Patent Application 15182313 - Route Planning for an Autonomous Vehicle

Title: Route Planning for an Autonomous Vehicle
Application Information

Invention Title: Route Planning for an Autonomous Vehicle
Application Number: 15182313
Submission Date: 2025-05-20T00:00:00.000Z
Effective Filing Date: 2016-06-14T00:00:00.000Z
Filing Date: 2016-06-14T00:00:00.000Z
National Class: 701
National Sub-Class: 025000
Examiner Employee Number: 94217
Art Unit: 3665
Tech Center: 3600
Rejection Summary

102 Rejections: 0
103 Rejections: 1
Cited Patents

The following patents were cited in the rejection:
US 0178998🔗
Office Action Text



    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 .

Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection.  Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.  Applicant's submission filed on 02/19/2025 has been entered.
 
Response to Amendment
This action is in response to amendments and remarks filed on 02/19/2025. Claims 22 and 24-42 are considered in this office action. Claims 22, 31, 39, and 42 have been amended. Claims 1-21 and 23 have been cancelled. Claims 22 and 24-42 are pending examination.

Response to Arguments
Applicant presents the following arguments regarding the previous office action:
“A determination of sensor accuracy as in Attard is not a known failure mode.”

Applicant's argument A. has been fully considered but they are not persuasive.
Regarding Applicant’s argument A. that “A determination of sensor accuracy as in Attard is not a known failure mode,” Examiner respectfully disagrees. Pg. 5 lines 23-24 of the instant specification describes “specific failure modes” as “conditions or circumstances where the sensor will reliably degrade or fail to generate a data product of interest.” Attard teaches determining a data collector 110 evaluation of likely accuracy, e.g., sensor accuracy (failure modes, e.g., degradation of sensor), from collected data 115, which includes information about an external environment in which the vehicle 101 is traveling, e.g., road attributes (physical properties of the road segment) (Attard, Par. [0028] lines 1-11). Thus, the “sensor accuracy” taught by Attard is representative of conditions or circumstances (road attributes) where the sensor will reliably degrade or fail to produce a product of interest. In other words, Attard teaches predicting known failure modes (determining evaluations of likely sensor accuracy, e.g., degradation of sensor) based on features of the road segment (collected data 115, including road attributes) that would cause the failure modes (cause the sensor accuracy to be degraded). Therefore, Examiner maintains that the determined sensor accuracy as in Attard is representative of known failure modes.

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.

The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.

Claims 22 and 24-42 are rejected under 35 U.S.C. 103 as being unpatentable over Attard et al. (US 2015/0178998 A1) in view of Slusar (US 9,587,952 B1).
Regarding claim 22, Attard teaches “A computer generated method comprising:
	retrieving, by the one or more processors, one or more physical properties of a road segment of the first route (Par. [0016] lines 9-17 teaches data collectors 110 which include sensors for detecting conditions outside the vehicle 101 and/or for detecting road attributes, such as curves, pot-holes, dips, changes in grade, lane boundaries, etc.; Par. [0018] lines 12-15 teaches collected data 115 could include data 115 concerning detection of road attributes, weather conditions, etc.);
	predicting, by the one or more processors, one or more known failure modes of one or more sensors of the vehicle related to traversal of the road segment by the vehicle, wherein features of the road segment cause the one or more known failure modes;
	predicting, by the one or more processors, a level of performance of the one or more sensors based on the physical properties of the road segment and the one or more known failure modes;
	determining, by the one or more processors, whether the vehicle is capable of traveling the road segment based on the predicted level of performance (Par. [0023] lines 1-4 teaches storing one or more parameters 117 for comparison to confidence assessments 118 where a parameter 117 may define a set of confidence intervals; Par. [0025] lines 1-3 teaches various mathematical, statistical, and/or predictive modeling techniques could be used to generate and/or adjust parameters 117; Par. [0027] lines 1-10 teaches one or more vectors of autonomous attribute assessments 118 may be provided, where each value in the vector relates to an attribute of the vehicle 101 and/or a surrounding environment related to autonomous operation of the vehicle 101, e.g., attributes such as weather conditions, road conditions, etc.; Par. [0028] lines 1-11 teaches various ways of estimating confidences and/or assigning values to confidence intervals are used to generate the confidence assessments 118, including determining a data collector 110 evaluation of likely accuracy, e.g., sensor accuracy, from collected data 115, which includes information about an external environment in which the vehicle 101 is traveling, e.g., road attributes mentioned above; Par. [0028] lines 15-22 teaches by assessing such collected data 115, and weighting various determinations, e.g., a determination of a sensor data collector 110 accuracy (failure modes) and one or more determinations relating to external and/or environmental conditions, e.g., presence or absence of precipitation, road conditions, etc. (physical properties of the road segment) (i.e., conditions and circumstances where the sensor will reliably degrade), one or more confidence assessments 118 (predicted level of performance) may be generated regarding the ability of the vehicle 101 to operate autonomously (whether the vehicle is capable of traveling the road segment));” however Attard does not explicitly teach “selecting, using one or more processors of a vehicle, a first route” and “in response to determining that the vehicle is incapable of traveling the road segment, selecting, by the one or more processors, a second route excluding the road segment.”
	From the same field of endeavor, Slusar teaches “selecting, using one or more processors of a vehicle, a first route (Col. 13 lines 49-58 teaches the personal navigation device 110 initially provides the quickest/shortest route from a start location A to an end location B (selects a first route), and then determines the route traversal values using traffic and weather conditions)” and “in response to determining that the vehicle is incapable of traveling the road segment, selecting, by the one or more processors, a second route excluding the road segment (Col. 13 lines 60-62 teaches the driver may be presented with an alternate route which is less risky than the initial route calculated; Col. 22 lines 47-61 teaches the processor receiving hazard information which it analyzes in order to determine the size and type of hazard and assign it a route traversal value, which is then used to recalculate the route traversal value for the driving route; after making the determination (determining if vehicle is incapable of traveling the road segment), the processor communicates to the vehicle navigation device to stay on the current driving route or to alter the driving route in order to reduce the route traversal value).”
	It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to modify the teachings of Attard to incorporate the teachings of Slusar with a reasonable expectation of success to have the method taught by Attard include choosing a first route and upon determining the vehicle cannot travel a segment of the first route, selecting a second route excluding the segment as taught by Slusar.
	The motivation for doing so would be to optimize travel along a given route (Slusar, Col. 23 lines 53-54).
Regarding claim 24, the combination of Attard and Slusar teaches all the limitations of claim 22 above, and further teaches “wherein determining whether the vehicle is capable of traveling the road segment is further based on software processes that process data representing the properties of the one or more sensors (Attard, Par. [0031] teaches confidence estimates may be modified based on knowledge obtained about future events, such as weather along a planned route, where information about a likelihood of weather that might adversely affect the perceptual layer (PL) (e.g., heavy rain or snow) can be factored into the confidence assessments 118 in the vector ΦPL in advance of actual degradation of sensor data collector 110 signals, and the confidence assessments 118 may be adjusted to reflect not only the immediate sensor state but also a likelihood that the sensor state may degrade in the future; Par. [0034] lines 1-3 teaches confidence attribute sub-assessments 118, e.g., one or more values in a vector ΦPL or ΦAL, may relate to particular collected data 115 (properties of the one or more sensors)).”
Regarding claim 25, the combination of Attard and Slusar teaches all the limitations of claim 22 above, and further teaches “in which the predicted level of performance of the one or more sensors comprises an actual or estimated level of performance as a function of current or predicted future conditions (Attard, Par. [0028] lines 1-11 teaches various ways of estimating confidences and/or assigning values to confidence intervals are used to generate the confidence assessments 118, including determining a data collector 110 evaluation of likely accuracy, e.g., sensor accuracy, from collected data 115, which includes information about an external environment in which the vehicle 101 is traveling, e.g., road attributes mentioned above).”
Regarding claim 26, the combination of Attard and Slusar teaches all the limitations of claim 22 above, and further teaches “wherein the predicted level of performance comprises a capability of the one or more sensors to yield a data product at a specific level of performance (Attard, Par. [0028] lines 1-11 teaches various ways of estimating confidences and/or assigning values to confidence intervals are used to generate the confidence assessments 118, including determining a data collector 110 evaluation of likely accuracy, e.g., sensor accuracy, from collected data 115, which includes information about an external environment in which the vehicle 101 is traveling, e.g., road attributes mentioned above; Par. [0031] teaches confidence estimates may be modified based on knowledge obtained about future events, such as weather along a planned route, where information about a likelihood of weather that might adversely affect the perceptual layer (PL) (e.g., heavy rain or snow) can be factored into the confidence assessments 118 in the vector ΦPL in advance of actual degradation of sensor data collector 110 signals, and the confidence assessments 118 may be adjusted to reflect not only the immediate sensor state but also a likelihood that the sensor state may degrade in the future).”
Regarding claim 27, the combination of Attard and Slusar teaches all the limitations of claim 22 above, and further teaches “wherein determining whether the vehicle is capable of traveling the road segment is further based on characteristics of software processes used by the vehicle (Attard, Par. [0028] lines 1-11 teaches various ways of estimating confidences and/or assigning values to confidence intervals are used to generate the confidence assessments 118, including determining a data collector 110 evaluation of likely accuracy, e.g., sensor accuracy, from collected data 115 (characteristics of software processes including ability to yield data product of interest), which includes information about an external environment in which the vehicle 101 is traveling, e.g., road attributes mentioned above; Par. [0031] teaches confidence estimates may be modified based on knowledge obtained about future events, such as weather along a planned route, where information about a likelihood of weather that might adversely affect the perceptual layer (PL) (e.g., heavy rain or snow) (software characteristics including ability to yield data product of interest) can be factored into the confidence assessments 118 in the vector ΦPL in advance of actual degradation of sensor data collector 110 signals, and the confidence assessments 118 may be adjusted to reflect not only the immediate sensor state but also a likelihood that the sensor state may degrade in the future).”
Regarding claim 28, the combination of Attard and Slusar teaches all the limitations of claim 27 above, and further teaches “in which the characteristics of the software processes comprise the ability of the software processes to yield a data product at a specific level of performance (Attard, Par. [0028] lines 1-11 teaches various ways of estimating confidences and/or assigning values to confidence intervals are used to generate the confidence assessments 118, including determining a data collector 110 evaluation of likely accuracy, e.g., sensor accuracy, from collected data 115 (characteristics of software processes including ability to yield data product of interest), which includes information about an external environment in which the vehicle 101 is traveling, e.g., road attributes mentioned above; Par. [0031] teaches confidence estimates may be modified based on knowledge obtained about future events, such as weather along a planned route, where information about a likelihood of weather that might adversely affect the perceptual layer (PL) (e.g., heavy rain or snow) (software characteristics including ability to yield data product of interest) can be factored into the confidence assessments 118 in the vector ΦPL in advance of actual degradation of sensor data collector 110 signals, and the confidence assessments 118 may be adjusted to reflect not only the immediate sensor state but also a likelihood that the sensor state may degrade in the future).”
Regarding claim 29, the combination of Attard and Slusar teaches all the limitations of claim 27 above, and further teaches “in which the characteristics of the software processes comprise an ability of a data fusion network to yield a data product at a specific level of performance (Attard, Par. [0029] lines 1-5 teaches a vector of confidence estimates 118 include a vector ΦPL=(Φ1PL,Φ2PL,…,ΦnPL), relating to the vehicle 101 perceptual layer (PL), where n is a number of perceptual sub-systems, e.g., groups of one or more sensor data collectors 110, in the PL).”
Regarding claim 30, the combination of Attard and Slusar teaches all the limitations of claim 27 above, and further teaches “in which the software processes comprise one of motion planning processes, decision making processes, and motion control processes (Attard, Fig. 2 step 205 Autonomous driving (motion planning process), step 215 Compute confidence estimates and step 225 Provide alert? (decision making process and motion control process); Par. [0055] lines 1-5 teaches in block 205 the vehicle 101 commences autonomous driving operations and is operated in a manner partially or completely controlled by the autonomous driving module (motion planning process); Par. [0061] lines 1-3 teaches in block 225 the computer 105 determines whether the overall confidence assessment 118 meets or exceeds a predetermined threshold, and Par. [0063] lines 1-8 teaches in block 225 the computer determines whether to provide a message 116 (decision making process) relating to a recommendation that autonomous operations of the vehicle 101 be ended or is to be ended after some period of time has elapsed (motion control process)) (Slusar, Fig. 8 step 802 Receive a First Travel Route and a Second Travel Route for an Autonomous Vehicle (motion planning process), step 804 Determine a First Route Risk Value for the First Travel Route and a Second Route Risk Value for the Second Travel Route and step 806 Compare the First and Second Route Risk Values to Determine Which of Travel Routes Provides Less Risk (decision making process), step 808 Select the Travel Route that Provides Less Risk (motion control process); Col. 13 lines 49-62 teaches a personal navigation device 110 initially provides the quickest/shortest route from a start location A to an end location B (motion planning process), then determines the route traversal value based on the risk types on the route, with traffic and weather conditions being included in the determination of the route traversal value for comparison of routes (decision making process), and provides the driver with an alternate route which is less risky than the initial route calculated (motion control process)).”
Regarding claim 31, Attard teaches “A non-transitory computer-readable storage medium storing computer instructions (Par. [0084] lines 1-3 teaches a computer-readable storage medium which includes any medium that participates in providing data (e.g., instructions), which may be read by a computer) which when executed by one or more processors, cause the one or more processors to:
	retrieve one or more physical properties of a road segment of the first route (Par. [0016] lines 9-17 teaches data collectors 110 which include sensors for detecting conditions outside the vehicle 101 and/or for detecting road attributes, such as curves, pot-holes, dips, changes in grade, lane boundaries, etc.; Par. [0018] lines 12-15 teaches collected data 115 could include data 115 concerning detection of road attributes, weather conditions, etc.);
	predict one or more known failure modes of one or more sensors of the vehicle related to traversal of the road segment by the vehicle, wherein features of the road segment cause the one or more known failure modes;
	predict a level of performance of the one or more sensors based on the physical properties of the road segment and the one or more known failure modes;
	determine whether the vehicle is capable of traveling the road segment based on the predicted level of performance (Par. [0023] lines 1-4 teaches storing one or more parameters 117 for comparison to confidence assessments 118 where a parameter 117 may define a set of confidence intervals; Par. [0025] lines 1-3 teaches various mathematical, statistical, and/or predictive modeling techniques could be used to generate and/or adjust parameters 117; Par. [0027] lines 1-10 teaches one or more vectors of autonomous attribute assessments 118 may be provided, where each value in the vector relates to an attribute of the vehicle 101 and/or a surrounding environment related to autonomous operation of the vehicle 101, e.g., attributes such as weather conditions, road conditions, etc.; Par. [0028] lines 1-11 teaches various ways of estimating confidences and/or assigning values to confidence intervals are used to generate the confidence assessments 118, including determining a data collector 110 evaluation of likely accuracy, e.g., sensor accuracy, from collected data 115, which includes information about an external environment in which the vehicle 101 is traveling, e.g., road attributes mentioned above; Par. [0028] lines 15-22 teaches by assessing such collected data 115, and weighting various determinations, e.g., a determination of a sensor data collector 110 accuracy (failure modes) and one or more determinations relating to external and/or environmental conditions, e.g., presence or absence of precipitation, road conditions, etc. (physical properties of the road segment) (i.e., conditions and circumstances where the sensor will reliably degrade), one or more confidence assessments 118 (predicted level of performance) may be generated regarding the ability of the vehicle 101 to operate autonomously (whether the vehicle is capable of traveling the road segment));” however Attard does not explicitly teach causing the processors to “select a first route for a vehicle” and “in response to determining that the vehicle is incapable of traveling the road segment, select a second route excluding the road segment.”
	From the same field of endeavor, Slusar teaches causing the processors to “select a first route for a vehicle (Col. 13 lines 49-58 teaches the personal navigation device 110 initially provides the quickest/shortest route from a start location A to an end location B (selects a first route), and then determines the route traversal values using traffic and weather conditions)” and “in response to determining that the vehicle is incapable of traveling the road segment, select a second route excluding the road segment (Col. 13 lines 60-62 teaches the driver may be presented with an alternate route which is less risky than the initial route calculated; Col. 22 lines 47-61 teaches the processor receiving hazard information which it analyzes in order to determine the size and type of hazard and assign it a route traversal value, which is then used to recalculate the route traversal value for the driving route; after making the determination (determining if vehicle is incapable of traveling the road segment), the processor communicates to the vehicle navigation device to stay on the current driving route or to alter the driving route in order to reduce the route traversal value).”
	It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to modify the teachings of Attard to incorporate the teachings of Slusar with a reasonable expectation of success to have the instructions taught by Attard include choosing a first route and upon determining the vehicle cannot travel a segment of the first route, selecting a second route excluding the segment as taught by Slusar.
	The motivation for doing so would be to optimize travel along a given route (Slusar, Col. 23 lines 53-54).
Regarding claim 32, the combination of Attard and Slusar teaches all the limitations of claim 31 above, and further teaches “wherein determining whether the vehicle is capable of traveling the road segment is further based on software processes that process data representing the properties of the one or more sensors (Attard, Par. [0031] teaches confidence estimates may be modified based on knowledge obtained about future events, such as weather along a planned route, where information about a likelihood of weather that might adversely affect the perceptual layer (PL) (e.g., heavy rain or snow) can be factored into the confidence assessments 118 in the vector ΦPL in advance of actual degradation of sensor data collector 110 signals, and the confidence assessments 118 may be adjusted to reflect not only the immediate sensor state but also a likelihood that the sensor state may degrade in the future; Par. [0034] lines 1-3 teaches confidence attribute sub-assessments 118, e.g., one or more values in a vector ΦPL or ΦAL, may relate to particular collected data 115 (properties of the one or more sensors)).”
Regarding claim 33, the combination of Attard and Slusar teaches all the limitations of claim 31 above, and further teaches “in which the predicted level of performance of the one or more sensors comprises an actual or estimated level of performance as a function of current or predicted future conditions (Attard, Par. [0028] lines 1-11 teaches various ways of estimating confidences and/or assigning values to confidence intervals are used to generate the confidence assessments 118, including determining a data collector 110 evaluation of likely accuracy, e.g., sensor accuracy, from collected data 115, which includes information about an external environment in which the vehicle 101 is traveling, e.g., road attributes mentioned above).”
Regarding claim 34, the combination of Attard and Slusar teaches all the limitations of claim 31 above, and further teaches “wherein the predicted level of performance comprises a capability of the one or more sensors to yield a data product at a specific level of performance (Attard, Par. [0028] lines 1-11 teaches various ways of estimating confidences and/or assigning values to confidence intervals are used to generate the confidence assessments 118, including determining a data collector 110 evaluation of likely accuracy, e.g., sensor accuracy, from collected data 115, which includes information about an external environment in which the vehicle 101 is traveling, e.g., road attributes mentioned above; Par. [0031] teaches confidence estimates may be modified based on knowledge obtained about future events, such as weather along a planned route, where information about a likelihood of weather that might adversely affect the perceptual layer (PL) (e.g., heavy rain or snow) can be factored into the confidence assessments 118 in the vector ΦPL in advance of actual degradation of sensor data collector 110 signals, and the confidence assessments 118 may be adjusted to reflect not only the immediate sensor state but also a likelihood that the sensor state may degrade in the future).”
Regarding claim 35, the combination of Attard and Slusar teaches all the limitations of claim 31 above, and further teaches “wherein determining whether the vehicle is capable of traveling the road segment is further based on characteristics of software processes used by the vehicle (Attard, Par. [0028] lines 1-11 teaches various ways of estimating confidences and/or assigning values to confidence intervals are used to generate the confidence assessments 118, including determining a data collector 110 evaluation of likely accuracy, e.g., sensor accuracy, from collected data 115 (characteristics of software processes including ability to yield data product of interest), which includes information about an external environment in which the vehicle 101 is traveling, e.g., road attributes mentioned above; Par. [0031] teaches confidence estimates may be modified based on knowledge obtained about future events, such as weather along a planned route, where information about a likelihood of weather that might adversely affect the perceptual layer (PL) (e.g., heavy rain or snow) (software characteristics including ability to yield data product of interest) can be factored into the confidence assessments 118 in the vector ΦPL in advance of actual degradation of sensor data collector 110 signals, and the confidence assessments 118 may be adjusted to reflect not only the immediate sensor state but also a likelihood that the sensor state may degrade in the future).”
Regarding claim 36, the combination of Attard and Slusar teaches all the limitations of claim 35 above, and further teaches “in which the characteristics of the software processes comprise the ability of the software processes to yield a data product at a specific level of performance (Attard, Par. [0028] lines 1-11 teaches various ways of estimating confidences and/or assigning values to confidence intervals are used to generate the confidence assessments 118, including determining a data collector 110 evaluation of likely accuracy, e.g., sensor accuracy, from collected data 115 (characteristics of software processes including ability to yield data product of interest), which includes information about an external environment in which the vehicle 101 is traveling, e.g., road attributes mentioned above; Par. [0031] teaches confidence estimates may be modified based on knowledge obtained about future events, such as weather along a planned route, where information about a likelihood of weather that might adversely affect the perceptual layer (PL) (e.g., heavy rain or snow) (software characteristics including ability to yield data product of interest) can be factored into the confidence assessments 118 in the vector ΦPL in advance of actual degradation of sensor data collector 110 signals, and the confidence assessments 118 may be adjusted to reflect not only the immediate sensor state but also a likelihood that the sensor state may degrade in the future).”
Regarding claim 37, the combination of Attard and Slusar teaches all the limitations of claim 35 above, and further teaches “in which the characteristics of the software processes comprise an ability of a data fusion network to yield a data product at a specific level of performance (Attard, Par. [0029] lines 1-5 teaches a vector of confidence estimates 118 include a vector ΦPL=(Φ1PL,Φ2PL,…,ΦnPL), relating to the vehicle 101 perceptual layer (PL), where n is a number of perceptual sub-systems, e.g., groups of one or more sensor data collectors 110, in the PL).”
Regarding claim 38, the combination of Attard and Slusar teaches all the limitations of claim 35 above, and further teaches “in which the software processes comprise one of motion planning processes, decision making processes, and motion control processes (Attard, Fig. 2 step 205 Autonomous driving (motion planning process), step 215 Compute confidence estimates and step 225 Provide alert? (decision making process and motion control process); Par. [0055] lines 1-5 teaches in block 205 the vehicle 101 commences autonomous driving operations and is operated in a manner partially or completely controlled by the autonomous driving module (motion planning process); Par. [0061] lines 1-3 teaches in block 225 the computer 105 determines whether the overall confidence assessment 118 meets or exceeds a predetermined threshold, and Par. [0063] lines 1-8 teaches in block 225 the computer determines whether to provide a message 116 (decision making process) relating to a recommendation that autonomous operations of the vehicle 101 be ended or is to be ended after some period of time has elapsed (motion control process)) (Slusar, Fig. 8 step 802 Receive a First Travel Route and a Second Travel Route for an Autonomous Vehicle (motion planning process), step 804 Determine a First Route Risk Value for the First Travel Route and a Second Route Risk Value for the Second Travel Route and step 806 Compare the First and Second Route Risk Values to Determine Which of Travel Routes Provides Less Risk (decision making process), step 808 Select the Travel Route that Provides Less Risk (motion control process); Col. 13 lines 49-62 teaches a personal navigation device 110 initially provides the quickest/shortest route from a start location A to an end location B (motion planning process), then determines the route traversal value based on the risk types on the route, with traffic and weather conditions being included in the determination of the route traversal value for comparison of routes (decision making process), and provides the driver with an alternate route which is less risky than the initial route calculated (motion control process)).”
Regarding claim 39, Attard teaches “A vehicle comprising:
	one or more processors (Fig. 1 computing device 105);
	a non-transitory computer-readable storage medium storing computer instructions (Par. [0084] lines 1-3 teaches a computer-readable storage medium which includes any medium that participates in providing data (e.g., instructions), which may be read by a computer) which when executed by the one or more processors, cause the one or more processors to:
	retrieve one or more physical properties of a road segment of the first route (Par. [0016] lines 9-17 teaches data collectors 110 which include sensors for detecting conditions outside the vehicle 101 and/or for detecting road attributes, such as curves, pot-holes, dips, changes in grade, lane boundaries, etc.; Par. [0018] lines 12-15 teaches collected data 115 could include data 115 concerning detection of road attributes, weather conditions, etc.);
	predict one or more known failure modes of one or more sensors of the vehicle related to traversal of the road segment by the vehicle, wherein features of the road segment cause the one or more known failure modes and the one or more known failure modes indicate that one or more sensors fail to generate data products;
	predict a level of performance of the one or more sensors based on the physical properties of the road segment and the one or more known failure modes that indicate the one or more sensors fail to generate the data products;
	determine whether the vehicle is capable of traveling the road segment based on the predicted level of performance (Par. [0023] lines 1-4 teaches storing one or more parameters 117 for comparison to confidence assessments 118 where a parameter 117 may define a set of confidence intervals; Par. [0025] lines 1-3 teaches various mathematical, statistical, and/or predictive modeling techniques could be used to generate and/or adjust parameters 117; Par. [0027] lines 1-10 teaches one or more vectors of autonomous attribute assessments 118 may be provided, where each value in the vector relates to an attribute of the vehicle 101 and/or a surrounding environment related to autonomous operation of the vehicle 101, e.g., attributes such as weather conditions, road conditions, etc.; Par. [0028] lines 1-11 teaches various ways of estimating confidences and/or assigning values to confidence intervals are used to generate the confidence assessments 118, including determining a data collector 110 evaluation of likely accuracy, e.g., sensor accuracy (predicting ability of sensor to generate data products of interest), from collected data 115, which includes information about an external environment in which the vehicle 101 is traveling, e.g., road attributes mentioned above; Par. [0028] lines 15-22 teaches by assessing such collected data 115, and weighting various determinations, e.g., a determination of a sensor data collector 110 accuracy (failure modes that indicate if sensor fails to generate data products of interest) and one or more determinations relating to external and/or environmental conditions, e.g., presence or absence of precipitation, road conditions, etc. (physical properties of the road segment) (i.e., conditions and circumstances where the sensor will reliably degrade), one or more confidence assessments 118 (predicted level of performance) may be generated regarding the ability of the vehicle 101 to operate autonomously (whether the vehicle is capable of traveling the road segment));” however Attard does not explicitly teach causing the processors to “select a first route for a vehicle” and “in response to determining that the vehicle is incapable of traveling the road segment, select a second route excluding the road segment.”
	From the same field of endeavor, Slusar teaches causing the processors to “select a first route for a vehicle (Col. 13 lines 49-58 teaches the personal navigation device 110 initially provides the quickest/shortest route from a start location A to an end location B (selects a first route), and then determines the route traversal values using traffic and weather conditions)” and “in response to determining that the vehicle is incapable of traveling the road segment, select a second route excluding the road segment (Col. 13 lines 60-62 teaches the driver may be presented with an alternate route which is less risky than the initial route calculated; Col. 22 lines 47-61 teaches the processor receiving hazard information which it analyzes in order to determine the size and type of hazard and assign it a route traversal value, which is then used to recalculate the route traversal value for the driving route; after making the determination (determining if vehicle is incapable of traveling the road segment), the processor communicates to the vehicle navigation device to stay on the current driving route or to alter the driving route in order to reduce the route traversal value).”
	It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to modify the teachings of Attard to incorporate the teachings of Slusar with a reasonable expectation of success to have the instructions taught by Attard include choosing a first route and upon determining the vehicle cannot travel a segment of the first route, selecting a second route excluding the segment as taught by Slusar.
	The motivation for doing so would be to optimize travel along a given route (Slusar, Col. 23 lines 53-54).
Regarding claim 40, the combination of Attard and Slusar teaches all the limitations of claim 39 above, and further teaches “wherein determining whether the vehicle is capable of traveling the road segment is further based on software processes that process data representing the properties of the one or more sensors (Attard, Par. [0031] teaches confidence estimates may be modified based on knowledge obtained about future events, such as weather along a planned route, where information about a likelihood of weather that might adversely affect the perceptual layer (PL) (e.g., heavy rain or snow) can be factored into the confidence assessments 118 in the vector ΦPL in advance of actual degradation of sensor data collector 110 signals, and the confidence assessments 118 may be adjusted to reflect not only the immediate sensor state but also a likelihood that the sensor state may degrade in the future; Par. [0034] lines 1-3 teaches confidence attribute sub-assessments 118, e.g., one or more values in a vector ΦPL or ΦAL, may relate to particular collected data 115 (properties of the one or more sensors)).”
Regarding claim 41, the combination of Attard and Slusar teaches all the limitations of claim 39 above, and further teaches “in which the predicted level of performance of the one or more sensors comprises an actual or estimated level of performance as a function of current or predicted future conditions (Attard, Par. [0028] lines 1-11 teaches various ways of estimating confidences and/or assigning values to confidence intervals are used to generate the confidence assessments 118, including determining a data collector 110 evaluation of likely accuracy, e.g., sensor accuracy, from collected data 115, which includes information about an external environment in which the vehicle 101 is traveling, e.g., road attributes mentioned above).”
Regarding claim 42, the combination of Attard and Slusar teaches all the limitations of claim 39 above, and further teaches “wherein the predicted level of performance comprises a capability of the one or more sensors to yield the data products at a specific level of performance (Attard, Par. [0028] lines 1-11 teaches various ways of estimating confidences and/or assigning values to confidence intervals are used to generate the confidence assessments 118, including determining a data collector 110 evaluation of likely accuracy, e.g., sensor accuracy, from collected data 115, which includes information about an external environment in which the vehicle 101 is traveling, e.g., road attributes mentioned above; Par. [0031] teaches confidence estimates may be modified based on knowledge obtained about future events, such as weather along a planned route, where information about a likelihood of weather that might adversely affect the perceptual layer (PL) (e.g., heavy rain or snow) can be factored into the confidence assessments 118 in the vector ΦPL in advance of actual degradation of sensor data collector 110 signals, and the confidence assessments 118 may be adjusted to reflect not only the immediate sensor state but also a likelihood that the sensor state may degrade in the future).”

Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KATHERINE M FITZHARRIS whose telephone number is (469)295-9147.  The examiner can normally be reached on 7:30 am - 6:00 pm M-Th.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, CHRISTIAN CHACE can be reached on (571)272-4190.  The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system.  Status information for published applications may be obtained from either Private PAIR or Public PAIR.  Status information for unpublished applications is available through Private PAIR only.  For more information about the PAIR system, see https://ppair-my.uspto.gov/pair/PrivatePair. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.

/K.M.F./Examiner, Art Unit 3665 


/CHRISTIAN CHACE/Supervisory Patent Examiner, Art Unit 3665