Data processing: vehicles – navigation – and relative location – Vehicle control – guidance – operation – or indication – Traffic analysis or control of surface vehicle
Reexamination Certificate
2000-03-17
2002-11-12
Nguyen, Tan (Department: 3661)
Data processing: vehicles, navigation, and relative location
Vehicle control, guidance, operation, or indication
Traffic analysis or control of surface vehicle
C701S118000, C340S995190, C340S990000
Reexamination Certificate
active
06480783
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention describes the vehicle Guidance System consisting of a plurality of vehicles equipped with IMUs including GPS units and of the CTU computer. The CTU broadcasts the updated traffic data collected from a number of sample vehicles via Multicast Broadcasting System thereby enabling the IMUs to dynamically update the desired optimal travel routes.
2. Description of the State of Art
The conventional on-vehicle guidance systems are usually stand-alone applications wherein the traffic data are static and cannot be easily dynamically updated. Consequently, the proposed routes are accurate only under ideal traffic conditions. The stand-alone versions cannot take into account current traffic jam conditions or real time emergencies. Hence, even when a so-called ‘optimal route’ is found, it may not be usable solution in real time situations and can only be used as a general recommendation.
Other systems rely on electronic and optical sensors situated at various key locations to measure and update the current traffic loads. These systems are typically costly to install and to maintain and to be effective they must be distributed over large sectors of roads. Still other real time traffic control systems utilize real time field information typically gathered from various service vehicle such as traffic police, ambulances, road maintenance teams, etc. which is usually transmitted by radio to the control center and from there broadcasted to the public.
The present invention eliminates the need for a large number of static sensors and relies instead on a number of highly mobile GPS carrying sample vehicles. Naturally, the reliability of the results will depend on the size of the sample vehicle fleet, and the ability of the server to process the incoming information simultaneously. Similar ideas could be found in existing inventions. Of particular relevance is U.S. Pat. No. 5,699,056 wherein the problem statement is similar to ours. In particular, it formulates the problem of dealing with traffic jams and other bottleneck situations. For detecting traffic jams and representing them in its database, this patent uses data obtained from traveling vehicles, in particular their IDs, positions, times, and speeds. In Embodiment 2, it divides all the vehicles into blocks (i.e. groups), measures average speed in each block, and defines a situation as a jam if the average speed is less than a predetermined value.
In our view, the proposed there solution contains a number of problematic points that require further development and which remained unclear in the patent description. 1) The definition of blocks is not quite clear. No algorithm is given for partitioning the vehicles into blocks; 2) The number of roads or more precisely, sections of roads may be very large, say, tens of thousands. It may be difficult to cover them all, i.e. store all the relevant data, process and update it on-line; 3) The number of vehicles can also be very large, say, tens of thousands, so processing and updating in real time signals sent by them at time intervals of, say, a minute might be challenging for a server of average capacity; 4) An important point in his solution is evaluating vehicle speeds and averaging them over a block. This seemingly innocuous operation may highly problematic, however, within a traffic jam as many if not all speed measurements may return zero values. In other words, speed as a function of time may be wildly discontinuous and measuring it on time grid of a minute may prove highly inaccurate. Consider a case of growing jam. Average speeds will in this case give no indication of this dynamic change, the same being true of a dissipating jam. Averaging is a static operation incapable of catching a trend. Average speed is compared with a fixed constant while it might be better to compare it to “usual” speed empirically determined previously and stored in the database.
BRIEF DESCRIPTION OF THE INVENTION
This invention provides real time traffic Guidance System, which is capable of providing optimal route from the present position of a vehicle to a desired target destination when traffic jams may be present, thereby reducing the burden upon the driver when the vehicle is traveling at high speeds on unfamiliar roads. Thereafter the optimal route found is communicated to the driver and displayed on the vehicle's computer screen featuring the digital map of the relevant region and/or via audio instructions.
The travel time between two road intersections A and B is the sum of travel times for all sections of roads connecting A and B on the shortest route either by the minimal time criterion, or by some other criterion. Then in order to be able to compute a travel time between two positions on a map, we must be able to determine travel times for all sections of roads connecting those positions, or road intersections close to them. In the standard solution (an autonomous or stand-alone on-vehicle application), a route is computed by a mathematical optimization algorithm while travel times are computed as distances divided by maximal allowed speeds. While being simple, such solutions have an obvious shortcoming in that they do not take into account the real conditions on the roads and therefore can serve only as a guidance suggestion. Obviously, a true real time system should collect, store and make use of the following kinds of data:
1) Temporary changes in road conditions known in advance like closed roads under construction, traffic reroutes, etc.;
2) Regular predictable changes like everyday slowdowns in rush hours;
3) Sudden unpredictable changes such traffic accidents, traffic congestion due to sudden and drastic changes in traffic arrangements because of visiting dignitaries, etc.
The system in the present invention is built around an idea of collecting and processing information that describes all those changing conditions.
Overview of the Guidance System
The Guidance System in the present invention consists of CTU and a fleet of IMUs, i.e. traveling vehicles. In order to have an updated data on traffic situations, the vehicle fleet is divided into two categories: sample vehicles SMU and all other client vehicles CMU. In general, CMUs are only clients that “consume” traffic congestion data provided by the CTU. The sample vehicles, on the other hand can be both clients and serve also as antennas or tentacles for collecting real time data on traffic situations, which can be used by all end users for updating their optimal routes. This data collection is performed by permanent monitoring of GPS signals obtained from SMUs and by concurrent measuring of their current travel times along a broad range of roads.
FIG. 1
is a schematic representation of the information exchange between CTU and IMUs in the Guidance System. The data transfer from SMUs to CTU is done by wireless RF communication, and from CTU to both SMUs and CMUs by one-to-many multicasting system. The SMU vehicles communicate to CTU their GPS data: the present positions, the position time, their IDs, and their speed vectors at specific time intervals. After processing the information, CTU sends to CMUs updated information on traffic bottleneck situations (i.e. road ID, current time, and travel times of the latest n vehicles). At any given moment, the CTU also maintains the database containing travel times for all sections of roads at a particular time of the day, for a particular of day of the week, etc.
Initially, those travel times are theoretical travel times but as the time goes by and observational data are being collected and processed, they are replaced by empirical travel times reflecting realistic travel conditions, and on particular occasions by current travel times, which reflect sudden and unpredictable changes in traffic conditions. Those travel times are being measured and periodically broadcasted by the CTU via satellite IP Multicasting broadcast to end-users where they are entered into the databases of the on-vehicle computers for future use.
On receiv
Elman & Associates
Makor Issues and Rights Ltd.
Nguyen Tan
Protigal Stanley N.
Tran Dalena
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