©2013-2016 Traffic Technology Services, Inc. A portion of the disclosure of this patent document contains material which is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent file or records, but otherwise reserves all copyright rights whatsoever. 37 CFR § 1.71(d).
This disclosure pertains to electric traffic control signals of the sort commonly found at street intersections, freeway ramps, and the like, for directing traffic, which may include without limitation pedestrians, bicycles, cars, buses, mass transit vehicles, etc.
It has been suggested that predicting traffic signal changes would be useful. For example, Ginsberg refers to predicting a likely remaining duration of the traffic signal in a particular state; see U.S. Pat. App. Pub. No. 2013/0166109. The need remains, however, for practical and effective solutions for red light running warning message generation from predictive traffic signal state data and communication of such messages in near real-time to vehicle systems and or operators.
The Connected Vehicle Reference Implementation Architecture (CVRIA, http://www.iteris.com/cvria/html/applications/app57.html), takes the signal phase and timing (SPaT) data and begins broadcasting to vehicles approaching or near the intersection. The red light running warning applications focus on field communications between different entities within the local intersection context. They assume the SPaT will cover the red light message that has already been generated.
Other prior art such as US 2005/0156757, only implied that they will focus on the known state or sequence of state to prepare the red light running warning. For example, it implied that the yellow signal will lead to the red signal and thus at yellow light the warning can be already generated for concerned vehicles. [Paragraph 0019]. The need remains for effective red light running warning message generation from predictive traffic signal state data and communication of such messages in near real-time to vehicle systems and or operators. Vehicle systems may include autonomous or semi-autonomous control systems.
Some of the terms used herein may be defined as follows.
Some traffic signals operate on a fixed schedule, while some others are “actuated” or may be adaptive to various conditions. Embodiments of the present invention may be used with all types of non-fixed time signals. Connecting vehicles to the traffic signal infrastructure is a new concept that promises to reduce fuel consumption and save time. We described herein various methods and apparatus to accomplish this functionality. The embodiments described below are not intended to limit the broader inventive concept, but merely to illustrate it with some practical implementations. The ongoing improvements in related technologies, such as cloud computing, wireless data communications, vehicle head units, video, etc. will enable further embodiments in the future that may not be apparent today, but nonetheless will be equivalent variations on our disclosure, perhaps leveraging newer technologies to improve speed, lower cost, etc. without departing from our essential inventive concept.
Some communication infrastructure is necessary to deliver various “signal data” (for example, states, timers or predictions) into a (potentially moving) vehicle in real-time. Preferably, the vehicle (or its operator) not only is informed about the current status of the signal, but also what the signal is going to do in the near-term future. Predictions of traffic control signal status and or changes can be utilized to advantage by a vehicle control system, either autonomously or with driver participation. Predictions of traffic control signal status and or changes can be utilized by a vehicle operator independently of a vehicle control system. One important aspect of the following discussion is to describe how to create traffic signal predictions and deliver them to a vehicle/driver in a timely and useful manner.
Predictions of traffic control signal status and or changes may be delivered to a vehicle in various ways, for example, using the wireless telecom network, Wi-Fi, Bluetooth or any other wireless system for data transfer. Any of the above communication means can be used for communication to a vehicle, for example, to a “head unit” or other in-vehicle system, or to a user's portable wireless device, such as a tablet computer, handheld, smart phone or the like. A user's portable device may or may not be communicatively coupled to the vehicle. for example, it is known to couple a mobile phone to a vehicle head unit for various reasons, utilizing wired or wireless connections.
Predictions of traffic control signal status and or changes may be displayed for a user on a vehicle dashboard, head unit display screen, auxiliary display unit, or the display screen of the user's portable wireless device, such as a tablet computer, handheld, smart phone or the like. As an example, a prediction that a yellow light is going to turn red in two seconds may be provided to a driver and/or to a vehicle that is approaching the subject intersection. One aspect of this disclosure is directed to the use of control emulation to generate this type of short-term prediction.
Again referring to
The vehicle call data is provided to the prediction system 156. It may be communicated via the communication system 152. Preferably, the same vehicle call data generated by the FSC is provided both to the prediction system 156 and to the central management center 154. In some embodiments, the FSC may have a wireless modem 151 installed to communicate call data wirelessly. It may receive detector data wirelessly as well. The prediction system 156, responsive to received vehicle call data and other parameters, generates predictions of FSC state changes, which may include indicator state changes. The predictions may be communicated to a client or customer 160. For example, the client may be an automobile manufacturer, or an aftermarket product or service vendor. The predictions may be conveyed to the client 160 using a push protocol, a pull protocol, regularly scheduled updates or other variations which, in general, should be arranged to be reasonably timely. In a presently preferred embodiment, a push message is executed once per second. In some embodiments, the client 160 may communicate predictions, or information based on the predictions, via a wireless communication system or network 170, to its customers or consumers 180, typically in a motor vehicle. The prediction system 156 in some embodiments may correspond to the prediction system 100 explained in more detail with regard to
In this illustration, the prediction system 100 receives current signal status (observed) as input data 120. The current signal status (real time) may be communicated from the FSC using known protocols. The signal status preferably includes state information and current vehicle call data. The prediction system also receives past signal status (collected) as input data 122. Past signal status data may be collected and processed off-line. For example, such data may be accumulated over several days or weeks. This data may be stored in a database for statistical analysis as further described below.
The prediction system 100 also receives future vehicle call data (Predicted) as input data 140. The future (predicted) detection data 140 is used to advance the control emulator, while applying the local control parameters, to a new state that reflects what the actual controller state is likely to become in the near future. As discussed below, the emulator can be clocked at a rate faster than real-world time, so that it “gets ahead” of the current state of the actual FSC being emulated. The results of the emulation may comprise a future signal status (predicted signal status), indicated as output data 130. The predicted signal status may be communicated to a vehicle or a vehicle operator, or other user, as further described below.
One challenge presented is to synchronize a state of the controller emulator to the current state of the actual FSC. The difficulty arises because the FSC continues to run, and change state, continuously. It is not practical, and potentially even dangerous, to stop the FSC in order and capture a current state. In order to synchronize state to this “moving target,” a process may proceed as follows. First, actual FSC data is collected during a period 203 that is before the point in time marked “Sync Point” 204. An emulator process is initialized to that “old” FSC status to begin. Then, at the sync point in time 204, at least one emulator process is started, and it runs forward from the sync point, up to the current time t and beyond. The emulator “catches up” to the current real-world time t by clocking it at a faster rate. During this time period 207, the emulator process receives call data provided by the FSC responsive to detector inputs or the like. Consequently, the emulator will clock through the same state changes as the actual FSC during this period, up to the current time (t) at 208. Thus the emulator is now fully synchronized to the FSC, at the actual current time.
Starting from the current time t, it remains to predict what the FSC will do in the future. The units are not critical, but intervals of one second are convenient in a presently preferred embodiment. In order to drive the emulator to an expected future state, say a time t+1 or t+3 in
To simplify, here we discuss only a single detector for illustration. For example, one detector might be an in-ground induction loop that detects the presence of a car. Or, it might be a pedestrian push-button. The raw input signals from the detector are received by the FSC and converted into vehicle call data as noted. That call data may be collected and stored over a data collection period, say two weeks, and analyzed using known statistical analyses. The goal is to analyze past behavior of the FSC to help predict its likely future behavior. The data collection period may vary depending on circumstances, and may be changed to optimize it for a given application. The analysis may show, for example, that there is a 40% likelihood of a given call after 2 seconds; and a 60% likelihood of receiving that call after 3 seconds; and perhaps a 90% likelihood or receiving that call after 4 seconds. Each emulator may be calibrated as to how best use this data. For example, the 60% likelihood may be deemed sufficient to trigger a predicted call at t+3. In another application, it may wait until t+4 when the likelihood is greater. Assuming the predicted (and simulated) call is input to the emulator at time t+3, it will change state accordingly. Assuming no other inputs for simplicity of illustration, the emulator now reflects a state that the real FSC is likely to reflect in the future, namely at time t+3. Thus a prediction at 210 is completed. The prediction is captured and the emulator instance may be terminated.
At block 310, the likely future FSC behavior is predicted by fast forwarding the controller emulator, using predicted (future) detection data. The predicted state change may be saved and/or exported, as noted above. At block 312, we terminate the controller emulator process. In some embodiments, the same emulator process may then be re-initialized and run again, in the same fashion as above. Or a new instance may be spawned. On the next operation, and each subsequent run, the process is re-initialized to a more recent sync point.
An emulator process may be initialized and synchronized, block 752. For example, an emulator process may be synchronized to a sync point as discussed. Next, current vehicle call data may be input to the emulator process, block 754. For example, “short-term past” may correspond to the time period 207 in
In some embodiments, a method may include repeating the foregoing steps at a rate of once per second, so as to enable updating the predicted signal status once per second. In some embodiments, field detection data may be received as signal phase data for input to the emulator. In some embodiments, the current state of the emulator includes indicator phase displays (e.g., red, yellow, green, walk, flashing don't walk), and active timers (e.g., minimum green, yellow clearance, red clearance, pedestrian walk, pedestrian clearance, etc.)
The predicted signal status may be forwarded or communicated to a vehicle/driver who may be approaching the subject traffic signal. In an embodiment, a motor vehicle may be equipped with suitable equipment to receive that prediction data, and convey it to a control system and/or a passenger or driver of the vehicle. In one embodiment, prediction data may be displayed on the dashboard; in another embodiment it may be displayed on a head unit or navigation unit display screen. The “navigation unit” may be standalone, or implemented as an “app” on a mobile device.
It is not critical, however, that the light indicators be arranged in that manner, or that colored lights are used at all. Various visual display arrangements other than this example may be used; and indeed, audible signaling (not shown) may be used as an alternative, or in addition to, a visual display. The essential feature is to convey some traffic signal prediction information to a user. For example, in
Having knowledge of what an upcoming traffic signal is going to do in the near future can be used to save gas, save time, and reduce driver stress. For example, when the wait at a red light is going to be relatively long, the driver or an on-board control system may turn off the engine to save fuel. And the prediction system will alert the driver in advance of the light changing to green, to enable a timely restart of the engine. Or, a driver or control system may adjust speed to arrive at a green light. Travel time may be saved by routing optimizations that are responsive to anticipated traffic signal delays. Toward that end, the database prediction data may be provided to a mapping application. Stress is reduced as a driver need not continuously stare at a red signal light, waiting for it to change. In fact, if the wait is known to be long, the driver may want to check her email or safely send a message.
Instead of using only one emulation process to do the prediction, in another embodiment we use one separate process for each cycle second. That way, we don't have to go back in time to the sync point to resynchronize the emulator before being able to play forward every time step. Instead, in one embodiment, we start up as many emulation processes as there are cycle seconds at the synch point. We keep them all synchronized every time step, and then use one of them to play forward and predict for every time step as we move through the cycle second (after which we discard the process). This approach significantly reduces the computation and real-time data storage burdens as we no longer have to keep track of vehicle call data in real-time between sync point and current time. Instead, we have many more, but less computing-intense processes, which is preferable for a cloud computing environment.
Subsequently, at block 432, we “fast forward” all of the controller emulator instances to predict future control signal state changes, using predicted (future) call data. Each emulator instance may be terminated at a selected time “in the future.” For example, in
Next, at block 808, the system selects one of the running emulator instances, and then, block 810, “fast forwards” only the one selected instance, typically by applying a faster clock than the real-time clock. During the fast forward process, predicted future detection data is input to the instance, as discussed above. In one embodiment, the selected instance performs this prediction over a one-second interval.
At the end of that prediction, block 812, the system saves the selected emulator prediction results. For a first selected emulator, this would provide t+1 second prediction results. Then the selected emulator process (only one) is terminated, block 814. Note that meanwhile the other N−1 instances have continued, under real-time clocking, to remain synchronized to the field signal controller, so they are ready to go “fast forward” from their current state. Decision 816 determines whether all N instances have terminated. If not, the process continues via path 820 back to block 808, and selects a second one of the remaining emulators. The second selected emulator instance, only, is then “fast forwarded” as described above with regard to block 810 and the process continues as before using the second selected emulator instance to perform a second prediction. The second prediction may be for time t+2. This same loop 820 is then repeated again for each of the remaining N−2 instances, so that each instance provides a prediction at a time in the future. So, for example, 50 instances might be provisioned to predict signal changes 50 seconds into the future.
Decision 816 detects when all N instances have terminated. The process then loops via path 830 back to block 804 whereupon all N instances are synchronized anew to the new current time t. The process continues to repeat as described so as to continually provide predictions of field controller state. It should be noted that emulation is merely one approach to prediction of signal state changes. Other methods, for example, purely statistical methods, can be used for prediction of traffic control signal operations.
In the description above, we disclosed an emulation mechanism to predict traffic signal state changes. As noted, other prediction methods may be used such as statistics-based predictions. In both cases, there is uncertainty in the predictions; what will happen when is not exactly deterministic. In some embodiments, a prediction system may compute two main signal switches for any phase under control, namely red to green, or green to red. Utilizing statistical support, as explained earlier, the emulation mechanism can assign probabilities to different predictions when the prediction is not certain.
There are many potential uses and advantages to be gained with an accurate prediction or warning ahead of a traffic control signal state change to a red light. One obvious use is to alert a driver of a vehicle that is approaching a signal that it is going to turn red soon. Given an accurate warning, say for example, 10 seconds remain until a change to the red state, the driver can make an informed judgment as to whether to proceed through the intersection or slow to a stop. Autonomous or semi-autonomous vehicles can take the warning message into account in their operational logic to improve safety. Another advantage is savings in fuel (for example, fossil fuels, electrical charge, etc.) by slowing a vehicle ahead of an intersection, given an accurate warning in advance about when it will change to red. Improved fuel efficiency can be gained in human operated as well as automated vehicles.
The stumbling block with known technologies is the technological problem of achieving an accurate warning in advance of a red light change. Specifically, the technological problem involves two dimensions. First, the problem is that predictions are not entirely accurate. Some predictions are based on statistics, and they are imprecise for that reason. Often, for traffic control signals, even though a prediction was “correct” when made, a new input in the traffic control system can cause it to be wrong. For example, in some left-turn lane phases, a number of cars waiting or in the queue to turn left can cause the controller to extend the usual or default left-turn green state duration. And the duration may be variable, in response to a number of cars in the queue, or other factors, up to a predetermined limit or maximum green time. Upon reaching the limit, the signal will change state, typically to yellow, no matter how many cars are waiting to turn left. The point is that the state change to yellow actually is indeterminate.
The other problem is time, i.e. delay or latency. Traffic signal state data, in many applications, must travel from the traffic signal controller at a given intersection to a central traffic management center, and thence to another system where predictions are created. Ergo, the state data input to the prediction equipment is delayed, and the exact amount of this latency is variable, so it is hard to correct for it. This combination of latency and sometimes unreliable predictions make it challenging to programmatically generate a timely and reliable warning in advance of a red light change; and “false positives” should be avoided. That is, a red light warning message should not be sent out if it is wrong or may turn out to be wrong.
Below, we describe additional aspects and features more specific to the red signal phase change. Here, in preferred embodiments, uncertain predictions may be discarded, and only certain predictions that are certain to occur will be relayed, as explained in more detail below. This feature can be utilized to enable an event-based red light warning service that will be initiated only when the system “knows” the relevant signal switch times.
For example, for an actuated phase that is reaching the last vehicle extension before reaching maximum green, the emulation cannot predict exactly when the phase will change from green to yellow, but once yellow, it can predict exactly when, or how long until, it will turn red. That is, typically the yellow signal phase has a fixed, predetermined duration. That yellow time is one of several fixed-interval control events that may be provided in a traffic control timing plan. This prediction can be used to automatically form a red light warning message before the signal device is predicted to change state to a red signal phase. The warning message can be distributed as further described below, before the red signal change actually occurs.
It should be noted that computer implementation of this aspect of the invention is critical because time is of the essence. These red-light warning processes cannot be carried out mentally, or with paper and pencil, because the various steps must be completed, and determinations made, with a period on the order of approximately one second. Even a few seconds of delay will substantially degrade the utility of these processes, i.e. the value of the red light warning messages. In a preferred embodiment, new input data (output data from the prediction process) is received once per second. Calculations should be completed before the new updated data arrives. In short, the process should be carried out substantially in real-time, excepting communication latencies.
Various application services may use or “consume” red light warning messages. The application services, typically implemented in software or firmware, may be on board a vehicle and configured for warning a driver-user of the vehicle. In some embodiments, on board systems may comprise or be coupled to on-board autonomous or semi-autonomous vehicle control systems. Such systems may take the warning message into account in their operations. Some examples of such on-board systems are described below with regard to
Importantly, this feature does not rely on the traffic signal controller (FSC) to tell the signal state, nor does it rely solely on a known state (e.g., currently yellow). Instead, it is a generalized method that is all inclusive of any deterministic event from the traffic controller, and determines all potential red light phase change events ahead of time. This prediction based approach will provide additional safety benefits over current technologies. As just one example, on a slippery road, several seconds of prior warning can make the difference in avoiding a possible collision likely to occur if it was not in place. However, as explained above, predictions do not always turn out to be true. Therefore, additional logic is needed.
Referring now to
The prediction component for a given traffic signal generates predicted traffic signal state data; this data may include, for example, second by second signal status predictions for the next two main switches: red to green, and green to (yellow then to) red. In other words, the prediction component may generate updated data periodically, for example, once per second, although this interval is not critical. It should be no longer than a few seconds. The prediction component may be implemented in a server, for example, in the cloud, or on-board a vehicle, or in a hybrid combination of on-board and remote resources. A hybrid prediction system is described in our application Ser. No. 15/179,850 filed Jun. 10, 2016 and incorporated herein by this reference.
Again referring to
Continuing via path 1006, the process next determines whether the (certain) predicted state change will begin a fixed-time traffic control event, decision 1010. In more detail, the predictions from emulator output 1002 may not always be 100% accurate; many modern controllers can change signal timing based on traffic detection inputs (vehicle/bike detection, public transit check-in, and pedestrian push buttons are common examples). The emulation is based on the input of predicted traffic demand; for example, see
Fixed-time events can be scheduled or fixed in the sequence of signal control logic. In general, they can be discerned from the traffic signal timing plan. Analysis of the signal timing plan is discussed further with regard to
There are other signal control events that also have a fixed timer associated, for example, the typical fixed-time control, where the phase sequence and splits (green plus yellow plus all-red times) remain the same. These fixed events may include various signal switch times such as red to green as well. At the next decision step, block 1010, each of these fixed events is considered. Again, these fixed-time events may be extracted from the signal timing plan for the FSC of interest. Note that in a presently preferred embodiment, this selection process 1004-1010-1020 is completed at each prediction for each output; only a subset of the predicted signal state changes will make its way here.
At decision 1020, another filtering step, the illustrated process compares the current prediction (and current state) to those signal sequences in the timing plan that necessarily lead to a state change to a red light. For example, some events may be fixed and known, but not necessarily lead the switch from yellow to red. The most obvious is the prediction of red to green switch times; while the vehicle controlled by the concerned phase is approaching the intersection while the light is still red. When it reaches the stop line, the light could be green by prediction. Then this is not considered a red light violation (or potential violation) for at least this phase, as the vehicle is not about to enter the intersection (or cross the limit line) while the signal is red. However, this may be a true red light running possible occurrence for phases on conflicting approaches or movements; those will be recorded as the red light running warning event (RLRW). Above, for simplicity, we referred in some cases to green to red as one switch or state change; however, in practice usually controllers for urban intersections have the normal sequence of green-yellow-red. This sequence can vary, however; some countries may have additional intervals. For example, Canada has a flashing green interval and Germany has a combined red and yellow interval. All such variations can be accommodated in view of the present disclosure. Continuing with the illustrated embodiments, after the filtering step 1020, the filtered (fixed) events have been identified for vehicles controlled by at least one phase; some could have impact on vehicles controlled by multiple phases.
A timestamp is received from a source 1030, for example, a UTC timestamp. The latest timestamp is added to the incoming prediction data when it is received at block 1002. In the event that a warning message is generated, the same timestamp 1026 is added to the warning message 1050. That is, the same timestamp value as that associated to the prediction instance that led to the warning message is added to the warning message. In this way, downstream consumers of the message will know the time of the prediction. By comparing to a current time downstream, an adjustment may be made to correct for latency in delivery of the message. In an embodiment, a system may have an embedded time synchronization module that gets the Universal Coordinated Time (UTC) time. This can be polled from multiple Network Time Protocol (NTP) servers and the best estimate, or it can use a time server itself to get the time reference. Getting the timestamp is a critical component in some embodiments, as the prediction does not depend on field communications such as the dedicated short range communication (DSRC) as described in (http://www.iteris.com/cvria/html/applications/app57.html). In the case of DSRC, when such messages are broadcast, it is broadcast with no timestamps as it is required to have the latency of under 0.1 seconds.
The red light warning server (RLWS) 1210 also includes analysis software 1220 which is executable on the processor, again under control of the OS 1222. Operation of the analysis software 1220 was described above with regard to the flow diagram of
Some of the on-board systems may include emergency braking 1320; collision avoidance 1314; human interfaces 1316 (for example, dashboard displays, audio announcements, “head unit” or navigation screen, etc.); airbags 1320. Airbag logic may take a red light warning into account, along with other input variables. Finally, other components may include vehicle-to-vehicle (“V2V”) communications. For example, warnings may relayed to other vehicles that may be closely following the vehicle 1300. In another example, warning messages may be sent to crossing vehicles—for example, a first vehicle entering an intersection crossing the path of a second vehicle that is subject to an upcoming red light state change.
One of skill in the art will recognize that the concepts taught herein can be tailored to a particular application in many other ways. In particular, those skilled in the art will recognize that the illustrated examples are but one of many alternative implementations that will become apparent upon reading this disclosure. It will be obvious to those having skill in the art that many changes may be made to the details of the above-described embodiments without departing from the underlying principles of the invention.
The scope of the present invention should, therefore, be determined only by the following claims.
This application is a continuation-in-part of U.S. patent application Ser. No. 14/252,491, filed Apr. 14, 2014, which is a non-provisional of U.S. provisional application No. 61/811,655 filed on Apr. 12, 2013, both incorporated herein by this reference.
Number | Name | Date | Kind |
---|---|---|---|
5940010 | Sasaki et al. | Aug 1999 | A |
6519599 | Chickering | Feb 2003 | B1 |
6989766 | Mese | Jan 2006 | B2 |
8761991 | Ferguson | Jun 2014 | B1 |
9043138 | Ginsberg | May 2015 | B2 |
9396657 | Bauer | Jul 2016 | B1 |
20030128135 | Poltorak | Jul 2003 | A1 |
20040189489 | Terui | Sep 2004 | A1 |
20050156757 | Gamer | Jul 2005 | A1 |
20050187701 | Baney | Aug 2005 | A1 |
20060009188 | Kubota | Jan 2006 | A1 |
20070027583 | Tamir | Feb 2007 | A1 |
20070222638 | Chen et al. | Sep 2007 | A1 |
20080253615 | Kubota | Oct 2008 | A1 |
20090070031 | Ginsberg | Mar 2009 | A1 |
20110037618 | Ginsberg | Feb 2011 | A1 |
20110037619 | Ginsberg | Feb 2011 | A1 |
20110040621 | Ginsberg | Feb 2011 | A1 |
20110093178 | Yamada | Apr 2011 | A1 |
20110115646 | Matsumura | May 2011 | A1 |
20120026014 | Miller | Feb 2012 | A1 |
20120139754 | Ginsberg | Jun 2012 | A1 |
20120139755 | Ginsberg | Jun 2012 | A1 |
20120179358 | Chang | Jul 2012 | A1 |
20120274481 | Ginsberg | Nov 2012 | A1 |
20120288138 | Zeng | Nov 2012 | A1 |
20130076538 | Uno | Mar 2013 | A1 |
20130131980 | Ginsberg | May 2013 | A1 |
20130166109 | Ginsberg | Jun 2013 | A1 |
20130253754 | Ferguson | Sep 2013 | A1 |
20130297124 | Be | Nov 2013 | A1 |
20140032089 | Aoude | Jan 2014 | A1 |
20140046509 | Otake | Feb 2014 | A1 |
20140277986 | Mahler | Sep 2014 | A1 |
20150015421 | Krijger | Jan 2015 | A1 |
20150046055 | Yamada | Feb 2015 | A1 |
20160156881 | Guan | Jun 2016 | A1 |
20160203717 | Ginsberg et al. | Jul 2016 | A1 |
20160284215 | Ova | Sep 2016 | A1 |
20160339959 | Lee | Nov 2016 | A1 |
Number | Date | Country |
---|---|---|
1615190 | Jan 2006 | EP |
2824647 | Jan 2015 | EP |
2011019445 | Feb 2011 | WO |
2011163006 | Dec 2011 | WO |
2013109472 | Jul 2013 | WO |
2017003793 | Jan 2017 | WO |
Entry |
---|
Maile M. et al., Cooperative Intersection Collision Avoidance System or Violations (CICAS-V) for Avoidance of Ciolation-Based Intersection Crashes, Mercedes-Benz Research and Development North America, Inc. USA, Paper No. 09-0118, 2009, fourteen pages [online] [retrieved Jun. 5, 2012] retrieved from the internet URL http://nrd.nhtsa.dot.gov/pdf/esv/es21/09-0118.pdf. |
Aoude G. et al.; Behavior Classification Algorithms at Inersections and Validation using Naturalistic Data; Jul. 2011; six pages [online] [retrieved Jun. 5, 2012] URL http://acl.mit.edu/papers/IV11AoudeDesarajuLaurensHow.pdf. |
Gminsidenews.com; DCS Preps Safety System to Previent Red-Light Running; Jun. 18, 2006, eleven pages [-online] [retrieved Jan. 23, 2012] retrieved from the internet http://www.gminsidenews.com/forums/f58/dcx-preps-safety-system-prevent-red-light-running-32921. |
Faezipour, M et al.; Progress and Challenges in Intelligent Vehicle Area Networks; Communication of the ACM, Feb. 2012; pp. 90-100, vol. 55, No. 2. |
Strauss S., Traffic Magic, University Affairs, Dec. 7, 2009, [online] [retrieved Aug. 17, 2010] retrieved from the internet URL http://www.universityaffairs.ca/Print.aspx?id=7102. |
Vaughan Inman and Gregory Davis; The Effects of In-Vehicle and Infrastructure-Based Collision Warnings at Signalized Intersections; US Department of Transportation, Federal Highway Administration; Publication No. FHWA-HRT-09-049; Dec. 2009 (46 pages). |
European Patent Office; International Search Report and Written Opinion of the EPO (ISA) PCT/US2016/038817; dated Oct. 19, 2016; 14 Pages. |
PCT, International Search Report and Written Opinion of the lcnternational Searching Authority, PCT/US2016/038817, dated Oct. 19, 2016, 14 pages. |
Number | Date | Country | |
---|---|---|---|
20160293006 A1 | Oct 2016 | US |
Number | Date | Country | |
---|---|---|---|
61811655 | Apr 2013 | US |
Number | Date | Country | |
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Parent | 14252491 | Apr 2014 | US |
Child | 15185531 | US |