖^ 149348.docThe use of a pre-existing geometry is achieved by selecting one of the bundles by matching the trajectory 28 with a pre-existing digital map. In yet another method, a suitable average algorithm can be used to derive the bundles 28. Regardless of the particular technology selected, the bundles 28i represent features that will subsequently be analyzed for cross-learning. More specifically, the portion or length of the bundles corresponding to an obstacle crossing may be appropriately labeled. Once marked, the individual tracks 28 contained within (represented) bundles 28 may also traverse the information mark with an obstacle in the form of an attribute. As explained above, each of the tracks includes a sequence of geographic locations and time stamp records that represent the movement of the object 22 (such as a vehicle) along the roadway 14. Trace 28 represents the trace of such position and time records for each vehicle 22. Cluster 28' again represents a collection of trajectories 28 that have similar geographic and directional properties. Figure 6A shows an exemplary segment from a digital map, wherein 149348.doc -9-201202667 water body obstacle 16 separates first road segment 18 from second road segment 20 on opposite sides of a large water feature. If the forbidden bundles are viewed in a single-time segment (eg, time t1), then the set of geographic location records represented by each individual track may sometimes present something as a data point. The cluster or the exhibition here is not gathered around the departure point or the end point 26 of the respective road section 18.20. This A纟 will symbolize the car's crossing on the opposite side of the water obstacle 丄6. At time t2', as shown in Fig. 6B, the respective ferries will be driven to the opposite bank. The grouping data points indicate that the vehicle 22 (not shown) is recorded at an individual location when it is transported by the respective ferry boats. In these figures, any position along the trajectory 28 is indicated by the letter s, and the distance or arc length (inclusive of bundle 28) through which the obstacle traverses is indicated by the letter L. Fig. 6C shows the time segment 之后 after time t2 (Fig. 6B), and the individual material points contained in the display track bundle 28' are progressively moved more toward the other side of the obstacle 16. Figure 6D is taken from time t4' where the two ferries pass each other and their respective poor points look very close. Figures 6E and 6F depict respective time segments of "5" showing further movement of the escort article 22. In Figure 6G, the ferries have reached the opposite shore and the vehicle 22 begins to land. At time t8, as shown in Figure 6H As shown, the vehicles have left the individual ferry docks and have begun to travel along various sections. Thus, the marine ferry 24 described in the previous example can be shown to exhibit a typical behavior as it would be in a similar position at the same time. A large number of vehicles 22 are aggregated, while at other times there are no or only a small number of vehicles on the ferry route. 22 In addition, ferry crossings and many other types of obstacle crossing systems often exhibit repetitive behavior on many days, thereby causing this behavior to be enforced. Trace 28 is more pronounced after multiple accumulations. On the other hand, on the road 149348.doc -10- 201202667, even if repeated traffic jams are considered, the moving vehicles 22 are more dispersed in time and place. The repetitive behavior of obstacle crossing can be described by measuring the trajectory density of the bundle as a function of position and time. The term "density" is used herein to refer to The number of data points of individual tracks 28 at a particular location (4) and time (1) contained in cluster 28. Alternatively, the term "density" may refer to the number of individual traces 28 at a particular location (4) and time (1). Figure 7 shows the sample position_time (s, 〇 map corresponding to the example in Figures 6A to 6H. Here, the trajectory density is drawn from the intensity according to a gray scale legend. In other words, Μ ^ - 肖$ position and daytime When the number of f points or tracks is greater than 5 〇 (- for any arbitrarily selected number for this example), a maximum intensity or white is indicated. Conversely, at a particular point 8 at a particular time [along the obstacle 16 When the arc length does not contain data points or trace data, it indicates a black intensity. Therefore, in the time segments shown in Figure 6Α to Figure 6Η, the intensity trace points will appear as shown A cluster or group of constituent data points. As can be seen here, the car 22 reaches or leaves the first road segment 18 and the second road segment 20 more or less invariably, such as a substantially smooth gray value for all times (1). This is indicated. However, the ferry crossing is clearly discernible, and at some point in time there are no cars 22 and all cars can be identified at other times. This is a significant difference between the black intensity image and the white intensity image for a particular time segment. If the intensity After the extrapolation of the whole day, then _, the larger traffic volume is out during the day when the highest intensity is revealed. 9. At the entry/exit position 26, the vehicle travels slowly and thus explains the time of the end point 26 of the adjacent obstacle crossing. The greater strength of t. However, if the density is £ 149348.doc 201202667 is the number of traces instead of the point of funding _ all time = the map of the adjacent obstacle crossing, can directly identify a escort:: no Obviously. According to the intensity (that is, the departure schedule 'crossing frequency; = wearing and one crossing schedule ^ _ half and even crossing the travel time). Example = travel time in the second can be taken as _ between or " And -., span. This is the __ of the other section of the ferry leaving the road section and the ferry reaching the opposite side of the obstacle 16. Referring now to Figures 8 and 9, the cluster density can be plotted over time. The (4) map of the change is used to display important information to the digital map editor. Figure 8 shows an example of a roadway (such as a roadway that can be found on one of the primary or secondary roads in the road network). Information collected by the traveling vehicle 4 bundles It usually increases gradually during the traffic kaolin time and gradually stabilizes to a lower density during less travel time. However, Figure 9 shows an example of the position along the bundle 28'. From this figure, it is easy to observe a sharp vehicle burst. These vehicle bursts include violent and repeated density changes that occur periodically throughout the day. These sharp density changes from zero to substantially greater than zero indicate, for example, the escorting crossing of the ferry described in the previous example. By analyzing the periodic frequency of the trajectory density of the bundle 28' as a function of time, a useful analysis can be performed for enhancing the one-digit map. As can be seen by comparing Figures 8 and 9, the road (moving on the road) The density of objects (ie, vehicles) changes slowly over the course of a day) mainly with low frequency scores. This means that the track density of the time series face line changes slowly. This is true even for the usual repeated blockages. However, cluster 28, (where the vehicle density at a particular location suddenly changes from zero to more than 149348.doc •12·201202667 zero (ie, vehicle spur)) will have at least a considerable Frequency component. The results of this type of frequency analysis of a time series can be visualized using a frequency map or amplitude spectrum as shown in Figures 1A through 1〇c. The frequency peak in the amplitude spectrum (except for the DC component of approximately 0 Hz) will coincide with the obstacle crossing frequency. Thus, Figure 10A shows a collection of trajectory data on an open road segment showing small to insignificant frequency peaks. However, frequency peaks of approximately 〇.1 and 〇·2 Hz can be identified at the -drop point or end point 26, as shown in Figure 〇B. Figure 10C shows one or more points along the obstacle crossing from which large frequency peaks in the amplitude spectrum can be observed. Based on the shape of the frequency distribution, a decision can then be made regarding the ferry crossing characteristics of the trajectory at the (the) point of the bundle 28. Various guidelines can be designed to make this decision. As an example, which is graphically depicted in Figure u, the criterion c used can be the ratio between the two highest frequency peaks, as expressed by the formula: C = A(Vl) / A(v〇) where ν〇 = the frequency of the highest peak in the amplitude spectrum ν! = the frequency of the second highest peak in the amplitude spectrum, and A(v) = the absolute value of the amplitude of the frequency v in the amplitude spectrum. Using this method 'the road with a smooth traffic increase should have a criterion value in the range of 0 and in any case substantially less than 丨. However, the ferry crossing and other obstacle crossing methods (the escoring object 22 moves via the traversing methods) should have a criterion c value that is much greater than and closer to the value i. One of the values C can be set to determine the position along a bundle that corresponds to the obstacle crossing. In this example, 'select a threshold 〇3 and the threshold is shown in the figure ^ 149348.doc
-13- S 201202667 t之水平虛線表示。在 J成水+虛線（亦即，臨限 值〇·3)與標準c曲線之相處， 处 7在3近似等於0.4、〇.9(正 規化）之弧長位置識別屮玖 1我初出終點26。在該臨限值之上，推斷 -障礙物穿越；在該臨限值之下，推斷路段18、2〇。然 而’如先前所建議，用於判定兩個最高振幅頻率峰值之間 、、率之C值之此特疋技術僅為_分辨輪渡穿越行為之 方法。此外，可提出許多擴展方法以（❹墙㈣ 附近的其他位置上之頻率分佈形狀進行穿越位置之決策來 改良此決策。熟習此項技術者亦將瞭解其他技術。 橫越一障礙物之護送出發之頻率（例如，指示輪渡航行 之頻率）可直接自圖9之振幅頻譜或自圖7之強度圖瞭解。 當使用圖9之振幅頻譜時，若一大尖峰以頻率心存在，則 此意謂著每一船卜"V】⑷存在執跡密度之週期性顯著增 加，從而意謂著輪渡每一個tl⑷航行一次。當然，可對每 一類型之日子(例如，冬季月份中之星期-、星期日等)進 行頻率分析’從而允許判定一年中之不同曰子、部分時 間、操作週期等之出發時間表。此使一數位地圖能夠用此 等屬性擴充’且因此為導航及路線規劃用途提供一價值極 向的資訊源。 根據圖7之位置-時間（s，t)軌跡圖，可以判定一輪渡穿越 之行進時間及行進距離。為了使用先前實例之環境，輪渡 穿越將表現為連接點（si，tl)與點（s2, t2)之高密度線路。行 進時間因而為（t2-tl)，且行進距離為（s2_sl)。如此計算出 之行進時間亦可用於導航及路線選擇用途。此外，若行進 149348.doc 201202667 時間及行進距離均已知 在二障况下，物件22之護送的平均穿 越速度（v)可為有用的。 J芽 • 因：’使用本發明之方法，沿著位置·時間㈣圖中之 :束的細資訊可被視覺化以評定特定位置在特定時間 段（例如，工作日對假日等）期間之軌跡密度或活動。 許多軌跡時間序列執行上述頻率分析之特定應用以藉由偵 測可月匕位置上之車輛突增來判定輪渡穿越位置。亦即，在 其他情:兄下無車輛存在的位置上之大量移動中車㈣突然 出現可藉由該等頻率分析技術容易地分辨。此外，此等頻 率分析技術對特定位置之軌跡密度時間序列之特定應用可 用以判定輪渡出發時間表以及輪渡航行頻率及其他類型之 §蒦送穿越情況。 已根據相關法定標準描述了前述發明’因此該描述本質 為Ή示I1 生# %非限制性的。削愚示實施例之改變及修 改可對熟習此項技術者而言變得顯而易見且屬於本發明之 範疇。 【圖式簡單說明】 圖1為根據本發明之-實施例之可攜式導航裝置的例示 J·生視圖β玄可攜式導航裝置包括一用於呈現地圖資料資訊 之顯示螢幕； 圖2為一水體障礙物的高度簡化正視圖，該水體障礙物 具有一連結其一側上之一路段至該障礙物之另一侧上之一 路段的輪渡服務； 149348.doc •15· £ 201202667 圖3為一數位地圖的視圖’其指示接合一水體障礙物之 任一側上之路段的輪渡穿越； 圖4為如圖3之疊加有來自所收集的探測資料之執跡資訊 (諸如，可結合本發明之方法使用的軌跡資訊）的視圖； 圖5也述可藉以將所記錄的具有類似地理及方向性質之 軌跡集束在一起以用於後續分析之一種方式； 圖6A至圖6H描繪障礙物穿越位置處之時序⑴至t8)，根 據其可將所護送物件之典型行為觀測為同時處於類似位置 之許多物件； 圖7為圖6A至圖6H令之例示性輪渡穿越在一選定時間排 程以16中的位置時間（s，t)圖之一實例，且其中軌跡密度 由灰度階強度指示； 圖8為沿著公路之例示性位置的例示性時間序列測繪軌 跡密度對時間； 圖9為如圖8中之密度對時間的圖，但其係關於如06八至 圖6H所示的沿著輪渡穿越之例示性位置； 圖10A至圖10C提供頻率分析目，其展示在公路上（圖 l〇A)、在穿越終點（亦即，出發點）處（圖剛及沿著道口 (圖10C)之位置的振幅頻譜對頻率；及 圖"為沿著該集束之弧長的曲線圖，其用於藉由 於兩個最高頻率峰值之間的比率之臨限準則C來識別障礙 物穿越之終點（亦即，出發點）。 【主要元件符號說明】 10 導航系統/具備GpS功能之導航裝置 149348.doc •16- 201202667 12 顯示螢幕 14 道路網/道路 16 河流、湖泊或海洋/障礙物 18 第一路段 20 第二路段 22 汽車 24 輪渡 26 終點 28 軌跡資料/跡線 28' 集束-13- S 201202667 t is indicated by the horizontal dotted line. In the J water + dashed line (that is, the threshold 〇 · 3) and the standard c curve, at 7 is approximately equal to 0.4, 〇.9 (normalized) arc length position identification 屮玖 1 I am the end point 26. Above this threshold, it is inferred that the obstacle traverses; below this threshold, the road segments 18, 2〇 are inferred. However, as previously suggested, this feature technique for determining the C value between the two highest amplitude frequency peaks is only a method of _resolving the ferry crossing behavior. In addition, a number of extension methods can be proposed to improve the decision by traversing the position of the frequency distribution shape at other locations near the wall (4). Those skilled in the art will also be aware of other techniques. The frequency (for example, indicating the frequency of ferry navigation) can be directly obtained from the amplitude spectrum of Figure 9 or from the intensity map of Figure 7. When using the amplitude spectrum of Figure 9, if a large peak exists in the frequency center, this means Each ship's "V" (4) has a significant increase in the periodicity of the strike density, which means that the ferry sails once every tl (4). Of course, for each type of day (for example, the week of the winter month -, Sunday, etc.) Perform a frequency analysis' to allow determination of the departure schedule for different dice, part of the time, operating cycle, etc. during the year. This allows a digital map to be augmented with these attributes' and thus provides for navigation and route planning purposes. A value source of information. According to the position-time (s, t) trajectory map of Figure 7, the travel time and travel distance of a ferry crossing can be determined. Using the environment of the previous example, the ferry crossing will appear as a high-density line connecting the point (si, tl) and the point (s2, t2). The travel time is thus (t2-tl) and the travel distance is (s2_sl). The travel time can also be used for navigation and route selection. In addition, if the travel time 149348.doc 201202667 time and travel distance are known to be in the second obstacle condition, the average travel speed (v) of the escort of the object 22 can be useful. J buds • Because: 'Using the method of the present invention, along the position/time (four) diagram: the detailed information of the bundle can be visualized to assess the trajectory of a particular location during a particular time period (eg, weekdays to holidays, etc.) Density or activity. A number of trajectory time series perform a specific application of the above frequency analysis to determine the ferry crossing position by detecting a sudden increase in the vehicle at the lunar position. That is, in other situations: where there is no vehicle present under the brother The sudden appearance of a large number of mobile vehicles (4) can be easily resolved by these frequency analysis techniques. In addition, the specific application of these frequency analysis techniques to the trajectory density time series at a specific location may be It is used to determine the departure schedule of the ferry and the frequency of ferry sailing and other types of traversing. The foregoing invention has been described in accordance with relevant statutory standards. Therefore, the description is essentially that I1 is not restrictive. Variations and modifications of the embodiments will become apparent to those skilled in the art and are within the scope of the invention. BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is an illustration of a portable navigation device in accordance with an embodiment of the present invention. The raw view β-portable navigation device includes a display screen for presenting map information information; FIG. 2 is a highly simplified front view of a water body obstacle having a link to one side of the body to Ferry service on one of the other side of the obstacle; 149348.doc •15· £ 201202667 Figure 3 is a view of a digital map indicating the crossing of a ferry crossing a section on either side of a water body obstacle; 4 is a view of FIG. 3 superimposed with trace information from the collected probe data, such as trajectory information that can be used in conjunction with the method of the present invention; FIG. 5 is also illustrated by A method of grouping recorded trajectories having similar geographic and directional properties together for subsequent analysis; FIGS. 6A-6H depict timings (1) to t8) at the obstacle crossing position, according to which the escorted object can be The typical behavior is observed as many objects that are in similar positions at the same time; Figure 7 is an example of the positional time (s, t) of an exemplary ferry crossing in a selected time schedule of 16 in Figures 6A-6H, and wherein The trajectory density is indicated by the gray scale intensity; Figure 8 is an exemplary time series mapping trajectory density versus time along an exemplary location of the highway; Figure 9 is a plot of density versus time in Figure 8, but with respect to, for example, 06 8 to 6H show an exemplary position along the ferry crossing; FIGS. 10A to 10C provide a frequency analysis target, which is displayed on the road (Fig. 1A), at the crossing end point (ie, the starting point) (Fig. The amplitude spectrum vs. frequency at the position along the crossing (Fig. 10C); and the graph " is the graph along the arc length of the bundle, which is used to account for the ratio between the two highest frequency peaks. Criterion C to identify obstacles Through the end (ie, the starting point). [Main component symbol description] 10 Navigation system / GpS-enabled navigation device 149348.doc •16- 201202667 12 Display screen 14 Road network/road 16 River, lake or ocean/obstacle 18 First road section 20 Second section 22 Car 24 ferry 26 end point 28 trajectory data / trace 28' cluster
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