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 .
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 3-5, 11-13 and 15-17 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Hayakawa (US Patent Application Publication 2021/0237739).
Regarding claims 1 and 3-5, Hayakawa discloses an apparatus for an autonomous vehicle, the apparatus comprising:
a sensor (11, 12) configured to detect an object around the autonomous vehicle [0033-0034];
a communication interface (17) configured to communicate with an external device; and
a processor configured to control an autonomous driving controller [0041-0042, 0076],
wherein the processor is configured to:
generate, based on at least one of sensor data generated by the sensor and communication data collected by the communication interface, external environment data [0051-0052, the sensors detect external environment data such as a branching point in the road, exit on an expressway, preceding vehicle, etc.];
determine, using the external environment data, whether a current lane in which the autonomous vehicle is driving is an optimal lane [0051-0052, 0078, determines that a lane change may be necessary based on the detected external environment data];
create, based on determining that the current lane in which the autonomous vehicle is driving is not the optimal lane, a lane change strategy including an expected number of lane changes [0051-0052, the lane keeping control may determine that successive lane changes from lane 1 to lane 3 via lane 2 is necessary, as shown in Figure 3; 0081-0084, Figures 7A-7D show examples of lane change strategies];
cause, based on the lane change strategy, the autonomous driving controller to control the autonomous vehicle to change a lane once and move to a changed current lane [0051-0052, 0117, the controller performs an autonomous lane change control from lane 1 to lane 2, lower than the two expected lane changes and presents lane change information to the driver, as shown in Figure 3];
determine whether the autonomous vehicle has changed lanes as many times as the expected number of lane changes [0117, the controller determines whether the first autonomous lane change control is completed, as shown in step S23 of Figure 6C];
determine, based on determining that the autonomous vehicle has not changed as many times as the expected number of lane changes, whether the lane change strategy is valid in the changed current lane [0142]; and
cause, based on whether the lane change strategy is valid in the changed current lane, the autonomous driving controller to control autonomous driving of the autonomous vehicle [0145];
cause, based on determining that the autonomous vehicle has changed lanes as many times as the expected number of lane changes, the autonomous vehicle to maintain the changed current lane [0135];
cause, based on determining that the lane change strategy is valid in the changed current lane, the autonomous driving controller to control the autonomous vehicle to additionally change the changed current lane based on the lane change strategy [0142];
determine, based on determining that the lane change strategy is not valid in the changed current lane, whether the changed current lane is an optimal lane [0145, the process determines whether the current lane is the optimal lane by detecting the object ranges based on the external environment data as the previous step assessing whether the current lane is optimal].
Regarding claim 11, Hayakawa further discloses wherein the communication interface is configured to collect at least one of vehicle-to-vehicle based surrounding vehicle behavior information, infrastructure to vehicle based traffic information, surrounding control system, cloud based driving related service or edge platform based traffic information [0051-0052, the sensors detect external environment data such as a branching point in the road, exit on an expressway, preceding vehicle, etc.].
Regarding claim 12, Hayakawa further discloses wherein the processor is configured to determine whether the current lane in which the autonomous vehicle is driving is the optimal lane based on at least one of: surrounding objects of the autonomous vehicle, lane change regulations in a road, traffic signs, or the external environment data [0051-0052, 0078, determines that a lane change may be necessary based on the detected external environment data];
Regarding claim 13, Hayakawa discloses a method performed by an autonomous vehicle, the method comprising:
generating, based on at least one of sensor data generated by a sensor (11, 12) of the autonomous vehicle and communication data collected by a communication interface (17) of the autonomous vehicle, external environment data [0033-0034, 0051-0052, the sensors detect external environment data such as a branching point in the road, exit on an expressway, preceding vehicle, etc.];
determining, using the external environment data, whether a current lane in which the autonomous vehicle is driving is an optimal lane [0051-0052, 0078, determines that a lane change may be necessary based on the detected external environment data];
creating, based on determining that the current lane in which the autonomous vehicle is driving is not the optimal lane, a lane change strategy including an expected number of lane changes [0051-0052, the lane keeping control may determine that successive lane changes from lane 1 to lane 3 via lane 2 is necessary, as shown in Figure 3; 0081-0084, Figures 7A-7D show examples of lane change strategies];
causing, based on the lane change strategy, an autonomous driving controller to control the autonomous vehicle to change a lane once and move to a changed current lane [0041-0042, 0051-0052, 0117, the controller performs an autonomous lane change control from lane 1 to lane 2, lower than the two expected lane changes and presents lane change information to the driver, as shown in Figure 3];
determining whether the autonomous vehicle has changed lanes as many times as the expected number of lane changes [0117, the controller determines whether the first autonomous lane change control is completed, as shown in step S23 of Figure 6C];
determining, based on determining that the autonomous vehicle has not changed as many times as the expected number of lane changes, whether the lane change strategy is valid in the changed current lane [0142]; and
causing, based on whether the lane change strategy is valid in the changed current lane, the autonomous driving controller to control autonomous driving of the autonomous vehicle [0145];
Regarding claim 15, Hayakawa further discloses based on determining that the autonomous vehicle has changed lanes as many times as the expected number of lane changes, causing the autonomous vehicle to maintain the changed current lane [0135].
Regarding claim 16, Hayakawa further discloses causing, based on determining that the lane change strategy is valid in the changed current lane, the autonomous driving controller to control the autonomous vehicle to additionally change the changed current lane based on the lane change strategy [0142].
Regarding claim 17, Hayakawa further discloses based on determining that the lane change strategy is not valid in the changed current lane, determining whether the changed current lane is an optimal lane [0145, the process determines whether the current lane is the optimal lane by detecting the object ranges based on the external environment data as the previous step assessing whether the current lane is optimal].
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.
Claim(s) 2 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hayakawa (US Patent Application Publication 2021/0237739) in view of Ratnasingam (US Patent Number 9,672,734).
Regarding claims 2 and 14, Hayakawa discloses the apparatus of claim 1 and the method of claim 13 wherein the processor is configured to cause, based on determining that the expected number of lane changes is equal to or less than a maximum allowable number, the autonomous driving controller to control the autonomous vehicle to change the lane once based on the lane change strategy, the lane change strategy corresponding to a discretionary lane change.
Hayakawa does not disclose the processor determining, based on the lane change strategy , whether the expected number of lane changes is equal to or less than a maximum allowable number preset by a user.
Ratnasingam discloses an autonomous vehicle driving system (Title) configured to determine a maximum allowable number of lane changes based on a user setting (Col. 21, lines 31-42).
Ratnasingam teaches that limiting the number of lane changes may effectively reduce travel time on particular road segments (Col. 21, lines 31-42). Ratnasingam teaches that the driver may prefer to limit a number of lane changes for safety reasons or to reduce driving complexity (Col. 21, lines 31-42).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the setting of the maximum allowable number by the user disclosed by Ratnasingam with the control to change the lane once disclosed by Hayakawa, in order to reduce travel time and driving complexity and improve safety.
Claim(s) 6-10 and 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hayakawa (US Patent Application Publication 2021/0237739) in view of Abad (US Patent Application Publication 2024/0017726).
Regarding claims 6-10 and 18-20, Hayakawa discloses the apparatus of claim 1 and the method of claim 13 as discussed above but does not disclose wherein the processor is configured to determine whether the current lane in which the autonomous vehicle is driving is an optimal lane by determining whether the current lane in which the autonomous vehicle is driving is in a smooth traffic flow, wherein the processor is further configured to determine, based on determining that an average driving speed of the current lane in which the autonomous vehicle is driving is lower than an average driving speed of a surrounding lane of the autonomous vehicle by at least a preset average driving speed difference between lanes, that the current lane in which the autonomous vehicle is driving is not in the smooth traffic flow, wherein the processor is further configured to determine, based on determining that the current lane in which the autonomous vehicle is driving is not the smooth traffic flow, whether a surrounding lane of the autonomous vehicle is in a smooth traffic flow, and create, based on determining that the surrounding lane of the autonomous vehicle is in a smooth traffic flow, the lane change strategy, wherein the processor is further configured to determine whether a vehicle in front of the autonomous vehicle is a vehicle that obstructs a traffic flow and create, based on determining that the vehicle in front of the autonomous vehicle is the vehicle that obstructs the traffic flow, the lane change strategy, wherein the processor is further configured to determine whether a vehicle in front of the autonomous vehicle is a vehicle that obstructs a traffic flow, determine, based on determining that the vehicle in front of the autonomous vehicle is the vehicle that obstructs the traffic flow, whether a surrounding lane of the autonomous vehicle is in a smooth traffic flow, and create, based on determining that the surrounding lane of the autonomous vehicle is in a smooth traffic flow, the lane change strategy.
Abad discloses lane adjustment techniques involving a processor configured to determine whether a current lane in which an autonomous vehicle is driving is an optimal lane by determining whether the current lane in which the autonomous vehicle is driving is in a smooth traffic flow [0146, 0157],
determine, based on determining that an average driving speed of the current lane in which the autonomous vehicle is driving is lower than an average driving speed of a surrounding lane of the autonomous vehicle by at least a preset average driving speed difference between lanes, that the current lane in which the autonomous vehicle is driving is not in the smooth traffic flow [0157-0158], and
determine, based on determining that the current lane in which the autonomous vehicle is driving is not the smooth traffic flow, whether a surrounding lane of the autonomous vehicle is in a smooth traffic flow, and create, based on determining that the surrounding lane of the autonomous vehicle is in a smooth traffic flow, a lane change strategy [0159];
determine whether a vehicle in front of the autonomous vehicle is a vehicle that obstructs a traffic flow and create, based on determining that the vehicle in front of the autonomous vehicle is the vehicle that obstructs the traffic flow, a lane change strategy [0142, 0157-0158];
determine whether a vehicle in front of the autonomous vehicle is a vehicle that obstructs a traffic flow, determine, based on determining that the vehicle in front of the autonomous vehicle is the vehicle that obstructs the traffic flow, whether a surrounding lane of the autonomous vehicle is in a smooth traffic flow, and create, based on determining that the surrounding lane of the autonomous vehicle is in a smooth traffic flow, the lane change strategy [0142, 0157-0158].
Abad teaches “During freeway driving, it is common for a vehicle to encounter other vehicles that are traveling slower. These vehicles, also referred to herein as slow agents, often include oversized vehicles, partially disabled vehicles, vehicles pulling heavy loads, vehicles pulling onto the shoulder, and vehicles controlled by distracted drivers, among others. A slow agent that is traveling in the same lane and at a position in front of an autonomous vehicle can pose problems for the vehicle. These problems can be amplified when the autonomous vehicle is a truck pulling a trailer or another large type of vehicle that has limited maneuverability when compared to a smaller passenger vehicle. As such, a slow agent can increase the risk that another vehicle will rear-end the autonomous vehicle due to its slow speed behind the slow agent. In addition, the slow agent impacts travel time and delivery schedule for the autonomous vehicle and may also cause the autonomous vehicle's speed to drop below an allowable speed minimum associated with the road” [0002].
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to determine whether the current lane is optimal based on the presence of smooth traffic flow compared to an adjacent lane and to create a lane change strategy if the traffic flow isn’t smooth as disclosed by Abad in the apparatus and method disclosed by Hayakawa, because as taught by Abad changing lanes under non-smooth traffic flow, especially when the autonomous vehicle is pulling a heavy load, can lower the risk of another vehicle rear-ending the autonomous vehicle and help to maintain a desired travel time.
Conclusion
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/JOSHUA CAMPBELL/
Examiner, Art Unit 3747
/LOGAN M KRAFT/Supervisory Patent Examiner, Art Unit 3747
