Method and system for analyzing the control of a vehicle

Abstract

A method and a system analyze the control of a vehicle having an autonomous driving unit. A change in the driving mode from autonomous driving to manual driving is detected, and at least one driving parameter before and/or after detecting the change is monitored. Based on driving values obtained by the monitoring with respect to the detected change in driving mode, at least one driving quantity quantifying the quality of interplay between the autonomous driving unit and a human driver is determined.

Claims

1. A method for improving a processor assessing a control of a vehicle having an autonomous driving unit, which comprises the steps of: generating driving value data by: detecting a change in a driving mode from autonomous driving to manual driving; monitoring at least one driving parameter before and after detecting the change in the driving mode; determining at least one driving quantity quantifying a quality of interplay between the autonomous driving unit and a human driver based on driving values obtained by the monitoring with respect to the change detected in the driving mode and a length of a time interval between the autonomous driving unit outputting a control takeover request and the human driver taking over control, wherein the length of the time interval is weighted by a vehicle dynamic variable obtained during the time interval and the at least one driving quantity being based on a product of the time interval and the vehicle dynamic variable during the time interval, wherein the product reaches or exceeds a threshold, and the at least one driving quantity being further determined based on a quantity distribution, the quantity distribution being further determined based on driving value data obtained from at least one vehicle during a previous driving period of the at least one vehicle; rating the control of the vehicle based on the at least one driving quantity; training the autonomous driving unit by providing the driving value data corresponding to an above average rating to a neural network; and performing at least two of: using the rating for indicating usage-based product liability for vehicle insurance of autonomous vehicles; determining responsibility of either the autonomous driving unit or the human driver in a case of an accident; determining a capability of the autonomous driving unit to assess traffic scenarios and recognize potentially overtaxing driving situations to determine an insurance premium; and calculating an insurance premium for the human driver of the vehicle based on the rating.

2. The method according to claim 1, which further comprises storing the driving values obtained by the monitoring for a predetermined storage time interval spanning at least from a point in time before a control takeover request is outputted by the autonomous driving unit to a point in time the autonomous driving unit outputs the control takeover request.

3. The method according to claim 1, wherein the at least one driving quantity is a composite quantity.

4. The method according to claim 1, which further comprises determining the at least one driving quantity based on an autonomous driving quantity quantifying a quality of a driving of the autonomous driving unit before the change in the driving mode and on a manual driving quantity quantifying a quality of a driving of the human driver after the change in the driving mode.

5. The method according to claim 4, wherein the at least one driving quantity is further based on mileage accumulated in a respective driving mode.

6. The method according to claim 1, which further comprises determining the at least one driving quantity based on an autonomous driving quantity quantifying a quality of a driving of the autonomous driving unit after a control takeover request is outputted by the autonomous driving unit and on a manual driving quantity quantifying a quality of a driving of the human driver subsequently to the human driver taking over control.

7. The method according to claim 6, wherein the at least one driving quantity is further based on accident rate values for autonomous driving or manual driving after a takeover control request is outputted by the autonomous driving unit and subsequently to the human driver taking over control, respectively.

8. The method according to claim 1, which further comprises determining the at least one driving quantity based on the driving values obtained by the monitoring during a predetermined first time interval prior to the autonomous driving unit outputting a control takeover request.

9. The method according to claim 1, wherein the at least one driving quantity is interrelated to a quantity distribution of the driving quantity.

10. The method according to claim 1, which further comprises determining the quantity distribution based on the driving value data provided by a plurality of vehicles.

11. The method according to claim 10, which further comprises generating the driving value data by driving value statistic processors provided in each of the plurality of vehicles, the driving value statistic processors monitoring at least one driving parameter of each of the plurality of vehicles and transmitting corresponding driving values to a server.

12. The method according to claim 1, wherein the monitoring of at least one driving parameter includes monitoring at least the following: vehicle speed; vehicle acceleration; and vehicle deceleration.

13. A system for analyzing a control of a vehicle having an autonomous driving unit, the system comprising: a detector configured to detect a change in a driving mode from autonomous driving to manual driving; a sensor configured to monitor at least one driving parameter before and after the change in the driving mode was detected; and a processor configured to: determine at least one driving quantity quantifying a quality of interplay between the autonomous driving unit and a human driver based on driving values obtained by said sensor with respect to the change in the driving mode and a length of a time interval between the autonomous driving unit outputting a control takeover request and the human driver taking over control, wherein the length of the time interval is weighted by a vehicle dynamic variable obtained during the time interval and the at least one driving quantity further based on a product of the time interval and the vehicle dynamic variable during the time interval, wherein the product reaches or exceeds a threshold; the at least one driving quantity being determined further based on a quantity distribution, the quantity distribution being determined based on driving value data obtained from at least one vehicle during a previous driving period of the at least one vehicle; rate the control of the vehicle based on the at least one driving quantity; and calculate an insurance premium for the human driver of the vehicle based on the rating and determine a responsibility of either the autonomous driving unit or the human driver in a case of an accident; wherein the autonomous driving unit is trained by providing the driving values corresponding to an above average rating to a neural network.

Description

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING

(1) FIG. 1 is an illustration showing an example of a process of transfer of control of a vehicle;

(2) FIG. 2 is a block diagram showing an example of a method analyzing the control of the vehicle containing an autonomous driving unit;

(3) FIG. 3 is a graph showing an example of determining a driving quantity based on driving values obtained during a driving period;

(4) FIG. 4 is an illustration showing an example of determining a driving quantity based on a time interval between an autonomous driving unit outputting a control takeover request and a human driver taking over control; and

(5) FIG. 5 is an illustration showing an example of a system for analyzing the control of the vehicle containing the autonomous driving unit.

DETAILED DESCRIPTION OF THE INVENTION

(6) Referring now to the figures of the drawings in detail and first, particularly to FIG. 1 thereof, there is shown an example of a process of transfer of control of a vehicle 1 from an autonomous driving unit of the vehicle 1 to a human driver, wherein the thick black arrow indicates time t. The transfer of control is grouped into four stages A, B, C, D each representing a different phase of the transfer of control. The different stages A-D are separated by dotted lines.

(7) In the first stage A, the vehicle 1 is under autonomous control. When the autonomous driving unit realizes that an upcoming traffic scenarios too complex to safely control the vehicle in the scenario, the autonomous driving unit outputs a control takeover request R, for example in form of an optical signal, an acoustic signal, a haptic signal and/or the like. From the point in time at which the request is outputted (also referred to as the control takeover request event R) up to the point in time at which the human driver actually takes over control (also referred to as the control takeover event T), the autonomous driving unit still controls the vehicle 1. This period corresponds to the second stage B. During this stage, the control of the vehicle 1 by the autonomous driving unit is key for the situation in which the human driver finds himself in when taking over control at the control takeover event T.

(8) After the human driver has taken over control, the third stage C begins. During this stage, the human driver controlling the vehicle 1 adjusts to the current traffic scenario, for example by executing a particular driving maneuver as braking (decelerating) or evading (steering). The reactions of the human driver during this stage may be highly dependent on the driving behavior of the autonomous driving unit in the previous stages, in particular in the second stage B. Usually, adjusting to the current traffic scenario, i.e. thoroughly grasping the current situation and performing the necessary maneuvers, takes up to 30 seconds, in particular up to 60 seconds. For this reason, the third stage C preferably extends from the control takeover event T for a predetermined second time interval t2.

(9) In the fourth stage D, starting at the end of the predetermined second time interval t2, the human driver still controls the vehicle 1. Because a significant amount of time since taking over control has passed, no impact of the driving behavior of the autonomous driving unit, in particular driving decisions made by the autonomous driving unit, on the driving of the human driver is to be expected.

(10) In order to evaluate the driving quality of the driving of the autonomous driving unit, in particular in the first and second stage A, B, and/or the driving quality of the human driver, in particular in the third and fourth stage C, D, driving quantities Q1, Q2, Q3, Q4 may be derived from a plurality of driving values obtained by monitoring different driving parameters. For example, vehicle speed, vehicle acceleration, vehicle deceleration and erratic driving may be monitored and taken as the basis to determine the respective driving quantity Q1-Q4, wherein erratic driving is defined as the amount of acceleration and deceleration during a defined time interval. In FIG. 1, the driving quantities Q1-Q4 are indicated by horizontal bars extending through the different stages A-D.

(11) Preferably, to evaluate the quality of interplay between the autonomous driving unit and the human driver, e.g. to rate how well the autonomous driving unit controls the vehicle prior to handing over control to the human driver and/or how well the human driver copes with the traffic scenario the autonomous driving unit maneuvered him into, each of the driving quantities Q1-Q4 may be combined across at least two of the different stages A-D into a composite driving quantity. Alternatively or additionally, a driving quantity for a particular stage A-D may be at least partially based on driving values obtained in a different stage. For example, determining an autonomous driving quantity Q1-Q4 quantifying the quality of driving of the autonomous driving unit during the second stage B may be determined by considering not only driving values obtained by monitoring a driving parameter during the second stage B, but also driving values obtained by monitoring the same driving parameter during at least part of the preceding first stage A, this part being indicated by reference numeral A′. In particular, the autonomous driving quantity Q1-Q4 may be determined based on driving values obtained during a predetermined first time interval t1 extending from the control takeover request event R back into the first stage A, i.e. covering driving values obtained prior to outputting the control takeover request R.

(12) FIG. 2 shows an example of a method 2 for analyzing the control of a vehicle comprising an autonomous driving unit. In a step S1, a change in driving mode from autonomous driving to manual driving is detected. For example, a point in time may be determined at which the autonomous driving unit outputs a control takeover request signal, and a point in time may be determined at which a human driver takes over control.

(13) In a further step S2, at least one driving parameter as speed, acceleration, deceleration and/or the like is monitored. This monitoring may occur before and/or after the change in driving mode detected in step S1, i.e. during autonomous driving and/or manual driving.

(14) The driving values obtained by the monitoring are preferably grouped into different stages of the change in control (see FIG. 1). Additionally or alternatively, the driving values are stored at least for a predetermined storage time interval.

(15) Based on the obtained driving values, at least one driving quantity quantifying the quality of interplay between the autonomous driving unit and the human driver is determined in a further step S3. Therein, the driving quantity is determined with respect to the detected change in driving mode, e.g. by separately evaluating driving values obtained during at least two of the different stages of the change in control and comparing the evaluation results, or by differently weighting driving values from different stages.

(16) FIG. 3 shows an example of determining a driving quantity based on driving values obtained during a driving period. In particular, FIG. 3A schematically indicates how an autonomous driving quantity quantifying the quality of driving of the autonomous driving unit is determined, while FIG. 3B schematically indicates how a manual driving quantity quantifying the quality of driving of a human driver is determined. To eventually rate the overall combined driving of the entity comprising both the autonomous driving unit and the human driver, the autonomous and the manual driving quantity may be combined into a composite driving quantity.

(17) Both the autonomous and manual driving quantities are based on driving values which are obtained by monitoring a driving parameter, for example speed, during autonomous or manual operation of the vehicle, respectively. These driving values are preferably collected during a driving period, wherein a driving period may correspond to a single journey or a plurality of journeys of the vehicle, in particular during a predetermined time span as ten days, a month or quarter of the year. The driving values may be obtained with respect to the current location of the vehicle and compared to a driving value associated with the location, e.g. a statutory speed limit, an average speed or the like. The result of the comparison, e.g. a difference, may give a driving quality value.

(18) Thus, each monitoring of the driving parameter during a part of the driving period, e.g. during separate journey “events”, in particular with regard to different stages of the change in driving mode of the vehicle between autonomous control of the autonomous driving unit and the human driver, results in a particular quality value distribution Vi of driving quality values, the index i=1 . . . n indicating the ith part of the driving period or the ith event or stage, respectively.

(19) From each of these quality value distributions Vi, a normal distribution N may be obtained, in particular by averaging each of the quality value distributions Vi. This reflects the central limit theorem according to which a large number of independent random variables (the driving values) asymptotically forms a stable distribution, in particular the normal distribution.

(20) From the normal distribution N, the autonomous (FIG. 3A) or manual (FIG. 3B) driving quantity may be determined, respectively. In particular, the driving quantity may be determined based on a property of the normal distribution N. For example, the driving quantity may be associated with the expected value of the normal distribution N. Additionally or alternatively, it is also conceivable that the driving quantity is associated with or determined based on another property, as e.g. the variance or the full width at half maximum (FWHM), or possibly a combination thereof.

(21) In particular, the driving quantity may be determined based on a comparison of a property of the normal distribution N with the same property of an averaged distribution, the averaged distribution being preferably based on a plurality of normal distributions obtained from a plurality of driving periods of different vehicles driven by an autonomous driving unit or different human driver, respectively. For example, if the expected value of the average distribution with regard to vehicle speed during autonomous driving amounts to a first speed and the expected value of the determined normal distribution N with regard to vehicle speed during autonomous driving amounts to a second speed, the driving quantity may be determined as the difference between the first and the second speed.

(22) FIG. 4 shows an example of determining a driving quantity based on the time interval Δt between an autonomous driving unit outputting a control takeover request R (also referred to as control takeover request event R) and human driver 3 taking over control of a vehicle 1 (also referred to as control takeover event T). By basing the driving quantity on the time interval Δt, it is possible to quantify the driver's degree of attentiveness as well as the effectiveness of the design of the critical man-machine aspects of the autonomous driving unit particularly with respect to alerting the driver 3 for imminent transfer of control.

(23) The time interval Δt may be determined as the time lag between the control takeover request event R and the detection of a control signal generated by an interaction of the human driver 3 with the vehicle 1, in particular the control system of the vehicle 1. For example, the time interval Δt may be the time lag between the control takeover request event R and a contact being detected between the driver's hands and the steering wheel, a pressure exerted on a pedal being detected, a change in posture of the driver being detected, and/or the like. Accordingly, the driving quantity may be a time lag metric.

(24) Preferably, the driving quantity is determined based further on a vehicle speed the vehicle 1 exhibits during the time interval Δt. In particular, the time interval Δt may be weighted by the vehicle speed. This results in a distance d the vehicle 1 covers between the control takeover request event R and the control takeover event T. Because the vehicle speed may change during the time interval Δt, it is preferred to base the driving quantity on an average speed.

(25) Further preferably, the change in driving mode may be grouped for a particular locale as e.g. urban, rural or highway. This may help to obtain a more differentiated assessment of the intertwined impact of both handover alert design and driver alertness, because the speed of the vehicle 1 during control handovers in urban areas is significantly lower than during control handovers on a highway.

(26) From a plurality of such changes in driving mode, occurring for example during a driving period of a plurality of journeys of the vehicle 1 e.g. over ten days, a month or a quarter of a year and/or with respect to a particular locale category, a distribution of the weighted time interval, i.e. the distance d, may be obtained (see FIG. 3). The driving quantity is preferably associated with a property of this distribution, for example its expected value. In particular, the driving quantity may be associated with an average of a particular part of the distribution, e.g. the fourth quartile. In that way, only the 25% worst cases are taken into account, the rationale being here that not too many changes of control are to be expected and thus the focus should be on the most meaningful cases in terms of causation of AI assisted driving accidents.

(27) This driving quantity may be rated, e.g. by comparing it to a corresponding property of an average distribution of the weighted time interval obtained for e.g. a plurality of other vehicles or from records of the same vehicle and/or driver. Alternatively, the driving quantity may be determined by the comparison, e.g. as the difference between the property of the distribution of the present vehicle 1 and the corresponding property of the average distribution obtained from the plurality of other vehicles or records, respectively. If the driving quantity, e.g. the difference, exceeds a certain threshold, the driver 3 may be informed of a takeover attention deficit and/or autonomous navigation, at least for a certain locale category as urban or rural, may be suspended. Alternatively or additionally, the autonomous driving unit or the corresponding autonomous driving solution provider, respectively, may be assigned an increased premium from its insurer to reflect the ineffectiveness of its transfer of control solution relative to that of other autonomous driving solution providers.

(28) FIG. 5 shows an example of a system 4 for analyzing the control of a vehicle 1 containing an autonomous driving unit 5. The system 4 contains a detection unit 6 configured to detect a change in the driving mode from autonomous driving by the autonomous driving unit 5 to manual driving by a human driver and a sensor unit 7 configured to monitor at least one driving parameter before and/or after the change was detected. Further, the system 4 contains an evaluation unit 8 configured to determine, based on driving values provided by the sensor unit 7 upon monitoring with respect to the detected change driving mode, at least one driving quantity quantifying the quality of interplay between the autonomous driving unit 5 and the human driver. The evaluation unit 8 may further include position data indicative of the vehicle's position provided by e.g. a GPS sensor 9 in the determination of the at least one driving quantity, e.g. to compare a current driving value to a distribution of driving values obtained for other vehicles at the same position or to a statutory speed limit. In the present example, the evaluation unit 8 has access to a map database 10 containing map data, which provides e.g. information on such statutory speed limits with respect to location.

(29) Additionally, the map data may provide information on GPS signal quality. The GPS signal quality may be considered by the evaluation unit 9 with regard to the determination of the at least one driving quality. If e.g. the GPS signal is indicated as weak and thus considered unreliable, accelerometer of an accelerometer of the vehicle 1 or a mobile device carried along in the vehicle 1 may be used instead to determine the position of the vehicle 1.

(30) In the shown example, the evaluation unit 8 is configured as a central evaluation unit 8, e.g. a software module running on a processing unit communicatively coupled to the vehicle 1 by means of a wireless connection, for example via the Internet. However, in another embodiment (not shown), the evaluation unit 8 may be arranged in or be part of the vehicle 1, respectively.

(31) The evaluation unit 8 may evaluate the driving values provided by the sensor unit 7 during driving periods, e.g. a single journey or a plurality of journeys, with respect to the driving mode, in particular one of different stages A, A′, B, C, D of the change in driving mode (see FIG. 1). From the driving values, the evaluation unit 8 may determine a normal distribution, at least one property of which may be used to derive the driving quantity (see FIG. 2). In particular, the evaluation unit 8 may evaluate data in form of driving values obtained from a plurality of different vehicles as well or have at least access to a databank containing these data, and determine the driving quantity based on a comparison of at least one property of the normal distribution obtained for the present vehicle 1 and corresponding distributions obtained for the plurality of other vehicles using normal or non-parametric methods of inference.

(32) In particular, the evaluation unit 8 may be configured to determine, based on the driving values provided by the sensor unit 7, an autonomous driving quantity quantifying the driving of the autonomous driving unit 5 during a first stage A and/or a second stage B of the transfer in control and a manual driving quantity quantifying the driving of the human driver during a third stage C and/or a fourth stage D of the transfer in control (see FIG. 1). The autonomous and manual driving quantity may then be combined into the driving quantity quantifying the quality of the interplay between the autonomous driving unit 5 and the human driver, i.e. the quality of driving of the entity containing both the autonomous driving unit 5 and the human driver.

(33) For example, the evaluation unit 8 may be configured to determine a combined driving quantity Q.sub.C+D=Q.sub.M′ for manual driving during stages three (C) and four (D) of the change in driving mode as follows: When Q.sub.D>Q.sub.C then

(34) $Q_{M}^{'} = Q_{D} + %_{Acc C} .Math. [Q_{D} + \frac{s_{D}^{2}}{s_{C}^{2}}] .Math. (Q_{C} - Q_{D}),$
and when Q.sub.D<Q.sub.C then

(35) $Q_{M}^{'} = Q_{D} + %_{Acc C} .Math. [Q_{D} + \frac{s_{C}^{2}}{s_{D}^{2}}] .Math. (Q_{C} - Q_{D}) .$
Therein, Q.sub.D and Q.sub.C are the driving quantities quantifying the quality of driving during the fourth and third stage D and C, respectively, and %.sub.Acc C is the accident rate value for, e.g. nominal percentage of accidents occurring during, the third stage C. Further, S.sub.D.sup.2 and S.sub.C.sup.2 are the variances of the normal distributions determined from the driving values for driving during the fourth and third stage D and C, respectively (see FIG. 3).

(36) Similarly, the evaluation unit 8 may be configured to determine a combined driving quantity Q.sub.A+B=Q.sub.A′ for autonomous driving during stages one (A) and two (B) of the change in driving mode as follows: When Q.sub.A>Q.sub.B then

(37) $Q_{A}^{'} = Q_{A} + %_{Acc B} .Math. [Q_{A} + \frac{s_{A}^{2}}{s_{B}^{2}}] .Math. (Q_{B} - Q_{A}),$
and when Q.sub.A<Q.sub.B then

(38) $Q_{A}^{'} = Q_{A} + %_{Acc B} .Math. [Q_{A} + \frac{s_{B}^{2}}{s_{A}^{2}}] .Math. (Q_{B} - Q_{A}) .$
Therein, Q.sub.A and Q.sub.B are the driving quantities quantifying the quality of driving during the first and second stage A and B, respectively, and %.sub.Acc B is the accident rate value for, e.g. the nominal percentage of accidents occurring during, the second stage B. Further, S.sub.A.sup.2 and S.sub.B.sup.2 are the variances of the normal distributions determined from the driving values for driving during the first and second stage A and B, respectively.

(39) From the combined driving quantities for autonomous driving Q.sub.A+B=Q.sub.A′ and manual driving Q.sub.C+D=Q.sub.M′, the composite driving quantity Q.sub.A+B+C+D quantifying the quality of interplay between the autonomous driving unit 5 and the human driver may then be obtained as follows:

(40) When

(41) $Q_{M}^{'} > Q_{A}^{'} then Q_{A + B + C + D} = Q_{M}^{'} .Math. %_{M} + %_{A} .Math. [Q_{M}^{'} + \frac{s_{D}^{2}}{s_{A}^{2}}] .Math. (Q_{A}^{'} - Q_{M}^{'}),$
and
when

(42) $Q_{M}^{'} < Q_{A}^{'} then Q_{A + B + C + D} = Q_{M}^{'} .Math. %_{M} + %_{A} .Math. [Q_{M}^{'} + \frac{s_{A}^{2}}{s_{D}^{2}}] .Math. (Q_{A}^{'} - Q_{M}^{'}) .$
Therein, %.sub.M and %.sub.A are the relative mileages accrued under manual and autonomous control, respectively.

(43) Alternatively or additionally, the sensor data of the GPS sensor 9 and/or the GPS signal quality provided by the map database 10 may be used to determine the at least one driving quantity as follows: if the GPS signal is weak or indicated as weak, e.g. due to the vehicle passing through a tunnel, the driving values may be obtained by an extrapolation of previously obtained driving values. In particular, the speed of the vehicle 1 may be determined based on the locations of strong or at least reliable GPS signal, e.g. ahead and behind the tunnel, and the time needed by the vehicle 1 to travel between these locations. If speeding is detected this way, this may have a direct effect on the determined driving quantity.

(44) Alternatively or additionally, the evaluation unit 8 may be configured to interrelate the driving quantity, in particular for the different stages A-D of the change in driving mode, to a quantity distribution of the same driving quantity obtained from a plurality of other vehicles with an autonomous driving unit and different human drivers or from records of the present vehicle 1. By this means, it becomes possible for the evaluation unit 8 to assess the responsibility of either the autonomous driving unit 5 or the human driver in the case of an accident during one of the stages A-D of the change in driving mode. In other words, the determined driving quantity may be used as a forensic tool in case of accidents occurring during the change in driving mode, in particular during the second or third stage B, C.

(45) For example, if a driving quantity quantifying the quality of driving of the autonomous driving unit 5 during a part A′ of the first stage A, e.g. a first time interval prior to a control takeover request event, is substantially equal to the average of the same driving quantity obtained for the plurality of other vehicles during the first stage A, this may indicate responsibility of the human driver. That is because substantially equal quantities indicate no deterioration of the autonomous driving and thus a regularly operating autonomous driving unit 5.

(46) If, however, the driving quantity is higher than the average driving quantity obtained for the plurality of other vehicles during the first stage A, this may indicate deterioration of the autonomous driving, and thus suggest responsibility of the autonomous driving unit. That is, the relative increase of the driving quantity, e.g. due to higher speed, stronger and/or more erratic acceleration and/or deceleration, can be associated with the (unsuccessful) attempt of the autonomous driving unit 5 to cope with the upcoming complex traffic scenario.

(47) Examples for the different driving quantities which may be determined for the different stages A-D of the change in driving mode are given in the table below as follows:

(48) TABLE-US-00001 Period Driving quantity notation during first stage A Q.sub.A (quantifying the quality of unimpaired autonomous driving) during a predetermined first time Q.sub.pre-B (quantifying the quality of interval prior to the second stage B autonomous driving in view of an (stage A′) upcoming complex traffic scenario) during second stage B Q.sub.B (quantifying the quality of autonomous driving in view of immanent handover) Q.sub.Δt (quantifying the quality of handover alert design and driver alertness, see FIG. 3) during third stage C Q.sub.C (quantifying the quality of manual driving in view of a complex traffic scenario) during fourth stage D Q.sub.D (quantifying the quality of unimpaired manual driving)

(49) Further, the different driving quantities with exemplary interrelationships or relationships to their average, respectively, as well as the possible inferred responsibility is given in the table below as follows:

(50) TABLE-US-00002 Driving quantity Responsible Q.sub.Δt > Q.sub.Δt, average human driver Q.sub.B ≈ Q.sub.A Q.sub.pre-B ≈ Q.sub.A Q.sub.C > Q.sub.D Q.sub.Δt ≈ Q.sub.Δt, average autonomous driving unit Q.sub.B > Q.sub.A Q.sub.pre-B > Q.sub.A Q.sub.C ≈ Q.sub.D

(51) This responsibility may be assigned as described in the above table particularly if an accident occurs after the autonomous driving unit has attempted to re-engage the driver, i.e. if the autonomous driving unit has outputted a control takeover request. If, on the other hand, the accident occurs during autonomous driving with no attempt to re-engage the driver, the responsibility may be assigned to the autonomous driving unit or its manufacturer, respectively.

LIST OF REFERENCE SIGNS

(52) 1 vehicle 2 method 3 driver 4 system 5 autonomous driving unit 6 detection unit 7 sensor unit 8 evaluation unit 9 GPS sensor 10 map database A-D stages of the change in driving mode A′ part of the first stage N normal distribution R control takeover request event T control takeover event Vi quality value distribution d distance t time t1, t2 first, second time interval Δt time interval

Method and system for analyzing the control of a vehicle

Inventors

Cpc classification

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B60W60/0057

PERFORMING OPERATIONS; TRANSPORTING

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B60W2540/18

PERFORMING OPERATIONS; TRANSPORTING

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B60W60/0053

PERFORMING OPERATIONS; TRANSPORTING

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PERFORMING OPERATIONS; TRANSPORTING

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PERFORMING OPERATIONS; TRANSPORTING

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B60W2520/10

PERFORMING OPERATIONS; TRANSPORTING

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PERFORMING OPERATIONS; TRANSPORTING

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B60W40/09

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B60W40/04

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B60W60/00

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B60W40/12

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Abstract

Claims

Description