DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
1. Acknowledgment is made of applicant’s claim for priority based on foreign priority application filed in the People’s Republic of China on 06/28/2024.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
3. Claim(s) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over EGGERT et al (U.S. 2020/0231149) and further in view of Schmidt et al (U.S. 2022/0089131).
1. As per claims 1, 12 and 13 EGGERT disclosed a method for determining time to collision, comprising: determining ego vehicle data of an ego vehicle and detection data of a target object; determining based on the ego vehicle data and the detection data [Generally, the priority relationship between the ego-vehicle and one other traffic participant may be determined in an arbitrary way, which can even include information obtained from road infrastructure. For example, traffic lights may transmit their status to the ego-vehicle so the method can take into consideration the correct current status even at intersections where the priority relationship may change from time to time. Further, it is to be noted that the priority relationship is considered between the ego-vehicle and one further vehicle. In a situation where there are a plurality of other traffic participants] (Paragraph. 0010), a first predicted driving trajectory of the ego vehicle and a second predicted driving trajectory of the target object respectively [ determined that the trajectory of the ego-vehicle and the trajectory of the at least one traffic participant intersect or merge, a prediction model defining a delayed change of velocity for the at least one traffic participant within the prediction horizon is selected in the selecting step] (Paragraph. 0012);
determining a collision condition between the ego vehicle and the target object based on first driving data of the ego vehicle data, second driving data of the detection data, and the second predicted driving trajectory [The behavior relevant score can be calculated as risk and indicate, for the hypothetical future trajectory of the ego-vehicle, collision probability, collision severity, product of collision probability and collision severity, Time-of-Closest-Encounter, Time-To-Closest-Encounter, Position-of-Closest-Encounter or Distance-of-Closest-Encounter] (Paragraph. 0016);
determining a plurality of trajectory instructions based on the first predicted driving trajectory, and the second predicted driving trajectory [The prediction module 16 predicts a future behavior for the traffic participant 18 based on the selected prediction model, the information received from the image-processing module 11 and the signals received from the front radar 2 and the rear radar 3 and calculates a behavior relevant score for ego-vehicle 1 based on the calculated trajectories of ego-vehicle 1 and the traffic participant 18. It is to be noted that most part of the future behavior prediction is performed as known in the prior art. This means that the most likely future trajectory is identified. According to the invention, however, the prediction is based on a specific prediction model that comprises a definition of a speed profile over the prediction horizon. The specific prediction model is selected based on an identified priority.] (Paragraph. 0047); and
However, EGGERT did not disclose, determining the time to the collision between the ego vehicle and the target object based on the ego vehicle data, the detection data, the collision condition, and the plurality of trajectory instructions.
In the same field of endeavor Schmidt disclosed, “In the specific embodiment described here, each ego grid cell of ego grid 16 and each surroundings grid cell of surroundings grid 18 includes either a temporal occupation t.sub.i1, t.sub.e1 or t.sub.i2, t.sub.e2 between entry time t.sub.i1 or t.sub.i2 and exit time t.sub.e2 or t.sub.e2, or is equal to zero. By comparing temporal occupations t.sub.i1, t.sub.e1 and t.sub.i2, t.sub.e2 of each ego grid cell of ego grid 16 to their particular associated surroundings grid cell of surroundings grid 18, it may thus be checked in a comparatively rapid and very efficient manner whether grid cells with which the same subarea of the travel surroundings is associated are at the same time “occupied” by ego vehicle 12 and by at least one other object, which significantly increases the likelihood of a collision 26 of ego vehicle 12, driving along the travel trajectory, with the other object. Likewise, it may be recognized relatively early and very reliably, based on the comparison of temporal occupations t.sub.i1, t.sub.e1 and t.sub.i2, t.sub.e2 of each ego grid cell with the particular surroundings grid cell associated with it, when no grid cells, with which the same subarea of the travel surroundings is associated, are at the same time “occupied” by ego vehicle 12 and by at least one other object, and therefore a collision 26 of ego vehicle 12, driving along the travel trajectory, with an obstacle or a further road user may be ruled out with a high probability. Therefore, device 10 may also be reliably used, also in comparatively safety-critical systems, for protecting the travel trajectory of ego vehicle 12” (Paragraph. 0028).
It would have been obvious to one having ordinary skill in the art before the effective filing was made to have incorporated, “ In the specific embodiment described here, each ego grid cell of ego grid 16 and each surroundings grid cell of surroundings grid 18 includes either a temporal occupation t.sub.i1, t.sub.e1 or t.sub.i2, t.sub.e2 between entry time t.sub.i1 or t.sub.i2 and exit time t.sub.e2 or t.sub.e2, or is equal to zero. By comparing temporal occupations t.sub.i1, t.sub.e1 and t.sub.i2, t.sub.e2 of each ego grid cell of ego grid 16 to their particular associated surroundings grid cell of surroundings grid 18, it may thus be checked in a comparatively rapid and very efficient manner whether grid cells with which the same subarea of the travel surroundings is associated are at the same time “occupied” by ego vehicle 12 and by at least one other object, which significantly increases the likelihood of a collision 26 of ego vehicle 12, driving along the travel trajectory, with the other object. Likewise, it may be recognized relatively early and very reliably, based on the comparison of temporal occupations t.sub.i1, t.sub.e1 and t.sub.i2, t.sub.e2 of each ego grid cell with the particular surroundings grid cell associated with it, when no grid cells, with which the same subarea of the travel surroundings is associated, are at the same time “occupied” by ego vehicle 12 and by at least one other object, and therefore a collision 26 of ego vehicle 12, driving along the travel trajectory, with an obstacle or a further road user may be ruled out with a high probability. Therefore, device 10 may also be reliably used, also in comparatively safety-critical systems, for protecting the travel trajectory of ego vehicle 12 as taught Schmidt in the method and system of EGGERT to determine time overlap method to compute a specific time to collision (TTC) for the ego vehicle and target object.
2. As per claims 2,14 EGGERT-Schmidth disclosed wherein the determining a collision condition between the ego vehicle and the target object based on first driving data of the ego vehicle data, second driving data of the detection data (EGGERT, Paragraph. 0016), and the second predicted driving trajectory comprises: determining a relative motion direction between the ego vehicle and the target object based on the first driving data of the ego vehicle data and the second driving data of the detection data; determining a turning radius of the ego vehicle based on the first driving data (EEGERT, Paragraph. 0047); determining an intercept of the second predicted driving trajectory in an ego vehicle coordinate system; and determining the collision condition between the ego vehicle and the target object based on the intercept, the turning radius of the ego vehicle, and the relative motion direction between the ego vehicle and the target object (EGGERT, Paragraph. 0063).
3. As per claims 3,15 EGGERT-Schmidth disclosed wherein the determining the time to collision between the ego vehicle and the target object based on the ego vehicle data, the detection data, the collision condition, and the plurality of trajectory intersections comprises: determining positions of a plurality of first target points on the ego vehicle based on first size data of the ego vehicle data ;determining positions of a plurality of second target points on the target object based on second size data and position data of the detection data; predicting, based on the first driving data and the positions of the plurality of first target points (EGGERT, Paragraph. 0019), first times at which respective first target points pass through the plurality of trajectory intersections; predicting, based on the second driving data and the positions of the plurality of second target points, a second times at which respective second target points pass through the plurality of trajectory intersections; and determining the time to collision between the ego vehicle and the target object based on the first times, the second times, and the collision condition (EGGERT, Paragraph. 0010).
4. As per claims 4,16 EGGERT-Schmidth disclosed wherein the determining the time to collision between the ego vehicle and the target object based on the first times, the second times, and the collision condition comprises: determining whether there is a risk of collision between the ego vehicle and the target object based on the first times and the second times; and determining, based on the first times, the second times, and the collision condition, the time to collision between the ego vehicle and the target object in response to that there is the risk of collision between the ego vehicle and the target object (EGGERT, Paragraph. 0019).
5. As per claims 5,17 EGGERT-Schmidth disclosed wherein the determining whether there is a risk of collision between the ego vehicle and the target object based on the first times and the second times comprises: sorting a plurality of the first times based on values of the first times, and determining a first driving-in time and a first driving-away time for the ego vehicle among the plurality of first times based on a sorting result; sorting a plurality of second times based on values of the second times (EGGERT, Paragraph. 0050), and determining a second driving-in time and a second driving-away time for the target object among the plurality of second times based on a sorting result; and determining whether there is the risk of collision between the ego vehicle and the target object based on the first driving-in time, the first driving-away time, the second driving-in time, and the second driving-away time (EGGERT, Paragraph. 0039).
6. As per claims 6,18 EGGERT-Schmidth disclosed wherein the determining, based on the ego vehicle data and the detection data, a first predicted driving trajectory of the ego vehicle and a second predicted driving trajectory of the target object, respectively comprises: determining a turning radius of the ego vehicle based on the first driving data of the ego vehicle data; determining the first predicted driving trajectory based on the turning radius of the ego vehicle and the first size data of the ego vehicle data; determining trajectory slope of the target object based on the second driving data of the detection data; and determining the second predicted driving trajectory based on the trajectory slope of the target object and the detection data (EGGERT, Paragraph. 0008).
7. As per claims 7,19 EGGERT-Schmidth disclosed wherein the determining the first predicted driving trajectory based on the turning radius of the ego vehicle and the first size data of the ego vehicle data comprises: determining turning radius of the plurality of first target points on the ego vehicle based on the turning radius of the ego vehicle and the first size data (Schmidt, Paragraph. 0033); determining predicted driving trajectories for the plurality of the first target points based on the turning radius of the plurality of the first target points and the ego vehicle coordinate system; and determining the first predicted driving trajectory based on the predicted driving trajectories of the first target points (Schmidt, Paragraph. 0025). Claim 7 has the same motivation as to claim 1.
8. As per claims 8,20 EGGERT-Schmidth disclosed wherein the determining the second predicted driving trajectory based on the trajectory slope of the target object and the detection data comprises: determining positions of the plurality of second target points on the target object based on second size data and position data of the detection data (EGGERT, Paragraph. 0036); determining predicted driving trajectories for the plurality of the second target points based on the positions of the plurality of the second target points and the trajectory slope; and determining the second predicted driving trajectory based on the predicted driving trajectories of the second target points (EGGERT, Paragraph. 0058).
9. As per claim 9 EGGERT-Schmidth disclosed wherein the determining the second predicted driving trajectory based on the predicted driving trajectories of the second target points comprises: determining a historical trajectory of the target object; and determining the second predicted driving trajectory based on the historical trajectory of the target object and the predicted driving trajectories of the second target points (EGGERT, Paragraph. 0019).
10. As per claim 10 EGGERT-Schmidth disclosed wherein the determining the second predicted driving trajectory based on the historical trajectory of the target object and the predicted driving trajectories of the second target points comprises: determining a predicted position and historical trajectory slope of the target object based on the historical trajectory of the target object (EGGERT, Paragraph. 019); determining predicted trajectory slopes of the predicted driving trajectories of the second target points based on the position data of the detection data and the predicted driving trajectories of the second target points; performing correction on the predicted driving trajectories of the second target points based on a difference between the predicted trajectory slope and the historical trajectory slope and a difference between the position data and the predicted position; and determining the second predicted driving trajectory based on the corrected predicted driving trajectories of the second target points (EGGERT, Paragraph. 0010).
11. As per claim 11 EGGERT-Schmidth disclosed wherein the determining a predicted position and historical trajectory slope of the target object based on the historical trajectory of the target object comprises: determining, based on a plurality of historical trajectory points in the historical trajectory, trajectory point slopes corresponding to any two trajectory points among the plurality of historical trajectory points; determining average slope corresponding to the historical trajectory based on the trajectory point slopes (EGGERT, Paragraph. 0037); determining the historical trajectory slope of the target object based on the trajectory point slopes and the average slope; and determining the predicted position of the target object based on the historical trajectory slope, the second driving data of the detection data, the position data of the detection data, and time interval between adjacent trajectory points in the historical trajectory (EGGERT, Paragraph. 0042).
Conclusion
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examiner should be directed to Adnan Mirza whose telephone number is (571)-272-3885.
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examiner’s supervisor, Faris Almatrahi can be reached on (313)-446-4821.
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/ADNAN M MIRZA/Primary Examiner, Art Unit 3667
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