Method for identifying and assessing the availability of a vehicle driver

Abstract

This method for identifying and assessing a vehicle driver's readiness to adapt to demands comprises the following steps: - Acquisition (step E1) of driver and / or vehicle and / or environmental data likely to influence the driver's readiness to receive demands from the vehicle; - Definition (step E2), based on the acquired data, of a set of internal and external vehicle factors and assignment to each factor a value depending on its impact on driver readiness; - Calculation (step E3) of a driver readiness index based on the set of internal and external factors; and - Determination (step E4) of the type of demand to be sent to the driver according to the calculated readiness index. Figure for the abstract: Fig. 1

Description

Title of the invention: Method for identifying and assessing the availability of a vehicle driver technical field

[0001] The present invention relates to the evaluation of the availability and possibility for a vehicle driver to receive different types of signals from human-machine interfaces depending on the driver's state and contextual constraints.

[0002] In particular, the present invention relates to the evaluation of the availability of a car driver based on various parameters such as the mental workload of said driver, the speed of the vehicle, the weather, traffic density, etc.

[0003] In general, the invention applies to any driver of a vehicle or workstation requiring them to remain focused under penalty of endangering an outside person or themselves. Previous techniques

[0004] The increasing connectivity of vehicles makes it possible to acquire a lot of data on the driving context in which the driver of said vehicle is operating.

[0005] In particular, measurement and detection sensors are constantly evolving, and the computing and information transmission capacity within vehicles is constantly increasing. In this context, electronic systems on board vehicles, for example motor vehicles, make it possible to offer numerous services to the driver: hazard alerts, information on charge level, charging station availability, traffic, route, weather, reading of signs and lines, all this via prompts in the form of text, pictograms, sounds, pop-ups, changes in ambient lighting or via a digital avatar.

[0006] The driver's level of exertion may then, in some cases, be too high in relation to the concentration required for driving at that time.

[0007] For example, some current technologies make it possible to measure a driver's stress level and offer them a relaxing breathing exercise if necessary. If the driver is driving during the day on an open highway, this suggestion is relevant; however, if they are driving at night on a country road in heavy rain, the notification of this suggestion is entirely inappropriate because it adds a source of distraction when total concentration is required. Description of the invention

[0008] The present invention therefore aims to overcome the aforementioned drawbacks and to provide a method for determining the availability level of a real-time driver and to define the services, the signals sent by human-machine interfaces, or even the services that can be offered to him depending on this availability.

[0009] The present invention relates to a method for identifying and evaluating the availability of a vehicle driver to adapt requests to the driver's attention, the method comprising the following steps: - Acquisition of physiological and / or behavioral data relating to the driver and / or data relating to the vehicle and / or environmental data likely to affect the driver's availability to receive requests from the vehicle; - Definition, based on the acquired data, of a set of factors internal to the vehicle and factors external to the vehicle and assignment to each factor of a value depending on the impact of said factor on the availability of the driver; - Calculation of a driver availability index based on all internal factors and all external factors, with a first coefficient applied to all internal factors and a second coefficient applied to all external factors, both depending on the driver's experience and / or accident history; and - Determining the type of request to send to the driver based on the calculated availability index.

[0010] Thus, the present method gives importance to the accumulation of demands and the simultaneous consideration of several factors, including internal and external to the vehicle, makes it possible not to treat each demand individually and to provide an effective method for evaluating the availability of a driver for driving.

[0011] Advantageously, the sum of the first and second coefficients is equal to 1.

[0012] Advantageously, the step of calculating the availability index is implemented so that the availability index is equal to [OOB] ID = IDq x FIj + JJ™}FE^

[0014] with IDq a predetermined initial availability index,

[0015] a and (1-a) the first and second coefficients between 0 and 1,

[0016] n the number of internal factors, m the number of external factors,

[0017] FIj and FEj are the values ​​of the i-th internal factor and respectively external factor, between 0 and 1.

[0018] Advantageously, the data acquisition step is carried out via sensors and / or computers comprising a physiological sensor, and / or a camera directed towards the driver, and / or a camera directed towards the road, and / or a vehicle computer, and / or a weather module, and / or a human-machine interface adapted for data entry by the driver.

[0019] In one embodiment, the value of each internal or external factor is a function of a third coefficient ô depending on the ease of driver distraction, and / or driving style, and / or driving conditions.

[0020] Advantageously, the third coefficient is between 0.05 and 0.45 and each internal or external factor has a value equal to 1 if said factor has no impact on driver availability, or equal to l-ô if said factor has a moderate impact on driver availability, or equal to l-2ô if said factor has a high impact on driver availability.

[0021] In a particular embodiment, the first and / or third coefficient is determined by machine learning, preferably by artificial neural network.

[0022] Advantageously, the internal factors include an internal factor of the driver's emotional state, and / or an internal factor of the driver's attentional state, and / or an internal factor of the driver's alertness state, and / or an internal factor of driving style.

[0023] Advantageously, external factors include an external traffic factor, and / or an external contextual event factor, and / or an external road type factor, and / or an external environmental context factor.

[0024] In a particular embodiment, the type of solicitation determined during the step of determining the type of solicitation to be sent to the driver according to the calculated availability index takes into account the frequency of sending the solicitations, and / or the sensory modality of the solicitation, and / or the density of information transmitted by a solicitation, and / or the area of ​​emission of the solicitation, and / or the reduction or increase of said solicitation by the driving assistance, and / or the type of information transmitted by the solicitation, and / or the implementation of a psychological countermeasure aimed at increasing the availability of the driver.

[0025] Advantageously, the data acquisition step includes the acquisition of the vehicle speed, the method directly including a step of assigning a predetermined value to the availability index when said vehicle speed is zero. Brief description of the drawings

[0026] Other objects, features and advantages of the invention will become apparent from the following description, given solely by way of non-limiting example, and made with reference to the accompanying drawing in which:

[0027] [Fig.1] is a schematic representation of the different steps of the method according to the invention.

[0028] Detailed description of at least one embodiment

[0029] The different stages of a method for identifying and evaluating the availability of a vehicle driver to adapt to requests from the vehicle, for the attention of said driver, are schematically represented in [Fig.1].

[0030] The method is for example implemented in a motor vehicle by a computer of said motor vehicle.

[0031] To implement this method, a first step is performed involving the acquisition of physiological and / or behavioral data relating to the driver and / or data relating to the vehicle and / or environmental factors likely to influence the driver's readiness to receive input from the vehicle. Data acquisition is preferably carried out for several of these datasets, and even more preferably for all of them.

[0032] This step El is implemented through sensors and / or computers including, for example, a physiological sensor, and / or a camera directed towards the driver, and / or a camera directed towards the road, and / or a vehicle computer, and / or a weather module, and / or a human-machine interface adapted for data entry by the driver, said driver being able to enter information on the driving situation or on his own feelings of fatigue or availability.

[0033] Physiological data include, for example, the driver's heartbeats captured by a connected bracelet.

[0034] Behavioral data includes, for example, driver movements captured by a camera, such as slow or rapid gestures indicating fatigue or nervousness, or the movement of closing eyelids. A microphone can also be used to detect a conversation.

[0035] Vehicle data includes, for example, vehicle speed, interior and / or exterior temperature, driving mode, and data concerning the journey.

[0036] Environmental data include, for example, weather data, surrounding traffic data, or the presence of works or hazards.

[0037] Then, a step E2 is carried out to define, from the acquired data, a set of factors internal to the vehicle, noted FI, and of factors external to the vehicle, noted FE, this step E2 also including a step of assigning to each factor FI or FE a value depending on the impact of said factor FI or FE on the availability of the driver.

[0038] Each value of each factor FI or FE is, for example, between 0 and 1, excluding the value 0. The closer the factor value is to 1, the less significant the factor's impact on driver availability. Conversely, the closer the factor value is to 0, the more significant the factor's impact on driver availability.

[0039] Internal factors include, for example, an internal factor relating to the driver's emotional state, indicating anger or, conversely, serenity, and / or an internal factor relating to the driver's attentional state, indicating, for example, an ongoing telephone conversation, and / or an internal factor relating to the driver's alertness, indicating, for example, fatigue or a long journey time, and / or an internal factor relating to driving mode, indicating, for example, a sport mode. Preferably, the internal factors include all of the aforementioned internal factors. Even more preferably, the internal factors include only all of the aforementioned internal factors.

[0040] External factors include, for example, an external traffic factor, indicating, for example, road traffic density, and / or an external contextual event factor, indicating, for example, the presence of a pedestrian crossing, a roundabout, or a toll plaza nearby, and / or an external road type factor, indicating, for example, the presence of a motorway or a country road, and / or an external environmental context factor, indicating, for example, rainy, sunny, or nighttime weather. Preferably, the external factors include all of the aforementioned external factors. Even more preferably, the external factors include only all of the aforementioned external factors.

[0041] We then carry out a step E3 of calculation of a driver availability index as a function of the set of internal factors and the set of external factors, a first coefficient being applied to the set of internal factors, a second coefficient being applied to the set of external factors, the first and second coefficient depending on the experience and / or accident history of the driver.

[0042] The sum of the first and second coefficients is equal to 1.

[0043] Thus, the first coefficient, denoted a, is between 0 and 1 and the second coefficient is equal to (1-a).

[0044] Thus, depending on the value of the first coefficient a, a greater weight can be placed on internal factors or on external factors.

[0045] According to a particular embodiment, step E3 of calculating the availability index, denoted ID, is implemented such that the availability index is equal to

[0046] ID = JDq x + (1 - a) II^FE;)

[0047] with ID0 a predetermined initial availability index, for example equal to 100, n the number of internal factors, and m the number of external factors.

[0048] The first coefficient a is, for example, equal to 0.6. However, since the first coefficient depends on the driver's experience and / or accident history, it can be reduced or increased, for example to 0.8 for an experienced driver. At such a coefficient, external factors become less significant. Conversely, a young driver may be assigned a coefficient of 0.5, based on the assumption that any factor, whether internal or external, could represent a risk. Similarly, a driver who has recently had an accident would have a lower first coefficient a value to emphasize external factors in their availability index.

[0049] In a particular embodiment, the first coefficient is determined by means of a loop based on neural network learning, for example fed by feedback from the driver on his availability in certain situations.

[0050] Advantageously, the value of each internal or external factor is a function of a third coefficient, denoted ô, which depends on the ease of driver distraction, and / or driving style, and / or driving conditions.

[0051] Advantageously, the third coefficient is between 0.05 and 0.45 and each internal or external factor has a value equal to 1 if said factor has no impact on driver availability, or equal to l-ô if said factor has a moderate impact on driver availability, or equal to l-2ô if said factor has a high impact on driver availability.

[0052] Thus, in this mode of implementation, each internal or external factor can only reach three different values ​​depending on the impact on the availability of the driver that said factor can have.

[0053] The third coefficient ô is chosen to be close to 0.05 if the driver is not easily distracted. Conversely, the third coefficient ô is chosen to be close to 0.45 if the driver is easily distracted. For a standard driver, the third coefficient ô is, for example, 0.2.

[0054] The value of the third coefficient ô can be chosen or modified by considering pre-established inter-individual differences, declared by the driver or calculated, for example by neural network learning or clustering.

[0055] For example, the driver may declare that he is not comfortable driving at night. The value of the third coefficient ô will be increased for this driver compared to other drivers who have not declared this.

[0056] Similarly, depending on the detected driving style (jerking, slowing down, trajectory correction, unusual lateral / longitudinal acceleration) which could indicate hesitation or discomfort on the part of the driver, the value of the third coefficient The fare will be increased for this driver compared to other drivers for his journey and future journeys.

[0057] The value of the third coefficient ô can also be modulated by the anticipation of a risk event. For example, during an event such as reduced visibility, a traffic jam, or an obstacle on the road, the value of the third coefficient ô will be inversely proportional to the distance of the event. If the event is close, then the value of the third coefficient ô will be high.

[0058] According to a particular embodiment, the data acquisition step E1 comprises acquiring the vehicle speed V, the method then directly comprising a step E3b of assigning a predetermined value to the availability index when said vehicle speed V is zero. This predetermined value is, for example, the predetermined initial availability index IDG, for example equal to 100, thus indicating total driver availability when the vehicle is stopped.

[0059] Finally, a step E4 is performed to determine the type of request to be sent to the driver according to the calculated availability index. Each type of request corresponds to a value of the availability index.

[0060] Indeed, during driving, the driver receives requests from the vehicle, these requests being modulated during this E4 stage according to the value of the availability index.

[0061] In particular, the term "type of solicitation" means all the parameters of the solicitations that can affect the availability of the driver, including in particular the frequency of sending the solicitations, and / or the sensory modality of the solicitation, whether visual, auditory, or haptic, and / or the density of information transmitted by a solicitation, and / or the area of ​​emission of the solicitation, for example the dashboard or the head-up display, and / or the reduction or increase of said solicitation by driving assistance, and / or the type of information transmitted by the solicitation, and / or the implementation of a psychological countermeasure aimed at increasing the availability of the driver.

[0062] Driving assistance is for example lateral and / or longitudinal assistance and / or vehicle speed regulation, allowing the sum of all stresses to be minimized.

[0063] A psychological countermeasure is, for example, starting up a playlist of relaxing music aimed at reducing the emotional intensity of the driver and thus increasing his availability index.

[0064] In a particular embodiment, the value of the availability index is a value between 0 and 100.

[0065] Driver availability is classified according to four levels. The first level corresponds to an availability index of less than 25.

[0066] The second level is equivalent to an availability index between 25 and 50.

[0067] The third level is equivalent to an availability index between 50 and 75.

[0068] The fourth level is equivalent to an availability index greater than 75.

[0069] For a calculated availability index below 25, the vehicle's computer determines, during step E4, the lowest level of interaction and input from the driver. Consequently, the sensory input methods, the frequency of information display, the display areas, and the information density transmitted to the driver must be reduced to the bare minimum. Furthermore, the level of driver assistance will be enhanced to ensure driver safety. The services offered will be significantly reduced. Finally, an appropriate countermeasure may be implemented to raise the availability index to a level suitable for driving, for example, above 75.

[0070] For example, for a calculated availability index of less than 25, driver input will be haptic or audible only, the frequency of input will be reduced to the maximum, for example to one input every five minutes, the display area will be the head-up display or the instrument panel, the density of information displayed will be limited to a few words, for example less than 5 words, driving assistance will be high, the radio and weather will not be available and a stop suggestion will be offered as a countermeasure.

[0071] Conversely, for example, for a calculated availability index greater than 75, the driver may be prompted by visual and audible cues, the frequency of cues may be high, for example one cue every ten seconds, the display area will be the head-up display and the instrument panel as well as any other screen and indicator, the density of information displayed may be high, the driving assistance may be deactivated, and the radio and weather information will be available.

[0072] For intermediate availability levels, requests are also adjusted.

[0073] In order to ensure an up-to-date availability index, the present method is implemented regularly, for example at a frequency between every five seconds and every five minutes.

Claims

Demands

1. Method for identifying and evaluating a vehicle driver's readiness to adapt stimuli to the driver's attention, characterized in that it comprises the following steps: - Acquisition (step E1) of physiological and / or behavioral data relating to the driver and / or data relating to the vehicle and / or environmental factors likely to influence the driver's readiness to receive stimuli from the vehicle; - Definition (step E2), from the acquired data, of a set of factors internal to the vehicle and factors external to the vehicle and assignment to each factor of a value depending on the impact of said factor on the driver's readiness;- Calculation (step E3) of a driver availability index based on the set of internal factors and the set of external factors, a first coefficient being applied to the set of internal factors, a second coefficient being applied to the set of external factors, the first and second coefficients depending on the driver's experience and / or accident history; and - Determination (step E4) of the type of request to send to the driver according to the calculated availability index.

2. Method according to claim 1, wherein the sum of the first and second coefficients is equal to 1.

3. A method according to any one of claims 1 and 2, wherein the step (E3) of calculating the availability index is implemented such that the availability index is equal to with IDg a predetermined initial availability index, a and (1-a) the first and second coefficients between 0 and 1, n the number of internal factors, m the number of external factors, FI / and FEt are the values ​​of the i-th internal factor and respectively external factor, between 0 and 1.

4. A method according to any one of claims 1 to 3, wherein the data acquisition step (El) is carried out via sensors and / or computers comprising a physiological sensor, and / or a driver-facing camera, and / or a road-facing camera, and / or a vehicle computer, and / or a weather module, and / or a human-machine interface adapted for data input by the driver.

5. Method according to any one of claims 1 to 4, wherein the value of each internal or external factor is a function of a third coefficient (ô) depending on the ease of driver distraction, and / or driving style, and / or driving conditions.

6. Method according to claim 5, wherein the third coefficient is between 0.05 and 0.45 and each internal or external factor has a value equal to 1 if said factor has no impact on driver availability, or equal to l-ô if said factor has a moderate impact on driver availability, or equal to l-2ô if said factor has a high impact on driver availability.

7. A method according to any one of claims 5 and 6, wherein the first and / or third coefficient is determined by machine learning, preferably by artificial neural network.

8. Method according to any one of claims 1 to 7, wherein the internal factors include an internal factor of the driver's emotional state, and / or an internal factor of the driver's attentional state, and / or an internal factor of the driver's alertness state, and / or an internal factor of driving style, and / or wherein the external factors include an external factor of traffic, and / or an external factor of contextual event, and / or an external factor of road type, and / or an external factor of environmental context.

9. A method according to any one of claims 1 to 8, wherein the type of stimulus determined in step (E4) of determining the type of stimulus to be sent to the driver according to the calculated availability index takes into account the frequency of sending the stimuli, and / or the sensory modality of the stimulus, and / or the density of information transmitted by a solicitation, and / or the area from which the solicitation is emitted, and / or the reduction or increase of said solicitation by the driver assistance system, and / or the type of information transmitted by the solicitation, and / or the implementation of a psychological countermeasure aimed at increasing the driver's availability

10. Method according to any one of claims 1 to 9, wherein the data acquisition step (E1) includes the acquisition of the vehicle speed, the method directly comprising a step (E3b) of assigning to the availability index a predetermined value when said vehicle speed is zero.

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