WO2018029758A1 - 自動運転車両の制御方法及び制御装置 - Google Patents
自動運転車両の制御方法及び制御装置 Download PDFInfo
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- WO2018029758A1 WO2018029758A1 PCT/JP2016/073327 JP2016073327W WO2018029758A1 WO 2018029758 A1 WO2018029758 A1 WO 2018029758A1 JP 2016073327 W JP2016073327 W JP 2016073327W WO 2018029758 A1 WO2018029758 A1 WO 2018029758A1
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- vehicle
- driving
- manual
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- automatic driving
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Definitions
- the present invention relates to a control method and a control device for an autonomous driving vehicle.
- Patent Document 1 a technology for switching to automatic driving when an override is detected in switching from manual driving to automatic driving in a control device for an automatic driving vehicle has been proposed.
- Patent Document 1 does not examine changes in driving characteristics when switching from manual operation to automatic operation. Therefore, there has been a problem that the occupant feels uneasy when switching from manual operation to automatic operation.
- the present invention has been made in order to solve such a conventional problem, and an object of the present invention is to provide an automatic that can suppress anxiety of an occupant when switching from manual operation to automatic operation.
- An object is to provide a method and a control device for a driving vehicle.
- the automatic operation when the manual operation is switched to the automatic operation, the automatic operation is executed while maintaining the manual operation characteristic that is the operation characteristic at the time of the manual operation.
- FIG. 1 is a block diagram showing a configuration of a control device for an autonomous driving vehicle according to an embodiment of the present invention.
- FIG. 2 is an explanatory diagram showing changes in maintenance time T1, switching time T2, vehicle speed, and inter-vehicle distance when switching from manual operation to automatic operation.
- FIG. 3 is a block diagram showing a detailed configuration of the own vehicle state detection unit.
- FIG. 4 is a block diagram illustrating a detailed configuration of the surrounding state detection unit.
- FIG. 5 is an explanatory diagram showing three learning methods for learning the characteristics of driving behavior by machine learning.
- FIG. 6 is an explanatory diagram showing the flow of learning driving behavior for each detected feature point.
- FIG. 7 is an explanatory diagram showing classification of travel situations.
- FIG. 1 is a block diagram showing a configuration of a control device for an autonomous driving vehicle according to an embodiment of the present invention.
- FIG. 2 is an explanatory diagram showing changes in maintenance time T1, switching time T2, vehicle speed, and inter-vehicle distance when switching
- FIG. 8 is an explanatory diagram illustrating an example of classifying data of other vehicles into meaningful items.
- FIG. 9 is a flowchart illustrating a procedure for executing machine learning based on input information and acquiring automatic driving characteristics and manual driving characteristics.
- FIG. 10A is an explanatory diagram when the traveling speed of the host vehicle and the traveling speed of other vehicles traveling around are both 80 [km / h].
- FIG. 10B is an explanatory diagram when the traveling speed of the host vehicle is 60 [km / h] and the traveling speed of other vehicles traveling around is 80 [km / h].
- FIG. 11 is a graph showing the relationship between vehicle speed and inter-vehicle distance and perceived amount, relationship between perceived amount and anxiety, and relationship between vehicle speed and inter-vehicle distance and anxiety.
- FIG. 10A is an explanatory diagram when the traveling speed of the host vehicle and the traveling speed of other vehicles traveling around are both 80 [km / h].
- FIG. 10B is an explanatory diagram when the traveling speed of the
- FIG. 12 is an explanatory diagram of processing for obtaining T1 and T2 based on the automatic driving characteristics and the manual driving characteristics stored in the driving characteristics database and the anxiety / physical quantity model.
- FIG. 13A is a graph showing the relationship between vehicle speed and anxiety.
- FIG. 13B is a graph showing the relationship between vehicle speed and anxiety when the road width is narrow and wide.
- FIG. 14A is a graph showing the relationship between the inter-vehicle distance and anxiety.
- FIG. 14B is a graph showing the relationship between the inter-vehicle distance and the sense of anxiety when other vehicles exist in the adjacent lane and when there are no other vehicles.
- FIG. 15 is an explanatory diagram showing the relationship between changes in vehicle speed and inter-vehicle distance, and maintenance time T1 and switching time T2.
- FIG. 16A is a graph showing a jerk section when the vehicle speed is changed.
- FIG. 16B is a graph showing a change in acceleration when the vehicle speed is changed.
- FIG. 16C is a graph showing a change in speed when the vehicle speed is changed.
- FIG. 17A is an explanatory diagram illustrating a situation where the host vehicle is traveling at 40 [km / h].
- FIG. 17B is a graph showing changes in vehicle speed when the vehicle speed is increased from 40 [km / h] to 60 [km / h].
- FIG. 17C is an explanatory diagram illustrating a situation where the host vehicle is traveling at 80 km / h.
- FIG. 17D is a graph showing changes in vehicle speed when the vehicle speed is increased from 80 [km / h] to 100 [km / h].
- FIG. 18A is an explanatory diagram illustrating a situation in which the host vehicle is traveling at 40 [km / h] on a narrow road.
- FIG. 18B is a graph showing changes in vehicle speed when the vehicle speed is increased from 40 [km / h] to 60 [km / h] while traveling on a road having a narrow road width.
- FIG. 18C is an explanatory diagram showing a situation where the host vehicle is traveling at 40 [km / h] on a wide road.
- FIG. 18A is an explanatory diagram illustrating a situation in which the host vehicle is traveling at 40 [km / h] on a narrow road.
- FIG. 18B is a graph showing changes in vehicle speed when the vehicle speed is increased from 40 [km / h] to 60 [km / h] while traveling on a road having a narrow road
- FIG. 18D is a graph showing changes in vehicle speed when the vehicle speed is increased from 40 [km / h] to 60 [km / h] while traveling on a wide road.
- FIG. 19A is an explanatory diagram illustrating a situation where the vehicle is traveling at an inter-vehicle distance of 50 [m].
- FIG. 19B is a graph showing a change in the inter-vehicle distance when the inter-vehicle distance is shortened from 50 [m] to 20 [m].
- FIG. 19C is an explanatory diagram showing a situation where the vehicle is traveling at an inter-vehicle distance of 100 [m].
- FIG. 19A is an explanatory diagram illustrating a situation where the vehicle is traveling at an inter-vehicle distance of 50 [m].
- FIG. 19B is a graph showing a change in the inter-vehicle distance when the inter-vehicle distance is shortened from 50 [m] to 20 [m].
- FIG. 19C is an explanatory diagram
- FIG. 19D is a graph showing a change in the inter-vehicle distance when the inter-vehicle distance is shortened from 100 [m] to 70 [m].
- FIG. 20A is an explanatory diagram illustrating a situation where the inter-vehicle distance is 80 [m] and another vehicle is traveling in the adjacent lane.
- FIG. 20B is a graph showing a change in the inter-vehicle distance when the inter-vehicle distance is shortened from 80 [m] to 50 [m] when another vehicle exists in the adjacent lane.
- FIG. 20C is an explanatory diagram illustrating a situation where the inter-vehicle distance is 80 [m] and no other vehicle is traveling in the adjacent lane.
- FIG. 20A is an explanatory diagram illustrating a situation where the inter-vehicle distance is 80 [m] and another vehicle is traveling in the adjacent lane.
- FIG. 20B is a graph showing a change in the inter-vehicle distance when the inter-veh
- FIG. 20D is a graph showing a change in the inter-vehicle distance when the inter-vehicle distance is shortened from 80 [m] to 50 [m] when there is no other vehicle in the adjacent lane.
- FIG. 21 is an explanatory diagram showing switching of driving characteristics when the host vehicle stops at the maintenance time T1.
- FIG. 22 is a flowchart showing a processing operation of the control device for an autonomous driving vehicle according to the embodiment of the present invention.
- FIG. 23 is an explanatory diagram showing manual operation characteristics and automatic operation characteristics executed in a modification of the present invention.
- FIG. 1 is a block diagram showing a configuration of a control device for an autonomous driving vehicle according to an embodiment of the present invention.
- the control device for an autonomous driving vehicle includes a traveling state detection unit 1, an individual-compatible driving characteristic determination unit 4, a driving characteristic database 7, an automatic driving characteristic determination unit 8, and A switching parameter setting unit 11 is provided.
- maintenance time T1 when driving
- the manual driving characteristics described here are driving characteristics when the occupant is manually driving.
- the manual driving characteristics include vehicle speed, acceleration, inter-vehicle distance, steering acceleration, yaw rate, and the like.
- the manual driving characteristics are not limited to those described above, and any manual driving characteristics can be applied as long as they are generally used when indicating the characteristics of the vehicle.
- the operation characteristic is gradually changed to the automatic operation characteristic during an operation characteristic switching time T2 (hereinafter, abbreviated as “switching time T2”) obtained by a method described later, and is switched to the automatic operation characteristic at time t2.
- the automatic driving characteristics described here are driving characteristics different from the manual driving characteristics. This automatic driving characteristic may be set by learning the characteristics of the occupant's manual driving or may be set for each driving scene (general road, highway, etc.). Anything can be used.
- the vehicle speed (curve q11) and the inter-vehicle distance (curve q12) are shown as examples of physical quantities of the autonomous driving vehicle. In FIG.
- the driving characteristic during the automatic driving is switched from the manual driving characteristic to the automatic driving characteristic.
- the driving characteristic during the automatic driving is not necessarily limited to the driving characteristic during the manual driving. If the driving characteristic is changed to a different driving characteristic, there is no limitation on the method of changing the driving characteristic.
- the processing circuit includes a processing device including an electric circuit.
- Processing devices also include devices such as application specific integrated circuits (ASICs) and conventional circuit components arranged to perform the functions described in the embodiments.
- ASICs application specific integrated circuits
- the traveling state detection unit 1 includes a host vehicle state detection unit 2 that detects the state of the host vehicle and a surrounding state detection unit 3 that detects a surrounding state.
- the host vehicle state detection unit 2 acquires vehicle speed data detected by the vehicle speed sensor 32, acceleration data detected by the acceleration sensor 33, and steering angle data detected by the steering angle sensor 34. Based on these data, the traveling state of the host vehicle is detected.
- the data detected by the own vehicle state detection unit 2 is output to the manual driving learning unit 5 and the automatic driving learning unit 9 shown in FIG.
- the surrounding state detection unit 3 includes a vehicle interval detection unit 35, a non-vehicle detection unit 36, a surrounding vehicle type detection unit 37, a lane detection unit 38, a road type detection unit 39, and traffic.
- An information detection unit 40 is provided.
- the vehicle interval detection unit 35 detects the vehicle interval on the front, rear, left and right sides of the host vehicle using a radar or the like.
- the non-vehicle detection unit 36 detects an object other than a vehicle such as a pedestrian or a bicycle existing around the host vehicle based on an image captured by a camera that captures the surroundings.
- the surrounding vehicle type detection unit 37 detects the type of vehicle existing around the host vehicle from the image taken by the camera. For example, passenger cars, trucks, buses, motorcycles, etc. are detected.
- the lane detector 38 detects a road lane from an image taken by a camera.
- the road type detection unit 39 detects the road type from information obtained from the navigation device.
- the traffic information detection unit 40 detects traffic information from information obtained from the navigation device.
- Various types of information can also be detected by vehicle-to-vehicle communication, road-to-vehicle communication, sonar, and the like.
- Data detected by the ambient condition detection unit 3 is output to the manual driving learning unit 5 and the automatic driving learning unit 9 shown in FIG.
- the personally-suitable driving characteristic determination unit 4 includes a manual driving learning unit 5 and a manual driving characteristic setting unit 6.
- the automatic driving characteristic determination unit 8 includes an automatic driving learning unit 9 and an automatic driving characteristic setting unit 10.
- the manual driving learning unit 5 and the automatic driving learning unit 9 are based on each piece of data (data acquired by each sensor shown in FIG. 3) indicating the driving state detected by the driving state detection unit 1 during manual driving.
- the type of road on which the vehicle is traveling is specified, and the driving characteristics of the occupant are learned for each type of road.
- the driving characteristics are vehicle speed, average vehicle speed, acceleration, yaw rate, brake timing, timing when changing lanes, merging point when entering the highway, merging when the occupant (for example, driver) performs manual driving. Speed, etc.
- the learned driving characteristics may be a single value such as a vehicle speed of 50 km / h or a range of 30 km / h to 60 km / h, for example, at the vehicle speed.
- the operating characteristics may be expressed using a function such as a probability density distribution.
- indicates the change of a driving characteristic during automatic driving
- FIG. 5 is an explanatory diagram showing three learning methods.
- learning method “1” learning is performed by human analysis.
- learning method “2” a hypothesis based on human knowledge and experience is set, and further learning is performed by machine learning.
- learning method “3” learning is performed fully automatically by machine learning. In the present embodiment, learning is performed by employing the method “2” as an example.
- FIG. 6 is an explanatory diagram showing a flow of learning features from the data detected by the traveling state detection unit 1.
- data is collected from the traveling state detection unit 1. Collect the data on the driving situation and surroundings of the vehicle. After collecting the data, necessary attribute data is extracted in step a2.
- the data collected by the driving situation detection unit 1 is not necessarily related to driving behavior, and if data unrelated to driving behavior is used as learning material, the learning result may be adversely affected. is there. For this reason, only necessary data (attribute data) is extracted in the process of step a2.
- step a3 elements that adversely affect learning such as noise included in the attribute data extracted in the process of a2 are removed, and the attribute data is corrected.
- step a4 the attribute data is classified into meaningful items (parameters).
- FIG. 8 shows an example of classifying data of other vehicles into meaningful items.
- each of these data is reclassified to acquire various items such as “the number of preceding vehicles”, “the number of preceding vehicles”, and “distance between the preceding vehicles”.
- pre-processing 6 is defined as pre-processing, and in step a5, the parameters generated by the pre-processing are used as input for machine learning, and machine learning is executed.
- machine learning for example, SOM (Self-Organizing-Map), SVC (Support Vector-Machine Classification), SGD (Stochastic-Gradient-Decent), logistics regression, or the like can be used.
- ⁇ ⁇ This machine learning outputs the type of road that is running.
- various road types are classified into (for example, b1 to b8). Specifically, when driving on a highway, “b1. Highway” is set, and when driving on two roads on one side of a general road, “b2. Main road” is set. “B3. Non-trunk road” when traveling, and “b4. Intersection” when traveling on a general road intersection. Further, when traveling on a general road or highway and no preceding vehicle is present, “b5. Cruise traveling” is set. When traveling on a general road or highway and a preceding vehicle is present, “b6. If the vehicle restarts after stopping at the intersection of general roads, it is classified as “b7. Crossing the intersection”, and if it turns right at the intersection of general roads, it is classified as “b8.
- step a6 the road type specified by learning and the driving characteristics of the road type are stored in the driving characteristic database 7.
- FIG. 9 is an explanatory diagram showing a process of storing the automatic driving characteristics and the manual driving characteristics in the driving characteristics database 7 by the automatic driving learning section 9 and the manual driving learning section 5 by taking a scene cruising on a two-lane road as an example. is there.
- step c ⁇ b> 1 of FIG. 9 the manual driving learning unit 5 and the automatic driving learning unit 9 obtain various input information from the traveling state detection unit 1. Specifically, road information such as positional relationship with other vehicles, speed limit, and traveling information of the host vehicle are acquired.
- step c2 the automatic driving learning unit 9 and the manual driving learning unit 5 execute a machine learning algorithm based on the acquired input information as shown in FIG. This identifies the type of road on which the vehicle is traveling.
- steps c3 to c5 the automatic driving characteristic corresponding to the identified road type is acquired from the driving characteristic database 7 and set as the driving characteristic during automatic driving.
- driving characteristics are learned in steps c6 to c8 according to the specified road type.
- Steps c3 to c5 will be described for the case of automatic operation.
- the automatic driving learning unit 9 acquires the automatic driving characteristic corresponding to the road type, and sets it as the driving characteristic during the automatic driving.
- FIG. 10A shows a situation where the host vehicle V1 cruises on the left lane of a two-lane road and other vehicles V2 and V3 travel on the right lane, and the speed limit is 80 [km / h].
- the case where the traveling speeds of the other vehicles V2, V3 are 80 [km / h] is shown.
- the travel speed is 80 [km / h] as a learned driving characteristic, that is, in this scene, the occupant travels at 80 [km / h] or travels. If the possibility is high, the traveling speed of the host vehicle V1 is set to 80 [km / h].
- step c4 the automatic driving learning unit 9 learns driving characteristics even during automatic driving.
- the instructed driving characteristics may be learned.
- operation can be reflected in the driving
- the driving characteristics during automatic driving are stored in the driving characteristics database 7.
- step c5 the data to be stored is labeled for the type of road that has been traveled so that it can be easily referred to later.
- the manual driving learning unit 5 acquires the manual driving characteristics according to the identified road type. For example, as shown in FIG. 10B, in a situation where the host vehicle V1 cruises in the left lane of a two-lane road and other vehicles V2 and V3 travel in the right lane, the speed limit is 80 [km / h], A situation is assumed in which the traveling speeds of the other vehicles V2, V3 are 80 [km / h]. In this situation, when the traveling speed of the host vehicle V1 is 60 [km / h], it is determined that the occupant tends to travel at a speed that is 75% of the speed limit during cruise traveling. Note that the cruise traveling shown in the present embodiment is defined as a situation in which an inter-vehicle time (a numerical value obtained by dividing the inter-vehicle distance by the traveling speed) with the preceding vehicle continues for 30 seconds or more.
- an inter-vehicle time a numerical value obtained by dividing the inter-vehicle distance by the traveling speed
- step c7 the manual driving learning unit 5 stores the driving characteristics acquired by learning in the driving characteristics database 7. Further, in step c8, the data to be stored is labeled for the type of road that has been traveled so that it can be easily referred to later. In this way, the automatic driving characteristic during automatic driving of the host vehicle and the manual driving characteristic during manual driving can be acquired by learning and stored in the driving characteristic database 7.
- the switching parameter setting unit 11 includes an anxiety / physical quantity model storage unit 12 (model storage unit) and a parameter control unit 13.
- the anxiety / physical quantity model storage unit 12 stores an anxiety / physical quantity model to be described later.
- the parameter control unit 13 estimates the feeling of anxiety felt by the occupant when switching the own vehicle from manual driving to automatic driving based on the current driving situation and anxiety / physical quantity model of the own vehicle. Accordingly, the maintenance time T1 shown in FIG. 2 and the switching time T2 required for switching to the automatic driving characteristics after the maintenance time T1 has elapsed are set.
- the anxiety / physical quantity model storage unit 12 stores an anxiety / physical quantity model indicating a relationship between a physical quantity when the host vehicle is traveling and anxiety felt by the occupant.
- FIG. 11 is an explanatory diagram showing an anxiety / physical quantity model. According to the well-known Weaver-Fechner rule, as shown in the graph 61, it is known that the perceived amount of the vehicle speed (or inter-vehicle distance) and the occupant's vehicle speed (or inter-vehicle distance) changes as shown by the curve Q1. .
- the horizontal axis of the graph 61 indicates the perceived amount of the vehicle speed (or the inter-vehicle distance) and the vertical axis indicates the vehicle speed (or the inter-vehicle distance).
- the horizontal axis is the vehicle speed as an example
- the change in the perceived amount with respect to the same vehicle speed change decreases.
- the change in the perceived amount is from e1 to e2
- the vehicle speed is from d2.
- the change of the perceptual amount when changing to d3 is from e2 to e3, that is, the perceptual amount despite the increase of 20 [km / h].
- the change of e1 to e2 is larger than e2 to e3 because when the vehicle accelerates from d1 to d2, the occupant undergoes a greater change than when the vehicle accelerates from d2 to d3. It shows that you feel like you are.
- the change in the perceived amount with respect to the change in the inter-vehicle distance increases as the inter-vehicle distance decreases. This indicates that the perceived amount perceived by the occupant is greater when the inter-vehicle distance is reduced from 10 m to 5 m than when the inter-vehicle distance is reduced from 10 m to 15 m, for example. ing.
- the graph 62 shows the relationship between the perceived amount of the vehicle speed and the anxiety of the vehicle speed.
- the curve Q2 shows the characteristic when the road width is narrow and the curve Q3 shows the characteristic when the road width is wide. It can be seen that as the perceived amount of the vehicle speed increases, the anxiety felt by the occupant with respect to the vehicle speed increases, and that the anxiety increases as the road width decreases.
- Curve Q4 indicates anxiety that the occupant feels with respect to vehicle speed when the road width is narrow and curve Q5 when the road width is wide. It can be seen that as the vehicle speed increases, the anxiety felt by the occupant increases, and that the anxiety increases more when the road width is narrow.
- the change in anxiety when the vehicle speed accelerates from 20 [km / h] to 40 [km / h] is anxiety when the vehicle speed accelerates from 40 [km / h] to 60 [km / h]. Greater than change. That is, although both are increased by 20 [km / h], the change in anxiety is 40 [km / h] from 20 [km / h] to 40 [km / h]. It is larger than the acceleration from 60 km / h.
- the perceived amount of the inter-vehicle distance and the anxiety about the inter-vehicle distance change as shown by the curves Q6 and Q7 in the graph 64.
- Curve Q6 shows the characteristics when there is no other vehicle in the adjacent lane (when the adjacent lane is empty), and curve Q7 shows the characteristic when there is another vehicle in the adjacent lane (when the adjacent lane is blocked). Show. It can be seen that as the perceived amount of the inter-vehicle distance decreases (in the left direction of the curves Q6 and Q7 in the figure), the anxiety felt by the occupant with respect to the inter-vehicle distance increases. Furthermore, it can be seen that the anxiety is greater when there is another vehicle in the adjacent lane than when there is no other vehicle.
- a curve Q8 indicates anxiety that the occupant feels with respect to the inter-vehicle distance when there is another vehicle in the adjacent lane
- a curve Q9 indicates that there is no other vehicle in the adjacent lane. It can be seen that the anxiety felt by the occupant increases as the inter-vehicle distance decreases, and that the anxiety increases when there is another vehicle in the adjacent lane.
- the change in anxiety when the inter-vehicle distance decreases from 15 [m] to 10 [m] is smaller than the change in anxiety when the inter-vehicle distance decreases from 10 [m] to 5 [m]. That is, in both cases, the change in anxiety decreased from 15 [m] to 10 [m] from 10 [m] to 5 [m], despite the decrease in inter-vehicle distance of 5 [m]. Smaller than you do.
- the parameter control unit 13 shown in FIG. 1 extracts manual driving characteristics and automatic driving characteristics stored in the driving characteristic database 7 in accordance with the road type specified by machine learning, and as shown in FIG.
- the parameter control unit 13 sets the maintenance time T1 and the switching time T2 required for switching the manual operation to the automatic operation based on the input anxiety.
- the anxiety / physical quantity model used in the anxiety / physical quantity model storage unit 12 may be set for each road type.
- the parameter control unit 13 has a function as a switching control unit that performs control to switch from manual operation to automatic operation.
- the speed that can be increased within a certain time is determined according to the vehicle speed.
- a curve q1 is a graph showing the relationship between the vehicle speed and the feeling of anxiety felt by the occupant.
- the allowable value of the change amount of anxiety within a certain time is set as “allowable change amount X1 (first threshold)”. Then, the vehicle speed is changed so that the amount of change in anxiety within a certain time is equal to or less than the allowable change amount X1. That is, when the anxiety increases with the switching from the manual operation to the automatic operation, the transition pattern is changed so that the increased amount of the anxiety within a certain time is equal to or less than the first threshold value.
- the anxiety change amount when the vehicle speed increases from 20 [km / h] to 40 [km / h] is the allowable change amount X1. Therefore, when the current vehicle speed is 20 [km / h], acceleration to 40 [km / h] within a certain time is allowed (see arrow Y1).
- a distance that can approach the preceding vehicle within a predetermined time is determined according to the inter-vehicle distance.
- a curve q4 is a graph showing the relationship between the inter-vehicle distance and the feeling of anxiety felt by the occupant.
- the allowable value of the change amount of anxiety within a certain time is set as “allowable change amount X2 (first threshold)”. Then, the inter-vehicle distance is changed so that the amount of change in anxiety within a certain time is equal to or less than the allowable change amount X2.
- the anxiety change amount when the inter-vehicle distance is changed from 20 [m] to 10 [m] is the allowable change amount X2. Therefore, when the current inter-vehicle distance is 20 [m], it is allowed to shorten to 10 [m] within a certain time (see arrow Y5). On the other hand, when the current inter-vehicle distance is 40 [m], it is allowed to shorten to 20 [m] within a predetermined time (see arrow Y6).
- the inter-vehicle distance that can be shortened within a predetermined time is determined according to whether or not there is another vehicle in the adjacent lane.
- a curve q5 indicates a characteristic when another vehicle exists in the adjacent lane
- a curve q6 indicates a characteristic when no other vehicle exists in the adjacent lane.
- the inter-vehicle distance is allowed to be shortened from 20 [m] to 10 [m] within a certain time (see arrow Y7).
- the inter-vehicle distance is allowed to be reduced from 35 [m] to 10 [m] within a certain time (see arrow Y8).
- the parameter control unit 13 sets the maintenance time T1 to be longer as the anxiety increases.
- the length of the switching time T2 is set so that the amount of change in anxiety within a certain time is equal to or less than the allowable change amount X1.
- the length of the switching time T2 is set so that the amount of change in anxiety within a certain time is equal to or less than the allowable change amount X2 (first threshold). By doing so, sudden changes in anxiety can be suppressed.
- the transition pattern is set so that the time required for the speed increase (switching time T2) becomes longer as the vehicle speed becomes lower, and the time required for the speed increase (switching time T2) becomes longer as the road width becomes narrower.
- the transition pattern is set so that the shorter the inter-vehicle distance is, the longer the approach time to the preceding vehicle (switching time T2) is. If there is another vehicle in the adjacent lane, the approach time to the preceding vehicle (switching) The transition pattern is set so that time T2) becomes longer.
- the transition pattern such that the rate of decrease in anxiety is greater than or equal to a preset threshold (second threshold). That is, when the anxiety decreases with the switching from the manual operation to the automatic operation, the transition pattern is changed so that the amount of decrease in the anxiety within a certain time is equal to or greater than the second threshold value.
- the amount of decrease in anxiety within a certain time period can be reduced by decelerating the vehicle speed from 100 [km / h] to 40 [km / h] or less within a certain time period. Is made larger than X1.
- the amount of decrease in anxiety within a certain time is made larger than X1. That is, by controlling the anxiety reduction rate to be equal to or greater than the second threshold, control is performed to quickly remove the anxiety felt by the occupant.
- the amount of decrease is made larger than X2.
- the amount of decrease in anxiety within the certain time is made larger than X2.
- the transition pattern is set so that the time required for deceleration (switching time T2) is shortened as the vehicle speed is high, and the transition pattern is set so that the time required for deceleration (switching time T2) is shortened as the road width is wide. Set. Furthermore, the transition pattern is set so that the longer the inter-vehicle distance is, the shorter the departure time from the preceding vehicle (switching time T2) is. When there is no other vehicle in the adjacent lane, the departure time (switching from the preceding vehicle) The transition pattern is set so that the time T2) is shortened.
- the parameter control unit 13 calculates the maintenance time T1 when switching from the manual operation to the automatic operation, and further calculates the switching time T2 until the automatic operation is executed. Furthermore, a transition pattern of operation characteristics at the switching time T2 is set.
- FIG. 15 is a diagram illustrating an example of setting the maintenance time T1 and the switching time T2.
- the control parameter that feels anxiety is the vehicle speed, and the T1 setting method when the vehicle speed is increased and the T1 setting method when the control parameter is the inter-vehicle distance and the inter-vehicle distance is shortened are shown.
- T1 When the current vehicle speed is low, T1 is set short and the switching time T2 is lengthened to avoid a sudden speed increase from a low speed and reduce anxiety of the occupant.
- the speed When the current vehicle speed is high, the speed is quickly increased by setting T1 long and setting the switching time T2 short. Thereby, a passenger's anxiety is reduced.
- the road width is narrow, by setting T1 to be long and T2 to be long, the inter-vehicle distance is gradually shortened to reduce the passenger's anxiety.
- the speed is quickly increased by setting T1 short and T2 short.
- FIG. 16 is an explanatory diagram showing a method for setting the switching time T2 when the vehicle speed is changed.
- a curve q21 in which a jerk interval is set and the maximum jerk and the minimum jerk in the jerk interval are set is determined.
- the maximum jerk interval, the minimum jerk interval, and the zero jerk interval be the same.
- a curve q22 indicating acceleration is obtained as shown in FIG. 16B.
- a curve q23 indicating the speed is obtained as shown in FIG. 16C.
- the curve q23 is set so that the speed from the current speed to the target speed becomes a smooth S-curve.
- the time until the target speed is reached from the current speed is set as the switching time T2.
- FIG. 17 is an explanatory diagram showing a method for setting the switching time T2 when the vehicle speed is increased by 20 [km / h].
- 17A and 17B show a case where the vehicle speed is increased from 40 [km / h] to 60 [km / h].
- the switching time T2 is lengthened.
- FIGS. 17C and 17D show a case where the vehicle speed is increased from 80 [km / h] to 100 [km / h].
- the switching time T2 is shortened. By doing so, it is possible to reduce the anxiety felt by the occupant. That is, when switching from manual operation to automatic operation, when the automatic operation is started, the vehicle speed is 80 [km / h] than the vehicle speed is 40 [km / h]. I feel a great sense of anxiety. Further, after switching to automatic driving, while changing from manual driving characteristics to automatic driving characteristics, the occupant increases anxiety due to changes in driving characteristics.
- the passenger's anxiety before starting to change the driving characteristics from the manual driving characteristics.
- the occupant's anxiety can be stabilized by the end of the maintenance time by increasing the maintenance time T1 as the vehicle speed increases, the occupant's anxiety until the driving characteristic changes to the automatic driving characteristic. Can be prevented from increasing excessively.
- the speed increase from 40 [km / h] to 60 [km / h] is from 80 [km / h] to 100 [km / h].
- the anxiety felt by the occupant is greater than the speed increase, so that the anxiety is reduced by setting the switching time T2 to be relatively long.
- FIG. 18 is an explanatory diagram showing a method for setting the switching time T2 when the vehicle speed is increased from 40 [km / h] to 60 [km / h].
- FIG. 18A shows a case where the host vehicle V1 is traveling on a road having a narrow road width H1.
- FIG. 18B when the road width is narrow, in addition to lengthening the maintenance time T1, the switching time T2 is lengthened.
- FIG. 18C and FIG. 18D show a case where the road width H2 of the road on which the vehicle travels is wide.
- the switching time T2 is shortened. By doing so, it is possible to reduce the anxiety felt by the occupant. That is, even if the speed is increased from 40 [km / h] to 60 [km / h], the feeling of anxiety that passengers feel is greater when the road width is narrow, so T1 and T2 are set relatively long. To reduce anxiety.
- FIG. 19 is an explanatory diagram showing a method for setting the switching time T2 when the inter-vehicle distance is shortened by 30 [m].
- 19A and 19B show a case where the inter-vehicle distance is set to 50 [m] to 20 [m].
- the switching time T2 is lengthened.
- FIGS. 19C and 19D show the case where the inter-vehicle distance is set to 100 [m] to 70 [m].
- the switching time T2 is shortened. By doing so, it is possible to reduce the anxiety felt by the occupant. In other words, even if there is the same 30 [m] approach, the approach from 50 [m] to 20 [m] is more anxious than the approach from 100 [m] to 70 [m]. Anxiety is reduced by setting T1 and T2 relatively long.
- FIG. 20 is an explanatory diagram showing a method for setting the switching time T2 when the inter-vehicle distance is shortened from 80 [m] to 50 [m].
- 20A and 20B show a case where there is another vehicle in the adjacent lane. As shown in FIG. 20B, when there is another vehicle in the adjacent lane, in addition to lengthening the maintenance time T1, the switching time T2 is lengthened.
- FIG. 20C and FIG. 20D show a case where there is no other vehicle in the adjacent lane.
- the switching time T2 is shortened. By doing so, it is possible to reduce the anxiety felt by the occupant. That is, even if there is an approach from the same 80 [m] to 50 [m], there is a greater sense of anxiety that the occupant feels when there is another vehicle in the adjacent lane, so T1 and T2 should be set relatively long. To reduce anxiety.
- the parameter control unit 13 shown in FIG. 1 performs an anxiety / physical quantity model when the host vehicle stops by a signal or the like before the total time (T1 + T2) of the maintenance time T1 and the switching time T2 elapses. Switch to automatic driving characteristics when restarting after stopping without using. That is, as shown in FIG. 21, when the own vehicle V1 passes the point P1 and is switched from manual operation to automatic operation, the manual operation characteristic is maintained thereafter. And when the own vehicle V1 stops at the point P2, it switches to an automatic driving characteristic after that. In this way, unnecessary switching of operating characteristics can be avoided.
- step S11 the surrounding state detection unit 3 detects the surrounding state of the host vehicle.
- step S12 the automatic driving learning unit 9 statistically learns the detected surrounding situation, and further classifies the current traveling situation in step S13. That is, as shown in FIG. 7, traveling conditions such as traveling on a highway and traveling on a general road are classified.
- step S14 the automatic driving characteristic setting unit 10 sets the automatic driving characteristic with reference to the driving characteristic database 7 based on the current traveling state.
- step S15 the host vehicle status detection unit 2 detects the current host vehicle status as shown in FIG.
- step S ⁇ b> 16 the manual driving learning unit 5 statistically learns the driving characteristics of the occupant, and in step S ⁇ b> 17 sets the manual driving characteristics according to the driving situation.
- the driving characteristics learned by the manual driving learning unit 5 are stored in the driving characteristics database 7.
- step S18 the parameter control unit 13 determines whether or not a switching operation (override) from manual operation to automatic operation has occurred.
- step S19 it is determined whether or not the manual operation characteristic set in step S17 is different from the automatic operation characteristic set in step S14. If they are not different (NO in step S19), the occupant does not feel uneasy at the time of switching, and the process proceeds to automatic driving control with automatic driving characteristics.
- step S20 the parameter control unit 13 refers to the anxiety / physical quantity model storage unit 12, and in step S21, the maintenance time T1 and switching are performed by the method described above. Time T2 is set. Thereafter, in step S22, the parameter control unit 13 executes automatic operation control using manual operation characteristics.
- step S23 the parameter control unit 13 determines whether or not T1 has elapsed. If it has elapsed (YES in step S23), the parameter control unit 13 gradually switches from manual operation characteristics to automatic operation characteristics in step S24.
- step S25 the parameter control unit 13 determines whether or not T2 has elapsed, and when it has elapsed (YES in step S25), executes automatic operation control.
- the automatic operation when switching from manual operation to automatic operation, the automatic operation is executed while maintaining the manual operation characteristic that is the operation characteristic at the time of the manual operation, so that the passengers feel uneasy. Can be avoided.
- the maintenance time T1 (manual operation maintenance time) is set and the manual operation characteristics are maintained for the maintenance time T1, the automatic operation characteristics are switched, so that it is possible to avoid making the passenger feel uneasy.
- the maintenance time T1 is set based on the relationship between the physical quantity of the autonomous driving vehicle (vehicle speed, distance between vehicles, etc.) and the occupant's anxiety, an appropriate maintenance time T1 corresponding to the traveling state of the autonomous driving vehicle is set. It becomes possible to do.
- the maintenance time T1 is lengthened when the vehicle speed is high and it is easy to feel anxiety, it is possible to set a more appropriate maintenance time T1, thereby avoiding the occupant feeling anxiety.
- the maintenance time T1 is lengthened, so that a more appropriate maintenance time T1 can be set, and it can be avoided that the passenger feels anxiety.
- the maintenance time T1 is increased when the inter-vehicle distance is short and it is easy to feel anxiety, it is possible to set a more appropriate maintenance time T1 and avoid making the occupant feel anxiety.
- the maintenance time T1 is lengthened, so that a more appropriate maintenance time T1 can be set, and it is possible to avoid making the passenger feel anxiety.
- the maintenance time T1 is set based on the feeling of anxiety felt by the occupant, it is possible to maintain manual driving characteristics without causing the occupant to feel a great anxiety.
- automatic driving is performed with automatic driving characteristics that are different from manual driving characteristics after maintaining the driving characteristics during manual driving, so automatic driving characteristics can be improved without causing the passengers to feel uneasy. Switching is possible.
- the anxiety given to the occupant can be suppressed.
- the amount of increase in anxiety within a certain time is set to be equal to or less than the first threshold value, so that anxiety due to sudden fluctuations in driving characteristics is suppressed. Can do.
- the shift from the manual driving characteristic to the automatic driving characteristic is performed so that the time required for the speed increase (driving characteristic switching time T2) becomes long. Setting becomes possible and can suppress sudden change of anxiety.
- the shift from the manual driving characteristic to the automatic driving characteristic is performed so that the time required for the speed increase (driving characteristic switching time T2) becomes long. Can be set, and sudden changes in anxiety can be suppressed.
- the manual driving characteristic shifts to the automatic driving characteristic so that the approach time to the preceding vehicle (driving characteristic switching time T2) becomes long.
- T2 can be set, and a sudden change in anxiety can be suppressed.
- driving characteristic switching time T2 when there is another vehicle in the adjacent lane and it is easy to feel anxiety, the shift from the manual driving characteristic to the automatic driving characteristic is made so that the approach time to the preceding vehicle (driving characteristic switching time T2) becomes longer.
- the driving characteristic switching time T2 can be set, and a sudden change in anxiety can be suppressed.
- the amount of decrease in anxiety within a certain time is set to be equal to or greater than the second threshold value, so that the driving characteristics can be switched quickly.
- the driving characteristic switching time T2 shifts from the manual driving characteristic to the automatic driving characteristic so that the time required for deceleration can be quickly switched, and the driving characteristic switching time T2 is appropriately set. It becomes possible to set.
- driving characteristic switching time T2 the time required for deceleration (driving characteristic switching time T2) is shifted from the manual driving characteristic to the automatic driving characteristic so that the road width is wider, quick switching is possible and the driving characteristic switching time T2 is appropriately set. It becomes possible to set to.
- the shift from the manual driving characteristic to the automatic driving characteristic is shortened so that the departure time (driving characteristic switching time T2) from the preceding vehicle is shortened, so that quick switching is possible and the driving characteristic switching time T2 Can be set appropriately.
- the shift from the manual driving characteristic to the automatic driving characteristic is performed so that the departure time (driving characteristic switching time T2) from the preceding vehicle is shortened, so that quick switching is possible.
- driving characteristic switching time T2 the departure time from the manual driving characteristic to the automatic driving characteristic
- the vehicle when the vehicle is switched from manual operation to automatic operation, it is determined whether or not the vehicle has stopped. If it is determined that the vehicle has stopped, it is automatically determined from the manual operation characteristics without setting the maintenance time T1 and the switching time T2. Since the operation characteristic is shifted, unnecessary calculation can be avoided.
- FIG. 23 is an explanatory diagram showing a modification.
- a brake operation is performed before the curve.
- the timing of the brake operation may differ between traveling by manual driving and traveling by automatic driving characteristics.
- reference numerals x1 to x5 indicate brake operation timings based on automatic driving characteristics.
- Reference numerals w1 to w5 indicate actual brake operation timings. In this case, the timing of the brake operation is the physical quantity of the host vehicle.
- the brake operation is performed at the symbol w1, which is the brake operation timing based on the manual operation characteristics.
- the time difference ⁇ t1 is maintained during the maintenance time T1. That is, the time difference between the symbols w2 and x2 is ⁇ t1.
- the time difference is gradually shortened like the time difference ⁇ t2 ( ⁇ t1) and ⁇ t3 ( ⁇ t2). Then, after the elapse of the switching time T2, the signs w5 and x5 are made to coincide. By doing so, it is possible to switch from manual driving to driving with automatic driving characteristics without causing the occupant to feel uneasy.
- control method and control apparatus of the autonomous driving vehicle of this invention were demonstrated based on embodiment of illustration, this invention is not limited to this,
- the structure of each part is arbitrary which has the same function. It can be replaced with a configuration one.
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Abstract
Description
本発明は、このような従来の課題を解決するためになされたものであり、その目的とするところは、手動運転から自動運転への切り替え時に、乗員の不安感を抑制することが可能な自動運転車両の制御方法及び制御装置を提供することにある。
その後、時刻t1で、後述する手法により求められる運転特性切替時間T2(以下、「切替時間T2」と略す)の間に、徐々に自動運転特性に変更し、時刻t2で自動運転特性に切り替える。ここで記述した自動運転特性とは、手動運転特性とは異なる運転特性のことである。この自動運転特性は、乗員の手動運転の特性を学習して設定するものでもよく、また走行シーン(一般道、高速道など)毎に設定されるものでもよく、従来からある自動運転の運転特性であればどのようなものでも構わない。図2では、自動運転車両の物理量として、車速(曲線q11)、及び車間距離(曲線q12)を例に挙げて示している。尚、図2においては、自動運転中における運転特性を手動運転特性から自動運転特性に切り替えるように記載したが、必ずしもそれに限らず、自動運転中の運転特性が、手動運転時の運転特性から、それとは異なる運転特性に変更されれば、運転特性の変更する方法は問わない。
図1に示すように、走行状況検出部1は、自車両の状況を検出する自車両状況検出部2と、周囲状況を検出する周囲状況検出部3を備えている。
個人適合運転特性判定部4は、手動運転学習部5と、手動運転特性設定部6を備えている。自動運転特性判定部8は、自動運転学習部9と、自動運転特性設定部10を備えている。
車両が走行している道路種別を特定し、道路種別ごとに乗員の運転特性を学習する。運転特性とは、乗員(例えば、運転者)が手動運転を実行する場合の、車速、平均車速、加速度、ヨーレート、ブレーキタイミング、車線変更時のタイミング、高速道路に進入するときの合流点、合流速度等である。そして、学習した運転特性に基づいて自動運転を実行することにより、自動運転が乗員の特徴に沿って実行されることになり、自動運転中に感じる乗員の違和感を抑制することができる。尚、学習された運転特性は、例えば、車速においては、車速50km/hといった1つの値としてもよく、30km/h~60km/hといった範囲としても良い。また、運転特性は、確率密度分布などの関数を用いて表されるようにしても良い。尚、乗員が、自動運転中に運転特性の変更を指示した場合に、指示した運転特性を学習して、以降の自動運転の運転特性に反映させるようにしても良い。
図9は、2車線道路を巡航しているシーンを一例に、自動運転学習部9及び手動運転学習部5により自動運転特性、手動運転特性を運転特性データベース7に保存する処理を示す説明図である。図9のステップc1において、手動運転学習部5及び自動運転学習部9は、走行状況検出部1より各種の入力情報を取得する。具体的には、他車両との位置関係、制限速度等の道路情報、自車両の走行情報等を取得する。
こうして、自車両が自動運転中の自動運転特性、及び手動運転中の手動運転特性を学習により取得し、運転特性データベース7に保存することができる。
次に、図1に示した切替パラメータ設定部11について説明する。切替パラメータ設定部11は、不安感・物理量モデル記憶部12(モデル記憶部)と、パラメータ制御部13を備えている。不安感・物理量モデル記憶部12は、後述する不安感・物理量モデルを記憶する。
不安感・物理量モデル記憶部12は、自車両走行時の物理量と、乗員が感じる不安感との関係を示す不安感・物理量モデルを記憶する。図11は、不安感・物理量モデルを示す説明図である。周知のウィーバー・フェヒナー則によれば、グラフ61に示すように、車速(又は車間距離)と乗員の車速(又は車間距離)の知覚量は、曲線Q1のように変化することが知られている。グラフ61の横軸は車速(又は車間距離)、縦軸は車速(又は車間距離)の知覚量を示している。
図1に示すパラメータ制御部13には、機械学習により特定した道路種別に合わせて、運転特性データベース7に記憶されている手動運転特性、自動運転特性を抽出し、そして、図12に示すように、不安感・物理量モデルを参照することにより、乗員の不安感が入力される。そして、パラメータ制御部13は、入力された不安感に基づいて、維持時間T1、及び手動運転を自動運転に切り替える際に要する切替時間T2を設定する。尚、本実施形態においては、不安感・物理量モデル記憶部12に用いる不安感・物理量モデルは、道路種別ごとに設定するようにしても良い。
また、図13Bに示すように、車速が同一であっても道路幅に応じて一定時間内に増速可能な速度が決められる。曲線q2は、道路幅が狭い場合、曲線q3は道路幅が広い場合の車速と乗員が感じる不安感との関係を示すグラフである。
一方、現在の車間距離が40[m]である場合には、一定時間内に20[m]まで短くすることが許容される(矢印Y6参照)。
しかしながら、現在の車間距離が40[m]である場合に一定時間内に10[m]までに減少する場合は、不安感の変化量がX2+X2=2X2となり、X2を超えるため許容されない。
上述のように、維持時間T1、切替時間T2を設定することにより、乗員が感じる不安感を低減した運転特性の切り替えが可能となる。その結果、不安感を感じさせることを回避できる。
このような方法で、切替時間T2を設定することにより、乗員に過大な加速度を感じさせることなく増速することが可能となる。
また、同じ20[km/h]の増速であっても、40[km/h]から60[km/h]への増速は、80[km/h]から100[km/h]への増速よりも乗員の感じる不安感が大きいので、切替時間T2を相対的に長く設定することにより、不安感を低減する。
即ち、図21に示すように、自車両V1が地点P1を通過した時点で手動運転から自動運転に切り替えられた場合には、その後手動運転特性を維持する。そして、地点P2で自車両V1が停止した場合には、その後、自動運転特性に切り替える。こうすることにより、不要な運転特性の切り替えを回避できる。
次に、本実施形態に係る自動運転車両の制御装置の処理動作の一例を、図22に示すフローチャートを参照して説明する。
初めに、ステップS11において、周囲状況検出部3は、自車両の周囲状況を検出する。ステップS12において、自動運転学習部9は、検出した周囲状況を統計的に学習し、更に、ステップS13において、現在の走行状況を分類する。即ち、図7に示したように、高速道路走行中、一般道路走行中等の走行状況を分類する。
これと同時に、ステップS15において、自車両状況検出部2は、図3に示したように、現在の自車両状況を検出する。ステップS16において、手動運転学習部5は、乗員の運転特性を統計的に学習し、ステップS17において、走行状況に応じた手動運転特性を設定する。また、手動運転学習部5で学習した運転特性を、運転特性データベース7に記憶する。
その後、ステップS22において、パラメータ制御部13は、手動運転特性を用いて自動運転制御を実行する。
ステップS25において、パラメータ制御部13は、T2が経過したか否かを判断し、経過した場合には(ステップS25でYES)、自動運転制御を実行する。
次に、本実施形態の変形例について説明する。図23は、変形例を示す説明図である。図23に示すように、自車両V1が連続するカーブ路を通行する場合には、カーブの手前でブレーキ操作を行う。ブレーキ操作のタイミングは、手動運転による走行と自動運転特性による走行で異なる場合がある。図23において、符号x1~x5は自動運転特性によるブレーキ操作のタイミングを示す。また、符号w1~w5は、実際のブレーキ操作のタイミングを示す。この場合、ブレーキ操作のタイミングが自車両の物理量である。
2 自車両状況検出部
3 周囲状況検出部
4 個人適合運転特性判定部
5 手動運転学習部
6 手動運転特性設定部
7 運転特性データベース
8 自動運転特性判定部
9 自動運転学習部
10 自動運転特性設定部
11 切替パラメータ設定部
12 不安感・物理量モデル記憶部(モデル記憶部)
13 パラメータ制御部
32 車速センサ
33 加速度センサ
34 ステア角度センサ
35 車両間隔検出部
36 非車両検出部
37 周辺車両種類検出部
38 車線検出部
39 道路種類検出部
40 交通情報検出部
T1 手動特性維持時間(維持時間)
T2 運転特性切替時間(切替時間)
Claims (22)
- 自動で走行する時の運転特性を設定し、前記運転特性に基づいて車両を自動で走行させる自動運転と乗員の操作に応じて車両を走行させる手動運転の切り替えが可能な自動運転車両の制御方法であって、
手動運転から自動運転に切り替わった場合、手動運転時の運転特性である手動運転特性を維持して自動運転を実行すること
を特徴とする自動運転車両の制御方法。 - 請求項1に記載の自動運転車両の制御方法において、
手動運転から自動運転に切り替わった場合、予め設定した手動特性維持時間だけ前記手動運転特性を維持して自動運転を実行すること
を特徴とする自動運転車両の制御方法。 - 請求項2に記載の自動運転車両の制御方法において、
前記自動運転時の物理量を検出し、
前記物理量に基づいて、前記手動特性維持時間を設定すること
を特徴とする自動運転車両の制御方法。 - 請求項3に記載の自動運転車両の制御方法において、
前記物理量として車速を検出し、
前記車速が高いほど、前記手動特性維持時間を長くすることを特徴とする自動運転車両の制御方法。 - 請求項3または4に記載の自動運転車両の制御方法において、
前記物理量として道路幅を検出し、
前記道路幅が狭いほど、前記手動特性維持時間を長くすることを特徴とする自動運転車両の制御方法。 - 請求項3~5のいずれか1項に記載の自動運転車両の制御方法において、
前記物理量として車間距離を検出し、
前記車間距離が短いほど、前記手動特性維持時間を長くすることを特徴とする自動運転車両の制御方法。 - 請求項3~6のいずれか1項に記載の自動運転車両の制御方法において、
前記物理量として、隣接車線に他車両が存在するか否か検出し、
前記他車両が存在する場合は、前記手動特性維持時間を長くすることを特徴とする自動運転車両の制御方法。 - 請求項2~7のいずれか1項に記載の自動運転車両の制御方法において、
前記自動運転時の乗員の不安感を検出し、
前記不安感に基づいて、前記手動特性維持時間を設定すること
を特徴とする自動運転車両の制御方法。 - 請求項1~8のいずれか1項に記載の自動運転車両の制御方法において、
手動運転時の運転特性を維持して自動運転を実行した後、手動運転特性とは異なる自動運転特性により自動運転を実行すること
を特徴とする自動運転車両の制御方法。 - 請求項9に記載の自動運転車両の制御方法において、
手動運転特性から自動運転特性に徐々に移行すること
を特徴とする自動運転車両の制御方法。 - 請求項9または10に記載の自動運転車両の制御方法において、
乗員の不安感の一定時間内の増加量が予め設定した第1閾値以下となるように、手動運転特性から自動運転特性に移行すること
を特徴とする自動運転車両の制御方法。 - 請求項9~11のいずれか1項に記載の自動運転車両の制御方法において、
車速を検出し、
前記車速が低いほど、増速に要する時間が長くなるように、手動運転特性から自動運転特性に移行する
ことを特徴とする自動運転車両の制御方法。 - 請求項9~12のいずれか1項に記載の自動運転車両の制御方法において、
道路幅を検出し、
前記道路幅が狭いほど、増速に要する時間が長くなるように、手動運転特性から自動運転特性に移行する
ことを特徴とする自動運転車両の制御方法。 - 請求項9~13のいずれか1項に記載の自動運転車両の制御方法において、
車間距離を検出し、
前記車間距離が短いほど、先行車両への接近時間が長くなるように、手動運転特性から自動運転特性に移行する
ことを特徴とする自動運転車両の制御方法。 - 請求項9~14のいずれか1項に記載の自動運転車両の制御方法において、
隣接車線に他車両が存在するか否か検出し、
前記隣接車線に他車両が存在する場合には、先行車両への接近時間が長くなるように、手動運転特性から自動運転特性に移行する
ことを特徴とする自動運転車両の制御方法。 - 請求項9~15のいずれか1項に記載の自動運転車両の制御方法において、
乗員の不安感の一定時間内の減少量が予め設定した第2閾値以上となるように、手動運転特性から自動運転特性に移行すること
を特徴とする自動運転車両の制御方法。 - 請求項9~16のいずれか1項に記載の自動運転車両の制御方法において、
車速を検出し、
前記車速が高いほど、減速に要する時間が短くなるように、手動運転特性から自動運転特性に移行すること
を特徴とする自動運転車両の制御方法。 - 請求項9~17のいずれか1項に記載の自動運転車両の制御方法において、
道路幅を検出し、
前記道路幅が広いほど、減速に要する時間が短くなるように、手動運転特性から自動運転特性に移行すること
を特徴とする自動運転車両の制御方法。 - 請求項9~18のいずれか1項に記載の自動運転車両の制御方法において、
車間距離を検出し、
前記車間距離が長いほど、先行車両からの離脱時間が短くなるように、手動運転特性から自動運転特性に移行すること
を特徴とする自動運転車両の制御方法。 - 請求項9~19のいずれか1項に記載の自動運転車両の制御方法において、
隣接車線に他車両が存在するか否か検出し、
前記隣接車線に他車両が存在しない場合には、先行車両からの離脱時間が短くなるように、手動運転特性から自動運転特性に移行すること
を特徴とする自動運転車両の制御方法。 - 請求項9~20のいずれか1項に記載の自動運転車両の制御方法において、
手動運転から自動運転に切り替わる場合に、車両が停車したか否か判定し、
停車したと判定した場合は、手動運転特性から自動運転特性に移行すること
を特徴とする自動運転車両の制御方法。 - 自動で走行する時の運転特性を設定し、前記運転特性に基づいて車両を自動で走行させる自動運転と乗員の操作に応じて車両を走行させる手動運転の切り替えが可能な自動運転車両の制御装置であって、
手動運転から自動運転に切り替わった場合、手動運転の運転特性である手動運転特性を維持して自動運転を実行する構成を備えた自動運転車両の制御装置。
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KR20190022759A (ko) | 2019-03-06 |
EP3498556B1 (en) | 2020-10-07 |
BR112019002546B1 (pt) | 2023-01-17 |
MX2019001525A (es) | 2019-07-04 |
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CN109562758B (zh) | 2020-07-17 |
BR112019002546A2 (pt) | 2019-05-21 |
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US10606264B2 (en) | 2020-03-31 |
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