Office Action 2: Final Rejection (2025-02-06)
Rejection types: 103 - Obviousness

CLAIMS:
Listing of Claims:

AMENDMENT TO THE CLAIMSThis listing of claims will replace all prior versions, and listings, of claims in the application.

1\. (Currently Amended) An apparatus for preventing car collision, the apparatus comprising:
a communication unit configured to communicate with a preceding vehicle or a camera;
and a processor operatively connected to the communication unit,wherein the processor is configured to:
obtain information about a third vehicle in front of the preceding vehicle acquired by the preceding vehicle;
calculate a deceleration amount for preventing a collision with the third vehicle based on the information about the third vehicle;
predict a preset event related to the preceding vehicle based on a change in a position of the preceding vehicle; and
control an autonomous driving vehicle based on the deceleration amount according to a prediction result regarding the preset events wherein the processor detects the change in the position of the preceding vehicle and predicts occurrence of the preset event using an event prediction model based on the detected change in the position of the preceding vehicle, and wherein the event prediction model is trained based on information related to the detected change in the position of the preceding vehicle using a machine learning algorithm.

2\. (Currently Amended) The apparatus of claim 1, wherein, when it is not possible to obtain, from the preceding vehicle, the information about the third vehicle in front of the preceding vehicle, the processor obtains the information about the third vehicle in front of the preceding vehicle detected by one or more cameras installed on a road on which the autonomous driving vehicle is traveling.

3\. (Currently Amended) The apparatus of claim 2, wherein the information about the third vehicle in front of the preceding vehicle includes at least one piece of information among the group of information about a distance between the preceding vehicle and the third vehicle in front of the preceding vehicle and information about a lane in which the third vehicle in front of the preceding vehicle is located.

4\. (Cancelled)

5\. (Currently Amended) The apparatus of claim 1,wherein the preset event includes an event where one side of the preceding vehicle is positioned at a point in an adjacent lane of either side of a lane in which the preceding vehicle is traveling, the point being separated by a distance exceeding 1/3 of a total width of the preceding vehicle from any one lane marking of a corresponding side of the lane in which the preceding vehicle is traveling.

6\. (Currently Amended) The apparatus of claim 1, wherein the information about the third vehicle in front of the preceding vehicle includes at least one piece of information among the group of information about a distance between the preceding vehicle and the third vehicle in front of the preceding vehicle and information about a lane in which the third vehicle in front of the preceding vehicle is located.

7\. (Cancelled)

8\. (Currently Amended) A method for preventing car collision, the method comprising:
obtaining information about a third vehicle in front of a preceding vehicle acquired by the preceding vehicle;
calculating a deceleration amount for preventing a collision with the third ehicle based on the obtained information about the third ehicle;
predicting a preset event related to the preceding vehicle based on a change in a position of the preceding vehicle; and
controlling an autonomous driving vehicle based on the deceleration amount according to a prediction result regarding the preset events wherein the predicting of the preset event related to the preceding vehicle comprises:

detecting the change in the position of the preceding vehicle;
training an event prediction model based on information related to the detected change in the position of the preceding vehicle using a machine learning algorithm; and
predicting occurrence of the preset event using the event prediction model based on the change in the position of the preceding vehicle.

9\. (Currently Amended) The method of claim 8, further comprising obtaining the information about the third vehicle in front of the preceding vehicle detected by one or more cameras installed on a road on which the autonomous driving vehicle is traveling when it is not possible to obtain, from the preceding vehicle, the information about the third vehicle in front of the preceding vehicle.

10\. (Currently Amended) The method of claim 9, wherein the information about the third vehicle in front of the preceding vehicle includes at least one piece of information among the group of information about a distance between the preceding vehicle and the third vehicle in front of the preceding vehicle and information about a lane in which the third vehicle in front of the preceding vehicle is located.

11\. (Cancelled)

12\. (Currently Amended) The method of claim 8,wherein the preset event includes an event where one side of the preceding vehicle is positioned at a point in an adjacent lane of either side of a lane in which the preceding vehicle is traveling, the point being separated by a distance exceeding 1/3 of a total width of the preceding vehicle from any one lane marking of a corresponding side of the lane in which the preceding vehicle is traveling.

13\. (Currently Amended) The method of claim 8, wherein the information about the third vehicle in front of the preceding vehicle includes at least one piece of information among the group of information about a distance between the preceding vehicle and the third vehicle in front of the preceding vehicle and information about a lane in which the third vehicle in front of the preceding vehicle is located.

14\. (Cancelled)

15\. (New) The apparatus of claim 1, wherein the third vehicle includes a vehicle that is stopped in front of the preceding vehicle.

16\. (New) The apparatus of claim 1, wherein the processor performs deceleration control of the autonomous driving vehicle based on the deceleration amount, at a point in time at which the processor determines that the preset event will occur based on the prediction result of the event prediction model, before a point in time at which the preset event actually occurs.

17\. (New) The apparatus of claim 1, wherein the event prediction model is trained from a first point in time to a second point in time, the first point in time is a point in time at which the preceding vehicle starts to move from a same lane as a lane in which the autonomous driving vehicle is traveling to an adjacent lane, the second point in time is a point in time at which a portion of the preceding vehicle is located at a point in the adjacent lane, the point in the adjacent lane being separated by a predetermined distance from any one lane marking of the lane in which the preceding vehicle is traveling, and
the second point in time is a point in time at which the preset event related to the preceding vehicle occurs.

18\. (New) The apparatus of claim 1, wherein the processor predicts that the preset event will occur based on data regarding a probability of the occurrence of the preset event generated by the trained event prediction model.

19\. (New) The method of claim 8, wherein the third vehicle includes a vehicle that is stopped in front of the preceding vehicle.
20\. (New) The method of claim 8, wherein the controlling of the autonomous driving vehicle comprises performing deceleration control of the autonomous driving vehicle based on the deceleration amount, at a point in time at which the processor determines that the preset event DB1/ 151595431.1 6

will occur based on the prediction result of the event prediction model, before a point in time at which the preset event actually occurs.

21\. (New) The method of claim 8, wherein the training of the event prediction model comprises training the event prediction model from a first point in time to a second point in time, the first point in time is a point in time at which the preceding vehicle starts to move from a same lane as a lane in which the autonomous driving vehicle is traveling to an adjacent lane, the second point in time is a point in time at which a portion of the preceding vehicle is located at a point in the adjacent lane, the point in the adjacent lane being separated by a predetermined distance from any one lane marking of the lane in which the preceding vehicle is traveling, and
the second point in time is a point in time at which the preset event related to the preceding vehicle occurs.

22\. (New) The method of claim 20, wherein the predicting of the preset event related to the preceding vehicle further comprises predicting that the preset event will occur based on data regarding a probability of the occurrence of the preset event generated by the trained event prediction model.

OFFICE ACTION:
DETAILED ACTION
Status of Claims
This Office action is in response to the amendment filed on 01/02/2025. Claims 4, 7, 11, and 14 have been canceled, and new claims 15-22 have been added. Claims 1-3, 5-6, 8-10, 12-13, and 15-22 are currently pending and are presented for examination.
Response to Amendment/Arguments
The amendment filed 01/02/2025 has been entered and applicant’s arguments filed 01/02/2025 have been fully considered.
Regarding specification objection:
Applicant has argued that the specification objection has been overcome by the filed amendment. The examiner agrees and has withdrawn the objection accordingly.
Regarding claim rejections under 35 U.S.C. §§ 102 and 103:
Applicant’s arguments regarding the claim rejections under 35 U.S.C. §§ 102 and 103 are moot in view of the new grounds of rejection which are necessitated by the filed amendment.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 8, 15, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Park (US 2022/0332319 A1) in view of Shalev-Shwartz et al. (US 2021/0078570 A1), hereinafter referred to as Shalev.
Regarding claim 1:
Park discloses the following limitations:
“An apparatus for preventing car collision, the apparatus comprising: a communication unit configured to communicate with a preceding vehicle or a camera.” (Park ¶ 28 discloses “a vehicle including: a communicator configured to communicate with a plurality of other vehicle.” As shown in Park FIG. 7B, the plurality of vehicles can include a second vehicle 2 that travels in front of the ego vehicle 1. This at least teaches a communication unit configured to communicate with a preceding vehicle as claimed.)
“and a processor operatively connected to the communication unit.” (Park ¶¶ 150 and 219: “the cruise control device may correspond to a second controller, and may communicate with … a communicator 250,” where “The second controller 280 may be implemented as a single processor.”)
“wherein the processor is configured to: obtain information about a third vehicle in front of the preceding vehicle acquired by the preceding vehicle.” (Park FIG. 7B shows a third vehicle 3 in front of the second vehicle 2. Further, Park ¶ 274 discloses that “The vehicle may also determine whether the third vehicle is in a stationary state from the travel information and position information of the third vehicle received by the communicator 250.”)
“calculate a deceleration amount for preventing a collision with the third vehicle based on the information about the third vehicle.” (Park ¶ 275: “As shown in FIG. 7B, the vehicle may determine whether there is an obstacle that is stopped in the travelling path, and upon determining that there is an obstacle that is stopped in the path, acquire a target deceleration based on position information of the obstacle in the stationary state and the travelling velocity information of the first vehicle, and control deceleration and braking based on the acquired target deceleration.”)
“predict a preset event related to the preceding vehicle based on a change in a position of the preceding vehicle.” (Park ¶¶ 262-263: “as shown in FIG. 7A, the vehicle may, upon determining that the second vehicle 2 is not detected through the obstacle detector 220, determine that the path of the second vehicle 2 is suddenly changed (cut out). The sudden path change may include a state in which the distance to the obstacle acquired based on obstacle information detected by the obstacle detector changes to a degree greater than or equal to a reference distance within a predetermined time.”)
“and control an autonomous driving vehicle based on the deceleration amount according to a prediction result regarding the preset event.” (Park ¶¶ 267-268: “the vehicle decelerates and performs braking while maintaining the cruise control mode based on the travel information of the third vehicle in front of the vehicle during execution of the cruise control mode, so that the cruise control mode is prevented from being deactivated, thereby eliminating a hassle of the driver in resetting the cruise control mode. As described above, even when the first vehicle 1 controls acceleration because the second vehicle 2 travelling in front of the first vehicle departs from the front of the first vehicle 1, the first vehicle 1 obtains the amount of acceleration in consideration of the travel information of the third vehicle 3 travelling in front of the second vehicle 2, so that the first vehicle 1 is prevented from colliding with the third vehicle 3.”)
Note that under the broadest reasonable interpretation (BRI) of claim 1, consistent with the specification, the communication unit being “configured to communicate with a preceding vehicle or a camera” is being treated as an alternative limitation. Applicant has elected to use the word “or” in the claim language, and therefore, the BRI covers the scenario in which only one of the limitations applies. Accordingly, while only communicating with a preceding vehicle has been addressed here, the claim is still rejected in its entirety.
The following limitations are not specifically disclosed by Park, but are taught by Shalev:
“wherein the processor detects the change in the position of the preceding vehicle and predicts occurrence of the preset event using an event prediction model based on the detected change in the position of the preceding vehicle.” (Shalev ¶ 189: “If the distance between the leading vehicle's instantaneous position and the look-ahead point varies during the specific period of time, and the cumulative sum of variation exceeds a predetermined threshold (for example, 0.3 to 0.4 meters on a straight road, 0.7 to 0.8 meters on a moderately curvy road, and 1.3 to 1.7 meters on a road with sharp curves), processing unit 110 may determine that the leading vehicle is likely changing lanes.” The leading vehicle changing lanes reads on the “preset event” as claimed.)
“and wherein the event prediction model is trained based on information related to the detected change in the position of the preceding vehicle using a machine learning algorithm.” (Shalev ¶ 190: “At step 584, processing unit 110 may determine whether or not leading vehicle 200 is changing lanes based on the analysis performed at step 582. … the analysis may make use of trained system (e.g., a machine learning or deep learning system), which may, for example, estimate a future path ahead of a current location of a vehicle based on an image captured at the current location.”)
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to modify the system of Park by using a trained machine learning model to predict a lane change of the preceding vehicle based on a detected change in its position as taught by Shalev with a reasonable expectation of success. A person having ordinary skill in the art could have been motivated to do this because Shalev ¶ 387 teaches that this modification “may help ensure that from a planning perspective there will be no accident of the AV's own responsibility.” A person having ordinary skill in the art would have recognized that using a machine learning model to predict a preceding vehicle’s lane change would help automate the process and allow the host vehicle to brake sooner and less sharply in response to the predicted lane change.
Regarding claim 8:
Claim 8 is rejected with the same rationale applied to claim 1 above, mutatis mutandis.
Regarding claim 15:
The combination of Park and Shalev teaches “The apparatus of claim 1,” and Park further teaches “wherein the third vehicle includes a vehicle that is stopped in front of the preceding vehicle.” (Park ¶ 274: “The vehicle may also determine whether the third vehicle is in a stationary state from the travel information and position information of the third vehicle received by the communicator 250.”)
Regarding claim 19:
Claim 19 is rejected with the same rationale applied to claim 15 above, mutatis mutandis.
Claims 2-3, 6, 9-10, and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Park in view of Shalev as applied to claims 1 and 8 above, and further in view of Grace et al. (US 2021/0304607 A1), hereinafter referred to as Grace.
Regarding claim 2:
The combination of Park and Shalev teaches “The apparatus of claim 1,” but does not specifically disclose “wherein, when it is not possible to obtain, from the preceding vehicle, the information about the third vehicle in front of the preceding vehicle, the processor obtains the information about the third vehicle in front of the preceding vehicle detected by one or more cameras installed on a road on which the autonomous driving vehicle is traveling.” However, Grace does teach this limitation. (Grace ¶ 69 discloses that data can be obtained about a static obstacle such as a bus 610. Further, Grace ¶ 86: “Turning back to the step 1006, upon negative determination in the step 1006 (e.g., when it is determined that there is no perception unit X in the vicinity of AV1 and/or when it is determined that there is no perception unit X that is capable of providing information indicative of the environment surrounding AV1), the method 1000 may proceed with a step 1016 that includes the vehicle management system 140 determining whether a further perception unit X capable of obtaining information indicative of the environment surrounding AV1 can be deployed.” Also, Grace ¶¶ 33 and 56-57: “the sensor suite 120 may include a camera 210,” and “The sensor suite 520 may be substantially similar to the sensor suite 120 of the AV 110… The perception analysis module [530] may be configured to process the perception data gathered by the sensor suite 520 of the infrastructure object 150.”)
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to modify the system disclosed by the combination of Park and Shalev by using infrastructure image data when a vehicle perception unit is not capable of providing information to the autonomous vehicle as is taught by Grace with a reasonable expectation of success. A person having ordinary skill in the art could have been motivated to do this because Grace ¶¶ 84-85 teach that with this modification, the autonomous vehicle can adjust its driving strategy, resulting in safer behavior.
Regarding claim 3:
The combination of Park, Shalev, and Grace teaches “The apparatus of claim 2,” and Grace further teaches “wherein the information about the third vehicle in front of the preceding vehicle includes at least one piece of information among the group of information about a distance between the preceding vehicle and the third vehicle in front of the preceding vehicle and information about a lane in which the third vehicle in front of the preceding vehicle is located.” (Grace ¶ 69: “with reference to the setting shown in FIG. 6, the complexity score computed based on the sensor data of the AV 110b may be indicative of … the lane direction of the bus 610.” This at least teaches the information including “information about a lane in which the vehicle stopped in front of the preceding vehicle is located” as claimed.)
Note that under the BRI of claim 3, consistent with the specification, the stopped vehicle information including “at least one piece of information among the group of information about a distance between the preceding vehicle and the third vehicle in front of the preceding vehicle and information about a lane in which the third vehicle in front of the preceding vehicle is located” is treated as an alternative limitation. Applicant has elected to use the phrase “at least one” in the claim language, and therefore, the BRI covers the scenario in which only one of the limitations applies. Accordingly, while only the “information about a lane in which the third vehicle in front of the preceding vehicle is located” has been addressed here, the claim is still rejected in its entirety.
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to modify the system disclosed by the combination of Park and Shalev by obtaining information about which lane the obstacle is located in as is taught by Grace, because modification amounts to a combination of prior art elements according to known methods to yield predictable results (see MPEP 2143(I)(A)). Obtaining data indicative of a lane in which an obstacle is located would have predictably functioned similarly whether done within the vehicle collaborative perception system of Grace or whether integrated into the advanced driver assistance system disclosed by the combination of Park and Shalev. A person having ordinary skill in the art would have recognized that identifying a lane of an obstacle would be beneficial since it would help the vehicle to avoid colliding with the obstacle.
Regarding claim 9:
Claim 9 is rejected with the same rationale applied to claim 2 above, mutatis mutandis.
Regarding claims 6, 10, and 13:
Claims 6, 10, and 13 are each rejected with the same rationale applied to claim 3 above, mutatis mutandis.
Claims 5 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Park in view of Shalev as applied to claims 1 and 8 above, and further in view of Kato et al. (US 2020/0247417 A1), hereinafter referred to as Kato.
Regarding claim 5:
The combination of Park and Shalev teaches “The apparatus of claim 1,” and Shalev further teaches “wherein the preset event includes an event where one side of the preceding vehicle is positioned at a point in an adjacent lane of either side of a lane in which the preceding vehicle is traveling.” (Shalev ¶ 190: “processing unit 110 may determine whether or not leading vehicle 200 is changing lanes.”)
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to modify the system of Park by predicting that a leading vehicle will move into an adjacent lane as taught by Shalev, because this modification amounts to a combination of prior art elements according to known methods to yield predictable results (see MPEP 2143(I)(A)). Predicting whether the leading vehicle is changing lanes would have predictably functioned similarly whether done within the vehicle navigation system of Shalev or whether integrated into the advanced driver assistance system of Park. A person having ordinary skill in the art would have recognized the importance of detecting a lane change of the preceding vehicle in order to prevent collisions with the preceding vehicles or other obstacles in the road.
The combination of Park and Shalev does not specifically teach “the point being separated by a distance exceeding 1/3 of a total width of the preceding vehicle from any one lane marking of a corresponding side of the lane in which the preceding vehicle is traveling.” However, Kato does teach this limitation. (Kato ¶ 60: “the calculation unit 54 determines whether the lane change is completed. For example, in a case where a half or more of the host vehicle 10 in a vehicle width direction is positioned in the adjacent lane 78, the calculation unit 54 can determine that the lane change is completed.” This teaches the claim limitation since half the width of the vehicle is “a distance exceeding 1/3 of a total width of the preceding vehicle” as claimed.)
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to modify the system disclosed by the combination of Park and Shalev by using a threshold vehicle width such as half the vehicle width when predicting a lane change occurrence as taught by Kato, because this amounts to a combination of prior art elements according to known methods to yield predictable results (see MPEP 2143(I)(A)). Using a threshold width when identifying a lane change occurrence would have predictably functioned similarly whether done within the vehicle control system of Kato or whether integrated into the advanced driver assistance system that is disclosed by the combination of Park and Shalev. A person having ordinary skill in the art would have recognized that a vehicle is unlikely to cross halfway into an adjacent lane unless it intends to change lanes, and that using a threshold vehicle width would provide an objective, quantifiable measure of lane changing that could easily be utilized by an autonomous vehicle system.
Regarding claim 12:
Claim 12 is rejected with the same rationale applied to claim 5 above, mutatis mutandis.
Claims 16 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Park in view of Shalev as applied to claims 1 and 8 above, and further in view of Schmudderich et al. (US 2013/0054106 A1), hereinafter referred to as Schmudderich.
Regarding claim 16:
The combination of Park and Shalev teaches “The apparatus of claim 1,” and Park further teaches “wherein the processor performs deceleration control of the autonomous driving vehicle based on the deceleration amount, at a point in time at which the processor determines that the preset event will occur based on the prediction result of the event prediction model.”
The combination of Park and Shalev does not specifically teach to perform deceleration control “before a point in time at which the preset event actually occurs.” However, this limitation is taught by Schmudderich. (Schmudderich ¶ 7: “the invention targets at predicting the target object's movement behavior before it actually performs a cutting-in or cutting-out maneuver. The prediction allows the host vehicle to either decelerate earlier and thus longer preventing under-runs of the safety distance or to accelerate earlier and thus allow for quicker overtaking process, or both.”)
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to modify the system disclosed by the combination of Park and Shalev by allowing the host vehicle to decelerate before the preceding vehicle performs its lane change maneuver as taught by Schmudderich with a reasonable expectation of success. A person having ordinary skill in the art could have been motivated to do this because Schmudderich ¶ 7 teaches that this allows the host vehicle to decelerate earlier and longer to prevent coming within an unsafe distance of the preceding vehicle.
Regarding claim 20:
Claim 20 is rejected with the same rationale applied to claim 16 above, mutatis mutandis.
Claims 18 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Park in view of Shalev as applied to claims 1 and 20 above, and further in view of Xue et al. (US 2022/0375349 A1), hereinafter referred to as Xue.
Regarding claim 18:
The combination of Park and Shalev teaches “The apparatus of claim 1,” but does not specifically teach “wherein the processor predicts that the preset event will occur based on data regarding a probability of the occurrence of the preset event generated by the trained event prediction model.” However, Xue does teach this limitation. (Xue ¶ 5 discloses “receiving a velocity and a position of the target vehicle; respectively obtaining, based on the velocity and the position of the target vehicle, a first lane-changing probability and a second lane-changing probability of the target vehicle by using a first machine learning model and a second machine learning model; and determining a possibility of lane changing of the target vehicle based on the first lane-changing probability and the second lane-changing probability.”)
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to modify the system disclosed by the combination of Park and Shalev by predicting a probability of a preceding vehicle changing lanes as taught by Xue with a reasonable expectation of success. A person having ordinary skill in the art could have been motivated to do this because Xue ¶ 3 teaches that a preceding vehicle changing lanes “poses a significant threat to road traffic safety. Therefore, the prediction of a lane-changing intention of vehicles on two sides of a present vehicle on a road can effectively prevent traffic incidents from happening and improve the driving safety.”
Regarding claim 22:
Claim 22 is rejected with the same rationale applied to claim 18 above, mutatis mutandis.
Allowable Subject Matter
Claims 17 and 21 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.

APPLICANT REBUTTAL: REMARKS
Introduction
Upon entry, claims 1-3, 5-6, 8-10, 12-13 and 15-22 are pending in this application, of which claims 1 and 8 are independent. By this response, claims 1-3, 5-6, 8-10, and 12-13 are amended, and claims 4, 7, 11, and 14 have been cancelled without prejudice or disclaimer. New claims 15- 22 have been added. All amendments made to the claims are fully supported by the specification as originally filed, for example, by Fig. 2B, paragraphs 0045-47 and 0063-0064, and the original claims. Hence, no new matter has been introduced.
The arguments and contentions presented herein should not be construed as any acquiescence or agreement by Applicant with the reasoning stated in the Office Action regardless of whether or not the following remarks specifically address any particular argument or contention from the Office Action. Furthermore, although certain distinctions between the claims of the present application and the cited references are addressed below, these distinctions are not necessarily exhaustive.
Reconsideration of this application for allowance of all pending claims is hereby respectfully requested in view of the amendments to the claims and following remarks.
Obj
ections to the S
p
ecification
SM, or ® following the term. To obviate this objection, paragraphs 0022, 0023, and 0081 of the application-as-filed are amended to replace the term "Wi-Fi" with --Wi-Fi*--.
Applicant believes that these amendments are fully responsive to the Examiner's concerns. Withdrawal of the objection to the disclosure is, therefore, respectfully solicited.
Claim Re
j
ections Under 35 U.S.C.
&&
Claims 1 and 8 stand rejected under 35 U.S.C. § 102(a)(2) as being anticipated by Park (U.S. Publication No. 2022/0332319 A1, hereinafter "Park").
Claims 2-3, 6, 9-10 and 13 stand rejected under 35 U.S.C. § 103 as being unpatentable over Park as applied to claims 1 and 8 above, and further in view of Grace et al. (U.S. Publication No. 2021/0304607 Al, hereinafter "Grace").
Claims 4 and 11 stand rejected under 35 U.S.C. 103 as being unpatentable over Park in view of Grace as applied to claims 2 and 9 above, and further in view of Rajab et al. (U.S. Publication No. 2019/0329777 Al, hereinafter "Rajab").
Claims 5 and 12 stand rejected under 35 U.S.C. 103 as being unpatentable over Park in view of Grace and Rajab as applied to claims 4 and 11 above, and further in view of Kato et al. (US Publication No. 2020/0247417 Al, hereinafter "Kato").
Claims 7 and 14 stand rejected under 35 U.S.C. §103 as being unpatentable over Park as applied to claims 1 and 8 above, and further in view of Rajab.
These rejections are traversed for the following reasons.
Without acquiescing to the merits of these rejections, and solely in the interest of expediting prosecution, independent claim 1 is amended to incorporate subject matter from dependent claim
4 and additional features, as shown below (emphasis added):
the processor detects the change in the position
of the preceding vehicle
and predicts occurrence of the preset event using an event prediction model based
on the detected change in the position
of the preceding vehicle,
and
the event prediction model is trained
based on information related to the
detected change in the position of the preceding vehicle
using a machine learning
algorithm.
In the rejection of claim 4 (subject matter of which has been incorporated into claim 1), the Examiner admits that Park and Grace do not teach "the processor detects the change in the position of the preceding vehicle and predicts the preset event using a preset model based on the change in the position of the preceding vehicle" of the claim, but relies on Rajab as allegedly making up for the admitted deficiencies of Park and Grace. It is respectfully submitted that Rajab does not teach or suggest the amended recitations of claim 1.
Rajab's prediction model is merely "a lookup table based on naturalistic behavior data" (claim 7) on relationships between "the velocity differential between the following vehicle 208 and the first preceding vehicle 210" and "the spacing between the following vehicle 208 and the first preceding vehicle 210" (see paragraphs 0062-0063) for "identifying a potential lane change location and receiving proximate vehicle data associated with
proximate vehicles"
(underline added, see paragraph 0005). Rajab discloses "[t]he prediction plots 400 and 410-460 are illustrative of the data binning and data fit estimation being used to predict a kinematic data at
predictive increments, however, the data may be used as a look-up table or other accessible data form" (see paragraph 0062). That is, as illustrated in Fig. 2 of Rajab (reproduced below with annotation), Rajab's model is a prediction model of a potential lane change or cut-in
of
a
p
roximate vehicle
(on lane 202) to a lane (204) on which the following vehicle 208 and the first preceding vehicle 210 are located, rather than "change in the position of the
precedin
g
vehicle
(to an adjacent lane)" (emphasis added), as recited in claim 1.
I
I
I
Fig. 2 of Rajab (left, annotated) and Fig. 2B of the present application (right, annotated)
On the other hand, as illustrated in Fig. 2B of the present application (reproduced above with annotation) showing one exemplary embodiment of the present disclosure, a processor may detect a change in a position of a preceding vehicle 240 and predict occurrence of a preset event (e.g., a sudden lane change of a preceding vehicle due to a vehicle in front of the preceding vehicle having stopped) using an event prediction model based on the detected change in the position of the preceding vehicle 240. Here, the event prediction model may be trained based on information related to the detected change in the position of the preceding vehicle 240 using a machine learning algorithm.
In addition, the data plots in Figs. 4A-4G of Rajab merely show relative
velocity
data between the following vehicle 208 and the first preceding vehicle 210 and the space therebetween, but do
not
show
position
change data of the proximate vehicle 206. Therefore, Rajab also fails to
teach "predicts occurrence of the preset event using an event prediction model based on the detected
change in the position
of the preceding vehicle" (emphasis added), as recited in claim 1.
Furthermore, Rajab discloses, at best, "the prediction model may be based on simulated data or naturalistic data ... may be based on second-by-second vehicle trajectory data from a Verkehr In Stadten-Simulations model (VISSIM) freeway network" (see paragraph 0060). However, such a simulation software package that Rajab discloses, e.g., VISSIM, does not satisfy
"the event prediction model is trained
based on information related to the detected change in the position of the preceding vehicle
using a machine
l
earnin
g alg
orithm"
(emphasis added), as recited in claim 1.
In view of the foregoing aspects of Rajab, it is respectfully submitted that Rajab does not teach or suggest the aforementioned features of claim 1, and therefore fails to cure the admitted deficiencies of Park and Grace.
Kato, which is cited for allegedly teaching "the point being separated by a distance exceeding 1/3 of a total width of the preceding vehicle from any one lane marking of a corresponding side of the lane in which the preceding vehicle is traveling" of claim 5, does not remedy the aforementioned Park, Grace, and Rajab.
Based on all the foregoing, it is respectfully submitted that the cited references, whether taken alone or in combination, do not disclose, teach or suggest each and every feature in the particular combination embodied by claim 1. "All words in a claim must be considered in judging the patentability of that claim against the prior art." In re Wilson, 165 USPQ 494, 496 (CCPA 1970). Accordingly, claim 1 and the claims dependent thereupon are allowable over the prior art of record.
Independent claim 8 is directed to a method for preventing car collision. While of different scope than claim 1, independent claim 8, as amended, includes recitations substantially similar to those argued above in relation to claim 1. Hence, claim 8 and the claims dependent thereupon are allowable over the cited prior art for at least reasons similar to those discussed above with respect to claim 1.
Applicant therefore submits that the rejections of the claims are overcome and respectfully requests that the rejections of the claims under 35 U.S.C. §§ 102/103 be withdrawn.
New Claims
Dependent claims 15-22 are added. Support for these claims can be found in, e.g., paragraphs 0046-47 and 0063-0064 of the application-as-filed. No new matter is added. Separate from dependency from claim 1 or claim 8, none of the cited references teaches or suggests subject matter of claims 15-22, and claims 15-22 are allowable over the cited art.
Conclusion

NEXT OFFICE ACTION: DETAILED ACTION
Status of Claims
This Office action is in response to the after-final response filed on 04/04/2025. Claims 4, 7, 11, and 14 were previously canceled. With the filed amendment, claims 17 and 21 are also canceled. Claims 1-3, 5-6, 8-10, 12-13, 15-16, 18-20, and 22 are allowed.
Response to Amendment/Arguments
The amendment filed 04/04/2025 has been entered and applicant’s arguments filed 04/04/2025 have been fully considered. Applicant has argued that the claims are not taught by the prior art because they have been amended to include the features of claims 17 and 21, which were previously indicated as having allowable subject matter. The examiner agrees and has withdrawn the claim rejections under 35 U.S.C. § 103 accordingly.
Reasons for Allowance
The following is the examiner’s statement of reasons for allowance:
The closest prior art of record is Park (US 2022/0332319 A1) in view of Shalev-Shwartz et al. (US 2021/0078570 A1), hereinafter referred to as Shalev, and further in view of Silva et al. (US 2021/0370921 A1), hereinafter referred to as Silva.
Regarding claim 1:
Park discloses the following limitations:
“An apparatus for preventing car collision, the apparatus comprising: a communication unit configured to communicate with a preceding vehicle or a camera.” (Park ¶ 28 discloses “a vehicle including: a communicator configured to communicate with a plurality of other vehicle.” As shown in Park FIG. 7B, the plurality of vehicles can include a second vehicle 2 that travels in front of the ego vehicle 1. This at least teaches a communication unit configured to communicate with a preceding vehicle as claimed.)
“and a processor operatively connected to the communication unit.” (Park ¶¶ 150 and 219: “the cruise control device may correspond to a second controller, and may communicate with … a communicator 250,” where “The second controller 280 may be implemented as a single processor.”)
“wherein the processor is configured to: obtain information about a third vehicle in front of the preceding vehicle acquired by the preceding vehicle.” (Park FIG. 7B shows a third vehicle 3 in front of the second vehicle 2. Further, Park ¶ 274 discloses that “The vehicle may also determine whether the third vehicle is in a stationary state from the travel information and position information of the third vehicle received by the communicator 250.”)
“calculate a deceleration amount for preventing a collision with the third vehicle based on the information about the third vehicle.” (Park ¶ 275: “As shown in FIG. 7B, the vehicle may determine whether there is an obstacle that is stopped in the travelling path, and upon determining that there is an obstacle that is stopped in the path, acquire a target deceleration based on position information of the obstacle in the stationary state and the travelling velocity information of the first vehicle, and control deceleration and braking based on the acquired target deceleration.”)
“predict a preset event related to the preceding vehicle based on a change in a position of the preceding vehicle.” (Park ¶¶ 262-263: “as shown in FIG. 7A, the vehicle may, upon determining that the second vehicle 2 is not detected through the obstacle detector 220, determine that the path of the second vehicle 2 is suddenly changed (cut out). The sudden path change may include a state in which the distance to the obstacle acquired based on obstacle information detected by the obstacle detector changes to a degree greater than or equal to a reference distance within a predetermined time.”)
“and control an autonomous driving vehicle based on the deceleration amount according to a prediction result regarding the preset event.” (Park ¶¶ 267-268: “the vehicle decelerates and performs braking while maintaining the cruise control mode based on the travel information of the third vehicle in front of the vehicle during execution of the cruise control mode, so that the cruise control mode is prevented from being deactivated, thereby eliminating a hassle of the driver in resetting the cruise control mode. As described above, even when the first vehicle 1 controls acceleration because the second vehicle 2 travelling in front of the first vehicle departs from the front of the first vehicle 1, the first vehicle 1 obtains the amount of acceleration in consideration of the travel information of the third vehicle 3 travelling in front of the second vehicle 2, so that the first vehicle 1 is prevented from colliding with the third vehicle 3.”)
The following limitations are not specifically disclosed by Park, but are taught by Shalev:
“wherein the processor detects the change in the position of the preceding vehicle and predicts occurrence of the preset event using an event prediction model based on the detected change in the position of the preceding vehicle.” (Shalev ¶ 189: “If the distance between the leading vehicle's instantaneous position and the look-ahead point varies during the specific period of time, and the cumulative sum of variation exceeds a predetermined threshold (for example, 0.3 to 0.4 meters on a straight road, 0.7 to 0.8 meters on a moderately curvy road, and 1.3 to 1.7 meters on a road with sharp curves), processing unit 110 may determine that the leading vehicle is likely changing lanes.” The leading vehicle changing lanes reads on the “preset event” as claimed.)
“wherein the event prediction model is trained based on information related to the detected change in the position of the preceding vehicle using a machine learning algorithm.” (Shalev ¶ 190: “At step 584, processing unit 110 may determine whether or not leading vehicle 200 is changing lanes based on the analysis performed at step 582. … the analysis may make use of trained system (e.g., a machine learning or deep learning system), which may, for example, estimate a future path ahead of a current location of a vehicle based on an image captured at the current location.”)
to define the preset event based on “the point in the adjacent lane being separated by a predetermined distance from any one lane marking of the lane in which the preceding vehicle is traveling.” (Shalev ¶ 188: “If the difference in curvature of the snail trail and the expected curvature of the road segment exceeds a predetermined threshold, processing unit 110 may determine that the leading vehicle is likely changing lanes.”)
The combination of Park and Shalev does not specifically teach “wherein the event prediction model is trained from a first point in time to a second point in time,” where the first and second points in time occur while the autonomous driving vehicle is traveling. However, Silva does teach this limitation. (Silva ¶ 113: “the vehicle safety system 534 may train and execute machine-learned models based on observed vehicle behaviors and/or movement patterns while driving or performing particular maneuvers (e.g., backing, parking, merging, etc.).”)
None of the references in the prior art of record taken together or in combination disclose “wherein the first point in time is a point in time at which the preceding vehicle starts to move from a same lane as a lane in which the autonomous driving vehicle is traveling to an adjacent lane, wherein the second point in time is a point in time at which a portion of the preceding vehicle is located at a point in the adjacent lane … and wherein the preset event related to the preceding vehicle is predicted to occur at the second point in time” as required by claim 1.
Regarding claim 8:
Claim 8 is allowed using the same rationale applied to claim 1 above, mutatis mutandis.
Regarding claims 2-3, 5-6, 9-10, 12-13, 15-16, 18-20, and 22:
Claims 2-3, 5-6, 9-10, 12-13, 15-16, 18-20, and 22 are allowed due to their dependency upon allowed claims 1 and 8 above.
Since none of the prior art references taken separately or in combination disclose the remaining elements and features of the claimed invention in combination with the other elements and features of the claimed invention, applicant’s invention overcomes the prior art of record.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Haggblade et al. (US 2021/0269065 A1) ¶ 97 discloses “receiving log data associated with vehicles in an environment; detecting an event in the log data, wherein an event corresponds to a cut-in maneuver performed by a vehicle; determining a period of time associated with the event; accessing a portion of the log data that corresponds to the period of time; generating training data based at least in part on converting the portion of the log data that corresponds to the period of time into a top-down representation of the environment; inputting the training data into a model.”

CITED REFERENCE SUMMARIES: Ref US20210078570A1: Claims:
- 93 . (canceled)
An automated driving system for a host vehicle, the system comprising: an interface to obtain sensing data of an environment in a vicinity of the host vehicle, the sensing data captured from at least one sensor device of the host vehicle; and at least one processing device configured to: determine a planned navigational action for accomplishing a navigational goal of the host vehicle; identify, from the sensing data, a target vehicle in the environment of the host vehicle; predict a distance between the host vehicle and the target vehicle that would result if the planned navigational action was taken; obtain a current braking capability of the host vehicle, a current acceleration capability of the host vehicle, and a current longitudinal speed of the host vehicle; determine a current host vehicle stopping distance, based on the current braking capability of the host vehicle, the current acceleration capability of the host vehicle, and the current longitudinal speed of the host vehicle, the current braking capability of the host vehicle comprising a minimum braking rate that is less than a maximum braking rate of the host vehicle, and the current braking capability of the host vehicle limited by at least one condition relating to deceleration of the host vehicle; obtain a current braking capability of the target vehicle and a current longitudinal speed of the target vehicle, the current braking capability of the target vehicle based on a maximum defined braking rate of the target vehicle; determine a current target vehicle braking distance, based on the current longitudinal speed of the target vehicle and the current braking capability of the target vehicle; and cause the host vehicle to implement the planned navigational action when the predicted distance of the planned navigational action is greater than a minimum safe longitudinal distance, the minimum safe longitudinal distance being calculated based on the current host vehicle stopping distance and the current target vehicle braking distance.
an interface to obtain sensing data of an environment in a vicinity of the host vehicle, the sensing data captured from at least one sensor device of the host vehicle; and
at least one processing device configured to: determine a planned navigational action for accomplishing a navigational goal of the host vehicle; identify, from the sensing data, a target vehicle in the environment of the host vehicle; predict a distance between the host vehicle and the target vehicle that would result if the planned navigational action was taken; obtain a current braking capability of the host vehicle, a current acceleration capability of the host vehicle, and a current longitudinal speed of the host vehicle; determine a current host vehicle stopping distance, based on the current braking capability of the host vehicle, the current acceleration capability of the host vehicle, and the current longitudinal speed of the host vehicle, the current braking capability of the host vehicle comprising a minimum braking rate that is less than a maximum braking rate of the host vehicle, and the current braking capability of the host vehicle limited by at least one condition relating to deceleration of the host vehicle; obtain a current braking capability of the target vehicle and a current longitudinal speed of the target vehicle, the current braking capability of the target vehicle based on a maximum defined braking rate of the target vehicle; determine a current target vehicle braking distance, based on the current longitudinal speed of the target vehicle and the current braking capability of the target vehicle; and cause the host vehicle to implement the planned navigational action when the predicted distance of the planned navigational action is greater than a minimum safe longitudinal distance, the minimum safe longitudinal distance being calculated based on the current host vehicle stopping distance and the current target vehicle braking distance.
determine a planned navigational action for accomplishing a navigational goal of the host vehicle;
identify, from the sensing data, a target vehicle in the environment of the host vehicle;
predict a distance between the host vehicle and the target vehicle that would result if the planned navigational action was taken;
obtain a current braking capability of the host vehicle, a current acceleration capability of the host vehicle, and a current longitudinal speed of the host vehicle;
determine a current host vehicle stopping distance, based on the current braking capability of the host vehicle, the current acceleration capability of the host vehicle, and the current longitudinal speed of the host vehicle, the current braking capability of the host vehicle comprising a minimum braking rate that is less than a maximum braking rate of the host vehicle, and the current braking capability of the host vehicle limited by at least one condition relating to deceleration of the host vehicle;
obtain a current braking capability of the target vehicle and a current longitudinal speed of the target vehicle, the current braking capability of the target vehicle based on a maximum defined braking rate of the target vehicle;
determine a current target vehicle braking distance, based on the current longitudinal speed of the target vehicle and the current braking capability of the target vehicle; and
cause the host vehicle to implement the planned navigational action when the predicted distance of the planned navigational action is greater than a minimum safe longitudinal distance, the minimum safe longitudinal distance being calculated based on the current host vehicle stopping distance and the current target vehicle braking distance.
The system of claim 94 , wherein the minimum safe longitudinal distance is further based on a current acceleration distance that corresponds to a distance the host vehicle can travel over a time period at a maximum acceleration capability of the host vehicle, starting from the current longitudinal speed of the host vehicle.
The system of claim 94 , wherein the minimum safe longitudinal distance is further calculated based on a response time associated with the host vehicle.
The system of claim 94 , wherein the at least one condition relating to deceleration includes a vehicle condition that limits performance of a braking system of the host vehicle.
The system of claim 94 , wherein the at least one condition relating to deceleration includes weather and road conditions of the environment.
The system of claim 94 , wherein the current longitudinal speed of the target vehicle is determined from the sensing data.
The system of claim 99 , wherein the current longitudinal speed of the target vehicle is determined from output from at least one of a LIDAR system or a RADAR system of the host vehicle.
The system of claim 94 , wherein the planned navigational action causes at least one of steering, braking, or accelerating in the host vehicle.
The system of claim 94 , wherein the at least one sensor device includes a camera, and wherein the sensing data includes at least one image captured from the camera.
At least one non-transitory machine-readable storage medium comprising instructions stored thereupon, which when executed by a processor of a navigation system of a host vehicle, cause the processor to perform operations comprising: obtaining sensing data of an environment in a vicinity of the host vehicle, the sensing data captured from at least one sensor device of the host vehicle; determining a planned navigational action for accomplishing a navigational goal of the host vehicle; identifying, from the sensing data, a target vehicle in the environment of the host vehicle; predicting a distance between the host vehicle and the target vehicle that would result if the planned navigational action was taken; obtaining a current braking capability of the host vehicle, a current acceleration capability of the host vehicle, and a current longitudinal speed of the host vehicle; determining a current host vehicle stopping distance, based on the current braking capability of the host vehicle, the current acceleration capability of the host vehicle, and the current longitudinal speed of the host vehicle, the current braking capability of the host vehicle comprising a minimum braking rate that is less than a maximum braking rate of the host vehicle, and the current braking capability of the host vehicle limited by at least one condition relating to deceleration of the host vehicle; obtaining a current braking capability of the target vehicle and a current longitudinal speed of the target vehicle, the current braking capability of the target vehicle based on a maximum defined braking rate of the target vehicle; determining a current target vehicle braking distance, based on the current longitudinal speed of the target vehicle and the current braking capability of the target vehicle; and causing the host vehicle to implement the planned navigational action when the predicted distance of the planned navigational action is greater than a minimum safe longitudinal distance, the minimum safe longitudinal distance being calculated based on the current host vehicle stopping distance and the current target vehicle braking distance.
obtaining sensing data of an environment in a vicinity of the host vehicle, the sensing data captured from at least one sensor device of the host vehicle;
determining a planned navigational action for accomplishing a navigational goal of the host vehicle;
identifying, from the sensing data, a target vehicle in the environment of the host vehicle;
predicting a distance between the host vehicle and the target vehicle that would result if the planned navigational action was taken;
obtaining a current braking capability of the host vehicle, a current acceleration capability of the host vehicle, and a current longitudinal speed of the host vehicle;
determining a current host vehicle stopping distance, based on the current braking capability of the host vehicle, the current acceleration capability of the host vehicle, and the current longitudinal speed of the host vehicle, the current braking capability of the host vehicle comprising a minimum braking rate that is less than a maximum braking rate of the host vehicle, and the current braking capability of the host vehicle limited by at least one condition relating to deceleration of the host vehicle;
obtaining a current braking capability of the target vehicle and a current longitudinal speed of the target vehicle, the current braking capability of the target vehicle based on a maximum defined braking rate of the target vehicle;
determining a current target vehicle braking distance, based on the current longitudinal speed of the target vehicle and the current braking capability of the target vehicle; and
causing the host vehicle to implement the planned navigational action when the predicted distance of the planned navigational action is greater than a minimum safe longitudinal distance, the minimum safe longitudinal distance being calculated based on the current host vehicle stopping distance and the current target vehicle braking distance.
The machine-readable storage medium of claim 103 , wherein the minimum safe longitudinal distance is further based on a current acceleration distance that corresponds to a distance the host vehicle can travel over a time period at a maximum acceleration capability of the host vehicle, starting from the current longitudinal speed of the host vehicle.
The machine-readable storage medium of claim 103 , wherein the minimum safe longitudinal distance is further calculated based on a response time associated with the host vehicle.
The machine-readable storage medium of claim 103 , wherein the at least one condition relating to deceleration includes a vehicle condition that limits performance of a braking system of the host vehicle.
The machine-readable storage medium of claim 103 , wherein the at least one condition relating to deceleration includes weather and road conditions of the environment.
The machine-readable storage medium of claim 103 , wherein the current longitudinal speed of the target vehicle is determined from the sensing data.
The machine-readable storage medium of claim 108 , wherein the current longitudinal speed of the target vehicle is determined from output from at least one of a LIDAR system or a RADAR system of the host vehicle.
The machine-readable storage medium of claim 103 , wherein the planned navigational action causes at least one of steering, braking, or accelerating in the host vehicle.
The machine-readable storage medium of claim 103 , wherein the at least one sensor device includes a camera, and wherein the sensing data includes at least one image captured from the camera.
An apparatus, comprising: means for obtaining sensing data of an environment in a vicinity of a host vehicle, the sensing data captured from at least one sensor device of the host vehicle; at least one processing means for: obtaining sensing data of an environment in a vicinity of the host vehicle, the sensing data captured from at least one sensor device of the host vehicle; determining a planned navigational action for accomplishing a navigational goal of the host vehicle; identifying, from the sensing data, a target vehicle in the environment of the host vehicle; predicting a distance between the host vehicle and the target vehicle that would result if the planned navigational action was taken; obtaining a current braking capability of the host vehicle, a current acceleration capability of the host vehicle, and a current longitudinal speed of the host vehicle; determining a current host vehicle stopping distance, based on the current braking capability of the host vehicle, the current acceleration capability of the host vehicle, and the current longitudinal speed of the host vehicle, the current braking capability of the host vehicle comprising a minimum braking rate that is less than a maximum braking rate of the host vehicle, and the current braking capability of the host vehicle limited by at least one condition relating to deceleration of the host vehicle; obtaining a current braking capability of the target vehicle and a current longitudinal speed of the target vehicle, the current braking capability of the target vehicle based on a maximum defined braking rate of the target vehicle; determining a current target vehicle braking distance, based on the current longitudinal speed of the target vehicle and the current braking capability of the target vehicle; and causing the host vehicle to implement the planned navigational action when the predicted distance of the planned navigational action is greater than a minimum safe longitudinal distance, the minimum safe longitudinal distance being calculated based on the current host vehicle stopping distance and the current target vehicle braking distance.
means for obtaining sensing data of an environment in a vicinity of a host vehicle, the sensing data captured from at least one sensor device of the host vehicle;
at least one processing means for: obtaining sensing data of an environment in a vicinity of the host vehicle, the sensing data captured from at least one sensor device of the host vehicle; determining a planned navigational action for accomplishing a navigational goal of the host vehicle; identifying, from the sensing data, a target vehicle in the environment of the host vehicle; predicting a distance between the host vehicle and the target vehicle that would result if the planned navigational action was taken; obtaining a current braking capability of the host vehicle, a current acceleration capability of the host vehicle, and a current longitudinal speed of the host vehicle; determining a current host vehicle stopping distance, based on the current braking capability of the host vehicle, the current acceleration capability of the host vehicle, and the current longitudinal speed of the host vehicle, the current braking capability of the host vehicle comprising a minimum braking rate that is less than a maximum braking rate of the host vehicle, and the current braking capability of the host vehicle limited by at least one condition relating to deceleration of the host vehicle; obtaining a current braking capability of the target vehicle and a current longitudinal speed of the target vehicle, the current braking capability of the target vehicle based on a maximum defined braking rate of the target vehicle; determining a current target vehicle braking distance, based on the current longitudinal speed of the target vehicle and the current braking capability of the target vehicle; and causing the host vehicle to implement the planned navigational action when the predicted distance of the planned navigational action is greater than a minimum safe longitudinal distance, the minimum safe longitudinal distance being calculated based on the current host vehicle stopping distance and the current target vehicle braking distance.
The apparatus of claim 112 , further comprising: sensing means for capturing the sensing data.
sensing means for capturing the sensing data.
Ref US20130054106A1: Claims:
A method for computationally predicting future movement behaviors of at least one target object, said method comprising the steps of: producing sensor data by at least one sensor physically sensing the environment of a host vehicle, computing a plurality of movement behavior alternatives of a target object sensed by the sensor(s), by predicting movement behaviors of the traffic target vehicle applying a context based prediction step using indirect indicators or indicator combinations derived from said sensor data, wherein said context based prediction step uses a set of classifiers, each classifier estimating a probability that said sensed target object will execute a movement behavior at a time, wherein the probability is calculated based on a feature vector calculated for each classifier, validating the movement behavior alternatives by a physical prediction comparing measured points with trajectories of situation models and determining at least one trajectory indicating at least one possible behavior of the traffic participant, wherein the at least one movement behavior is a sub-set of the possible movement behavior alternatives, estimating at least one future position of the traffic participant based on the at least one trajectory, and outputting a signal representing the estimated future position.
producing sensor data by at least one sensor physically sensing the environment of a host vehicle,
computing a plurality of movement behavior alternatives of a target object sensed by the sensor(s), by predicting movement behaviors of the traffic target vehicle applying a context based prediction step using indirect indicators or indicator combinations derived from said sensor data,
wherein said context based prediction step uses a set of classifiers, each classifier estimating a probability that said sensed target object will execute a movement behavior at a time, wherein the probability is calculated based on a feature vector calculated for each classifier,
validating the movement behavior alternatives by a physical prediction comparing measured points with trajectories of situation models and determining at least one trajectory indicating at least one possible behavior of the traffic participant, wherein the at least one movement behavior is a sub-set of the possible movement behavior alternatives,
estimating at least one future position of the traffic participant based on the at least one trajectory, and
outputting a signal representing the estimated future position.
The method of claim 1 , wherein the estimated future position signal is supplied to an actuator or display means of the vehicle.
wherein the estimated future 
[Truncated to input limit]