Traveling route calculation device and traveling route calculation method for automobile
Patent Information
- Authority / Receiving Office
- JP · JP
- Patent Type
- Applications
- Current Assignee / Owner
- FERRARI SPA
- Filing Date
- 2023-06-07
- Publication Date
- 2026-06-09
AI Technical Summary
Existing navigation systems primarily focus on optimizing travel routes based on distance or time, neglecting the driver's enjoyment and pleasure, which is particularly important in sports cars, without excessive energy consumption or time loss.
A driving route calculation method that incorporates a 'fun index' to optimize routes based on parameters such as lateral acceleration, forward acceleration, gradient variation, deceleration, urban area presence, and population density, ensuring a enjoyable driving experience while considering energy efficiency and time.
The method provides a route that balances enjoyment with efficiency, offering an alternative route that enhances driving pleasure without significant increases in time or energy consumption, while ensuring the vehicle has sufficient fuel to complete the journey without stops.
Smart Images
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Abstract
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This patent application claims priority to Italian Patent Application No. 102022000012131, filed June 8, 2022, the entire disclosure of which is incorporated herein by reference. [Technical Field]
[0002] The present invention relates to a driving route calculation device and a driving route calculation method for an automobile. [Background technology]
[0003] As is known, there are many solutions on the market for satellite navigation systems for automobiles, based on devices installed in the automobile or on navigation programs installed on mobile devices such as mobile phones.
[0004] Navigation programs are typically configured to calculate a route that a vehicle can travel between two points, ie, a starting point and an arrival or destination point.
[0005] Typically, the driving route is calculated in a way that is optimized for parameters such as the total length of the route or the expected travel time of the route.
[0006] In other words, the navigation program can calculate, for example, the shortest driving route in terms of distance, or the shortest driving route in terms of time.
[0007] Typically, the journey time is predicted as a function of the vehicle's average expected speed along the route, and possibly as a function of additional information, including, for example, information about road traffic conditions, the vehicle's remaining fuel, legal speed limits, the presence of road maintenance work, the presence of mandatory detours, etc.
[0008] Generally, satellite navigation systems can obtain this additional information by connecting to specific servers or databases that contain information according to standard communication protocols, such as the ADASIS (Advanced Drivers Assistant System Interface Specifications) protocol.
[0009] To calculate the route, the navigation program uses a stored map containing start and end points. This map can be retrieved from a specific server or updated with information contained in the server. The communication between such a server and the navigation system in which the navigation program is installed can also be carried out according to a standard communication protocol, such as the ADASIS protocol.
[0010] The map is treated as a graph consisting of a number of nodes or vertices representing intersections between roads, and a number of edges or sides connecting the nodes and each representing a road between the intersections.
[0011] The navigation program associates with each edge a corresponding weight, which is an increasing scalar function of one or more optimization parameters, including the edge's estimated distance or estimated travel time.
[0012] The route is then calculated as the route along the graph that corresponds to the minimum sum of the weights of the edges that belong to the route. This route is usually called the shortest route between the starting point and the destination point.
[0013] Therefore, calculating a driving route corresponds to minimizing a cost function or an objective function that corresponds to the sum of the weights of the target edges on the graph.
[0014] This calculation can be performed according to any one of several well-known algorithms, such as, for example, the Dijkstra algorithm, or the A* algorithm, or an algorithm based on a contraction hierarchy.
[0015] The calculated driving route is displayed to the user, specifically the driver of the vehicle, via the screen of the satellite navigation system.
[0016] In some cases, the navigation program also provides suboptimal alternative driving routes that are presented to the user via a screen. For example, alternative driving routes can be determined by removing the optimal driving route from the graph and then re-running the cost function minimization.
[0017] Typically, a user can select a preferred driving route from those calculated by the navigation program and follow that route by driving the vehicle accordingly.
[0018] In this way, satellite navigation systems and navigation programs have a decisive influence on the actual route taken by the vehicle and therefore on energy consumption, the time it takes to reach the destination and the user's driving pleasure.
[0019] In reality, the different routes traveled by a vehicle correspond to different stresses on the vehicle's suspension or brakes, different lateral accelerations, different traffic or road conditions, different altitude differences traversed, etc.
[0020] For example, a route with multiple curves will take longer than a route with multiple straight roads, but the driver will find it more enjoyable due to the lateral acceleration around the curves, resulting in benefits in the pleasure of driving the car.
[0021] In general, or more specifically in the field of sports cars, there is a need to increase the driving pleasure of the driver, preferably without excessive energy waste and / or loss of time for the driver.
[0022] The object of the present invention is to meet the above requirements, preferably in a simple and reliable manner. Summary of the Invention
[0023] This object is achieved by a method for calculating a driving path of a motor vehicle and a device for calculating a driving path as defined in the independent claims.
[0024] Each dependent claim defines a particular embodiment of the invention. [Brief explanation of the drawings]
[0025] The following description, given by way of non-limiting example and with reference to the accompanying drawings, provides a better understanding of embodiments of the invention.
[0026] [Figure 1] 1 is a perspective view of a passenger compartment of an automobile equipped with a driving route calculation device according to the present invention. [Figure 2] FIG. 1 is a schematic diagram of a road map in graphical form. DETAILED DESCRIPTION OF THE INVENTION
[0027] In FIG. 1, the reference number 1 is used to indicate a motor vehicle as a whole.
[0028] The motor vehicle 1 has a passenger compartment 2 for accommodating a driver and one or more passengers.
[0029] Preferably, the car 1 is hybrid or electric, rechargeable by connecting to a power outlet, but this is a non-limiting example, as the car 1 can have any type of fuel, such as a common fossil fuel, or more precisely, a petroleum-based fuel.
[0030] Within the passenger compartment 2, the motor vehicle 1 comprises a dashboard or instrument panel 3 and a computing device 4, in particular an on-board computer. The computing device 4 is fixedly mounted on the dashboard 3, in particular in a position visible to the driver, and more particularly to one of the passengers sitting next to the driver.
[0031] This is not essential, as the device 4 can also be a mobile device, for example a tablet or a mobile phone.
[0032] The device 4 comprises a display 5, e.g., a touchscreen, configured to depict a road map 6. Furthermore, the device 4 comprises a number of command devices 7, e.g., in the form of physical buttons or virtual buttons depicted on the screen 5, configured to allow the driver or passengers to input various types of data or information into the device 4. The command devices 7 may also include voice commands or various types of mechanisms such as levers, rods, slide handles, etc., as well as a virtual keyboard.
[0033] The device 4 is, for example, part of or a satellite navigation system for assisting a driver in driving the vehicle 1 through locations represented by the map 6. The navigation system includes a GPS receiver connected to the device 4 so that the device 4 can receive GPS signals and locate the vehicle 1 on the map 6 based on the GPS signals.
[0034] The device 4 comprises a data processing unit 8 configured to calculate a driving route that can be followed by the vehicle 1. The unit 8 is adapted to calculate the driving route on the map 6.
[0035] 1, screen 5 specifically shows, at least approximately, a primary route P1 and an alternative route P2 for reaching a destination of interest starting from a starting point. Without loss of generality, this starting point may correspond to the current position of vehicle 1 on map 6, or may correspond to a choice or preference of the driver. The destination or arrival point may correspond to a choice or preference of the driver, or may be determined by device 4 arbitrarily or based on various criteria.
[0036] The paths P1, P2 are calculated according to various criteria, as will become clearer below, and the unit 8 and the screen 5 can actually calculate and show paths other than the paths P1, P2, as will become clearer below.
[0037] In general, the unit 8 is configured to obtain information corresponding to an origin and a destination.
[0038] For example, the unit 8 can obtain information about the starting point from a GPS signal, if the starting point coincides with the current position of the vehicle 1 or is based on the current position itself. Alternatively, the information obtained about the starting point can correspond to an input from the driver via the command device 7, or data from another electronic device, or data stored in the unit 8, for example data from a virtual log. Similarly, for example, information about the destination can correspond to an input from the driver via the command device 7, or data stored in the unit 8, for example data from a virtual log, or data from another electronic device.
[0039] The map 6 is calculated by unit 8 to include a starting point S and a destination point Y associated with the origin and destination respectively.
[0040] In particular, unit 8 may store a number of maps, each relating to a different geographical region of the world. Unit 8 may therefore select a map 6, which includes a starting point S and a destination point Y. Alternatively or additionally, unit 8 may obtain and possibly store one or more maps, including map 6, which includes starting point S and destination point Y, from an external source to which device 4 is connected, for example via a wireless connection or more precisely via the Internet, in particular according to a standardized protocol such as the ADASIS protocol. Alternatively or additionally, the stored map may be updated by unit 8, possibly in real time, using information obtained from the external source.
[0041] Hereinafter, so-called external sources may include data clouds, servers or public or private databases accessible to device 4, for example via a wireless or Bluetooth connection, as well as the Internet network and devices readable by device 4, such as USB sticks.
[0042] Generally, the starting point S and the arrival point Y can be the same as the starting point and the destination, respectively, but this is not required. In practice, for example, the map 6 may not contain the exact starting point or destination. In this case, the starting point S and the arrival point Y can be the points belonging to the map 6 that are closest to the starting point and the destination, respectively.
[0043] Although this is not essential, the map 6 is preferably processed or stored by the unit 8 as a graph, for example like the exemplary one in FIG.
[0044] Specifically, the graph in Figure 2 represents a map 6 and includes multiple nodes N and multiple edges or links M. Each node N represents an intersection between roads or a specific point on a road. Each edge M connects two corresponding nodes N and represents the road section between the two corresponding nodes N.
[0045] The starting point S and the destination point Y correspond to two nodes N on the map 6, respectively.
[0046] The unit 8 calculates a route P2, preferably a route P1, on the map 6 between a starting point S and a destination point Y. In other words, the unit 8 calculates the routes P1, P2 subject to the constraint that the starting point S and the destination point Y are the start and end points of the routes P1, P2, respectively.
[0047] In the particular embodiment of FIG. 2, paths P1, P2 are each individually defined by a particular sequence of edges M.
[0048] In this sense, each edge M can be thought of as a Boolean variable that takes a first value when edge M belongs to one of paths P1, P2, and a second value different from the first value when edge M does not belong to one of paths P1, P2.
[0049] The unit 8 is configured to calculate a path P2 on the map 6 from a start point S to a destination point Y by solving a path planning problem involving the optimization of an objective function according to scalar parameters defined by a fan index, where the term optimization refers in the mathematical sense to the minimization or maximization (relative or absolute) of an objective function.
[0050] When the objective function is optimized, it takes an optimal value, i.e., a minimum or maximum value. This value depends on the fan index. This optimal value corresponds to the solution of the path planning problem.
[0051] The Fun Index indicates the level of enjoyment of a route. Specifically, the Fun Index increases in accordance with the level of enjoyment experienced by the driver, but this is not required. In fact, since the Fun Index is a normal indicator, it may decrease in accordance with the level of enjoyment as normal, but still indicate the level of enjoyment.
[0052] The more the Fun Index deviates from the target level, or indicates a decrease in enjoyment, the more the objective function deviates from the optimum.
[0053] The level of enjoyment can generally be understood to increase as the driver experiences greater enjoyment and satisfaction if the route requires more concentration on driving the vehicle 1 or more technical ability, and / or if the route crosses open scenic areas, and / or if the route avoids slowdowns due to, for example, traffic jams, traffic lights, and other conditions that the driver generally perceives as tedious.
[0054] So the level of enjoyment is - a first quantity that is positively correlated with the expected lateral acceleration of the vehicle along the path P2; a second quantity that is positively correlated with the expected forward acceleration of the vehicle along the path P2; - a third quantity that is positively correlated with the expected gradient variation along path P2, a fourth quantity negatively correlated with the expected deceleration of the vehicle along the path P2; - A fifth quantity that is negatively correlated with the expected urban or central travel along route P2. It increases with one or more of the following:
[0055] More precisely, the first quantity may be a lateral acceleration, or a number A of curves with a lateral acceleration, higher than a first threshold value, for example defined by the unit 8 and possibly based on information entered by the driver via the command device 7, or information obtained, for example, from an external source. The prediction of the lateral acceleration may be made based on the shape of the path P2 and the habits of the driver, or based on a speed limit, for example stored in the unit 8 or obtained from an external source or entered by the driver via the command device 7.
[0056] Lateral acceleration indicates the amount and gentleness of the curve. Indeed, more curves and / or gentler curves increase the driver's driving enjoyment or pleasure.
[0057] Furthermore, the second quantity can be a numerical value B of the forward acceleration higher than a second threshold, for example defined by the unit 8 and possibly input by the driver via the command device 7 or based on information obtained, for example, from an external source. The prediction of the forward acceleration can be made based on the shape of the path P2 and the habits of the driver or based on a speed limit, for example stored in the unit 8 or obtained from an external source or input by the driver via the command device 7.
[0058] Forward acceleration indicates the presence of a straight road after a speed reduction due to a curve, for example, which typically increases the driver's driving pleasure or enjoyment.
[0059] Furthermore, the third amount may be a numerical value C of uphill and / or downhill slopes having a relative gradient higher than a third threshold, for example expressed as a percentage and / or defined by the unit 8 and possibly based on information entered by the driver via the command device 7 or information obtained, for example, from an external source. Based on the structure of the route P2, a prediction of the gradient variation may be made.
[0060] A gradient change may indicate, for example, an open scenic route, which typically increases the driver's driving pleasure or enjoyment.
[0061] Furthermore, the fourth amount can be a deceleration or braking value D below a fourth threshold, for example defined by the unit 8 and possibly based on information entered by the driver via the command device 7, or on other criteria, for example obtained from an external source. The prediction of the deceleration can be made based on the shape of the path P2 and the habits of the driver, or based on a speed limit, for example stored in the unit 8 or obtained from an external source or entered by the driver via the command device 7.
[0062] The presence of sudden deceleration is, for example, an indicator of the difficulty of a curve, which has a negative impact on the driver's driving pleasure or enjoyment.
[0063] Furthermore, the fifth quantity may be a value E of the crowded population centre, for example, information about the value E being obtained from an external source.
[0064] Clearly, population centers have a negative impact on driving pleasure or driver enjoyment, as they usually involve slower speeds, traffic volumes and the presence of traffic lights.
[0065] Preferably, the values A, B, C, D, E are stored by unit 8. Similarly, each threshold value can be stored separately in unit 8. Each threshold value can be selected arbitrarily or, for example, based on general properties, identified, for example, through research.
[0066] Each of the first to fifth quantities may be obtained or determined by unit 8 based on receiving signals or information from transducers of vehicle 1, e.g., regarding acceleration and deceleration, and / or from external sources, e.g., regarding gradient variations, population centers, the structure of route P2, driver habits. Alternatively, or in addition, each of the first to fifth quantities may be obtained or determined by unit 8 based on relative data stored in unit 8, e.g., regarding gradient variations, population centers, the structure of route P2, driver habits.
[0067] Unit 8 may be configured to calculate a Fan Index based on one or more of the obtained quantities, or may determine the Fan Index from the first to fifth quantities. Alternatively, unit 8 may obtain or determine the Fan Index associated with each edge M, or the entire path P2, directly, for example from an external source or from data stored in unit 8.
[0068] In particular, the Fan Index can be calculated as a combination of all or part of the first through fifth quantities, more specifically, linearly.
[0069] For example, the Fan Index can be calculated using the following formula:
number
[0070] The Fan Index calculation, ie, this formula, can be normalized to the distance of the path P2, for example, expressed in kilometers.
[0071] A Fan Index may be calculated for each edge M, or may be calculated for the entire path P2, for example, as the sum, possibly weighted, of the Fan Indexes calculated for each edge M. The Fan Index for each edge M may be thought of as an intermediate Fan Index, distinct from the overall Fan Index associated with the entire path P2.
[0072] More specifically, the objective function has one or more optimization variables whose values are suitable for determining path P2 and correspond to at least one value of the fan index in the objective function.
[0073] The path planning problem is solved with optimal values of each of the optimization variables corresponding to at least one optimal value of the fan index corresponding to path P2 and optimizing the objective function for path P2.
[0074] According to a first embodiment, each edge M of the graph is associated with a weight, ie corresponds to a weight as a function of the fan index.
[0075] Each edge M in the graph defines an optimization variable of the objective function, which can be thought of as the Boolean variables mentioned above. Thus, the path P2 is determined in a unique way by the set of values assigned to all the Boolean variables, specifically, by properly ordering them to form a sequence.
[0076] The objective function can be the sum of weights, e.g., the sum of weighted edges M belonging to path P2. In this way, the value of the objective function is a function of the fan index, since each weight is a function of the fan index, specifically the fan index calculated for a single edge M. This intermediate fan index function is a property of the particular path P2, determined by the values of the optimization variables.
[0077] In practice, furthermore, one or more of the Fan Index values in the objective function, e.g., intermediate Fan Index values calculated for each edge M, correspond to the path P2 determined by the values of the optimization variables and therefore correspond to intermediate Fan Index values.
[0078] Therefore, the optimal value of the optimization variable corresponds to the optimal path P2 and at the same time corresponds to the optimal value of the fan index, for example the intermediate fan index value calculated for edge M of the optimal path P2, thereby also optimizing the value of the objective function.
[0079] More precisely, according to a simple variant of the first embodiment, the weight of each edge M corresponds to the intermediate Fan Index calculated for the corresponding edge M. Preferably, the objective function then comprises the sum of the intermediate Fan Indices, whether weighted or not. If this sum is unweighted, i.e., if the sum is a simple calculation, the objective function comprises the overall Fan Index for the entire path P2. In this way, the value of the optimization variable corresponds to a single value of the Fan Index, or more precisely, to a single value of the overall Fan Index.
[0080] In this variant, the optimization of the objective function corresponds to the maximization of the objective function itself. Obviously, this is not necessary. In practice, the weights could correspond to the inverse of the intermediate fan indices. In this case, the optimization of the objective function would correspond to the minimization of that objective function, where we define the cost function.
[0081] For example, if the objective function is defined by the overall Fan Index, the objective function deviates from its optimal value, i.e., its maximum value, when the overall Fan Index decreases, i.e., when the Fan Index indicates a decrease in the fun level. However, this is a non-limiting example. In fact, the objective function can include a penalty contribution in addition to the overall Fan Index, which decreases the value of the objective function when the overall Fan Index deviates from a target value corresponding to the target fun level. For example, the penalty contribution can be a non-zero, negative function that is non-zero only when the value of the overall Fan Index is outside the target range. In this way, the search for a path P2 that guarantees the target fun level can be realized. Obviously, this particular example represents a general idea that can be applied to any variant of the first example, namely, the idea of adding an appropriately designed penalty contribution to the objective function to obtain a path P2 having the target fun level. More precisely, the penalty contribution penalizes the objective function when the Fan Index, or one or more of its values, are outside a predetermined target range.
[0082] Obviously, whether the optimization corresponds to minimizing or maximizing the objective function also depends on the calculation or formulation of the Fan Index values themselves, and on how the weights are functions of their values or their values. Following this explanation, a person skilled in the art can independently identify multiple variations according to the optimization objective, which are based on the Fan Index, with the aim of maximizing the fun level or reaching a target fun level.
[0083] According to a further variant of the first embodiment, the objective function further depends on at least one further parameter indicative of the expected duration, distance or remaining amount of fuel used by the route P2.
[0084] In fact, the objective function may depend on several additional parameters, each of which may individually indicate the expected travel time, distance or remaining fuel used by route P2.
[0085] If the or each additional parameter indicates an increase in the expected travel time, distance, or amount of remaining fuel used by route P2, the objective function will deviate from its optimum.
[0086] For example, each weight of an edge M may be a particularly linear combination of the value of the further parameter of the corresponding edge M and the inverse of the value of the intermediate fan index of the edge M, or alternatively may be a particularly linear combination of the inverse of the value of the further parameter and the value of the intermediate fan index. In the two alternative cases, the optimization corresponds to minimizing and maximizing the objective function, respectively.
[0087] In particular, this combination, and more particularly the linear combination, can be extended to all additional parameters.
[0088] According to a second embodiment, the unit 8 is arranged to calculate a number of alternative candidate routes according to known methodologies.
[0089] The values of the optimization variables are therefore the candidate paths themselves, and indeed each of the candidate paths corresponds to at least one value of the Fan Index, in particular the value of the overall Fan Index of the candidate path.
[0090] The objective function may be a function of the value of the optimization variable, i.e., the overall fan index, corresponding to one of the candidate paths.
[0091] In particular, the objective function may also correspond to the overall Fan Index corresponding to the value of the optimization variable, or may be a function of the overall Fan Index corresponding to the value of the optimization variable and one or more of the aforementioned additional parameters, in particular a linear combination.
[0092] On the other hand, route P1 is calculated by unit 8 differently from route P2. For example, route P1 is calculated according to known optimization methods, or more precisely, by minimizing a cost function with respect to one or more of the following parameters: expected travel time, distance, and remaining fuel used. In other words, route P1 is an optimal route according to general criteria, which does not include or take into account fan indexes.
[0093] In particular, path P1 therefore represents a shortest path according to the common technical meaning of the term in the field of pathfinding.
[0094] In other words, the unit 8 is configured to calculate a path P1 as the shortest path on the map 6 from the starting point S to the destination point Y as a solution to the minimization of a cost function, which includes one or more cost parameters that can be assigned to the shortest path.
[0095] Each cost parameter may be defined, for example, by the time required, the distance, the amount of fuel remaining used, or a linear or non-linear combination of two or more of these.
[0096] Each cost parameter can obviously be assigned to route P2, which in fact also has a specific duration, distance and includes the remaining amount of fuel used.
[0097] In some cases, unit 8 may calculate many other driving paths in addition to paths P1 and P2. Specifically, unit 8 may calculate multiple sub-optimal alternative paths for path P2 based on the fan index, for example, according to a process similar to the process for calculating path P2, where path P2 is specifically excluded, for example, removed from the graph or optimization variables, according to the first and second embodiments described above, respectively.
[0098] Preferably, unit 8 is configured to determine the current fuel level of vehicle 1. For example, the fuel level corresponds to an estimate of the number of kilometers that vehicle 1 can cover. In particular, the fuel level may correspond to the remaining charge in the battery of vehicle 1, or the amount of fuel contained in the tank of vehicle 1, etc.
[0099] The unit 8 is configured to calculate the route P2 only if the determined current remaining fuel level is insufficient to cover the route P1, more precisely only if the current remaining fuel level is insufficient to cover the route P1 without a stop to raise the level of the current remaining fuel, e.g. a stop at a charging station or a gas station. Alternatively, the unit 8 can calculate the route P2 only if the determined current remaining fuel level is insufficient to cover the route P1, and provide an output to the driver, e.g. via the screen 5.
[0100] Indeed, the need to stop along route P1 makes it less convenient in terms of typical travel time, so the opportunity to complete the more enjoyable route P2 becomes attractive, especially if route P2 does not require stopping.
[0101] Furthermore, the unit 8 is preferably configured to estimate whether the current fuel reserve is sufficient to cover the route P2.
[0102] If possible, the unit 8 can output route P2 to the driver, for example, in particular via the screen 5, especially if the current fuel reserve is insufficient to cover route P1.
[0103] If this is not possible, or more precisely only if this is not possible, apart from if this is possible, the unit 8 is preferably configured to determine whether there is at least one point, for example a node N, of the path P2 with a movement amount, for example in terms of time or space, that is less than a predetermined threshold, to a refueling station on the map 6 for refueling.
[0104] This predetermined threshold value can be determined arbitrarily by the unit 8, for example on the basis of suitable criteria, or can be set by the driver, for example via the command device 7.
[0105] For clarity, the term displacement should be interpreted in its broadest sense, including in particular the time required to reach the station from a point on the path P2 and the length of the deviation required to reach the station from a point on the path P2.
[0106] The unit 8 is advantageously configured to output the route P2 only if the determination result is positive, i.e., in particular if the travel distance to the refueling station is less than a relevant predetermined threshold. In fact, the reverse is also true: route P2 will be highly inconvenient relative to route P1. In other words, the added enjoyment of route P2 will be completely lost due to the problem of the route deviating excessively to reach the refueling station.
[0107] The refueling station may be, for example, a charging station or a gas station.
[0108] As can be inferred from the above, providing the calculated path P2 is entirely optional and not mandatory, and in any case, the unit 8 may also provide the path P2 under all circumstances.
[0109] Preferably, the unit 8 is configured to assign a first value and a second value of the aforementioned cost parameter to the path P2 and the path P1, respectively, so that the paths P1 and P2 can be compared according to the same criteria.
[0110] Specifically, unit 8 outputs path P2 only if the first value satisfies a predetermined relationship with the second value, the relationship being such that path P2 is not unduly unfavorable relative to path P1.
[0111] For example, if the second value is smaller than the first value increased by a mathematical operation as a function of a predetermined coefficient, then this relationship is satisfied, e.g., by multiplying the first value by the coefficient or by adding the coefficient to the first value.
[0112] This factor can be determined arbitrarily by the unit 8, for example on the basis of suitable criteria, or can be set by the driver, for example via the command device 7.
[0113] If the cost parameter is, for example, the travel time of the routes P1, P2, the unit 8 can check whether the route P2 has a travel time that is less than the travel time of route P1 by a certain percentage, for example 20% more than the travel time of route P1. Here, the first value can be increased by multiplying it by 1.2 or by adding the first value multiplied by 0.2. Obviously, similar reasoning can be applied in general, especially when the cost parameter is, for example, the distance or the remaining fuel of the route used, or a combination thereof.
[0114] Alternatively or additionally, unit 8 may impose constraints on the optimization of the objective function, for example, a constraint that establishes that a first value satisfies a predetermined relationship with a second value, precisely at the step of calculating path P2.
[0115] In particular, in the first embodiment, this constraint can be enforced or satisfied by removing paths from the graph where the predetermined relationship is not satisfied. Similarly, in the second embodiment, this constraint can be enforced or satisfied by discarding paths where the predetermined relationship is not satisfied.
[0116] Preferably, for the calculated path P2, optionally provided at the output, unit 8 calculates the value of the overall fan index, which is the optimum value since it results directly from the optimization of the objective function, the result of which is the calculated path P2.
[0117] Advantageously, the unit 8 is configured to associate the optimum value of the fan index or the calculated path P2 with a category of a plurality of fan categories, each fan category corresponding to a respective value or range of the fan index.
[0118] For example, fan categories may be specifically represented by virtual labels or identifications, specifically defined by numbers or character strings such as "boring," "panoramic," "fun," and "exhilarating."
[0119] In other words, the unit 8 assigns the corresponding category to the optimum value and therefore to the path P2.
[0120] In particular, unit 8 stores path P2 in association with or together with each associated category.
[0121] In this way, the unit 8 can for example provide the driver, in particular via the screen 5, with information items relating to the associations or correspondences between the route P2 and the respective associated categories.
[0122] Generally, the information that may be provided by unit 8 may be provided to the driver or passengers in many ways, for example by sounds, symbols, letters, images, etc. emitted by appropriate parts of device 4.
[0123] By storing the route P2 between the starting point S and the destination point Y, the unit 8 does not have to recalculate the same route P2 each time the driver inputs information about the points S, Y. Instead, the unit 8 is already ready to provide the route P2, particularly in relation to the relevant category, which can be communicated to the driver.
[0124] For each or some of the categories, unit 8 can calculate an optimal driving route based on the fan index, for example in a similar way to the calculation of route P2, based on the fan category, by setting the objective function accordingly so that the objective function deviates from its respective optimum value when the fan index indicates that the level of enjoyment deviates from the target level corresponding to the fan category.
[0125] In particular, the objective function deviates from the optimum when the overall fan index value deviates from the respective value or range corresponding to the relevant fan category. This range can therefore be considered a target range. This can be set, for example, by a penalty contribution, as previously described herein.
[0126] More precisely, when the fan index, especially the overall fan index, is outside the target range, as opposed to when the fan index takes any value within the target range, the objective functions deviate more, especially more significantly, from their optimal values.
[0127] This concept is generally applicable, ie, whenever a target range is defined for a fan index, regardless of fan category.
[0128] Alternatively, even if unit 8 has calculated multiple alternative routes based on known methods, unit 8 may re-categorize the alternative routes based on the fan index by assigning each route to a fan category. In particular, an already calculated route may be associated with a fan category if the overall fan index value falls within or falls within a range of values for that particular category.
[0129] Preferably, the unit 8 is configured to obtain the driver's feedback about the route P2 regarding the level of enjoyment or fun category of the route P2.
[0130] The unit 8 is also configured to re-associate the path P2 with the re-associated category of the fan categories based on the obtained feedback, and to store the path P2 with respect to the re-associated category.
[0131] For example, if the driver later considers that the path P2 followed belongs to or can be associated with a different fan category than the one automatically associated by unit 8, the driver can input feedback, in particular via the command device 7, indicating for example the reassociated category. In this way, unit 8 takes the driver's feedback into account by reassociating path P2 with the reassociated category selected by the driver.
[0132] From the above, the device 4 or unit 8 executes a method, in particular implemented on a computer, to calculate the path P2, which method comprises: a. obtaining information corresponding to the origin and destination of a vehicle; b. determining a map 6 including a starting point S and an ending point Y of a route P2 associated with the origin and destination, respectively; c. calculating a path P2 on the map 6 from a starting point S to a destination point Y by solving a path planning problem including optimizing an objective function that depends on at least one first scalar parameter defined by a fan index; d. Providing the calculated driving route; Includes.
[0133] Step d. is entirely optional, as can be derived from the operation of unit 8. In other words, the actual execution of step d. depends on the fulfillment of one or more conditions.
[0134] Furthermore, the method preferably comprises: e. A step of calculating the shortest route on the map 6 from the starting point S to the destination point Y as a solution to the minimization of a cost function including a cost parameter that can be assigned to the shortest route and the route P2, wherein the cost parameter is different from the fan index and is defined, for example, by a linear combination of the required time, distance, remaining fuel used, or two or all of the required time, distance, and remaining fuel used; f. determining the current fuel level of vehicle 1; It includes one or more of the following.
[0135] Also, according to one embodiment, the method comprises: g. Further comprising the step of estimating whether the current fuel level is sufficient to complete the shortest route. Here, step c. or step d. is performed if step g. has a negative result, or more precisely, step c. or step d. is performed only if step g. has a negative result.
[0136] Alternatively, or in addition, the method may further comprise: h. Estimating whether the current remaining fuel level is sufficient to complete the travel route P2; i. if step g. has a negative result, or only if step g. has a negative result, determining whether there is at least one point on the path P2 with a movement amount, e.g. in terms of time or space, less than a predetermined threshold to a refueling station on the map 6; Further includes: Here, step d. is executed if, or more precisely, only if, step i. has a positive result.
[0137] Alternatively, or in addition, the method further comprises: j. assigning a first value of a cost parameter to the travel path P2; k. assigning a second value of the cost parameter to the shortest path; and preferably l. determining whether the first value satisfies a predetermined relationship with the second value; Includes. Here, step d. is executed if, or more precisely, only if, step l. has a positive result.
[0138] In addition, the method further comprises: m. Obtaining driver feedback on the route P2 regarding the level of enjoyment of the route P2; n. Reassociating the path P2 with the reassociated category of fan categories based on the obtained feedback; o. Associating and storing a path P2 with the reassociated category; Includes.
[0139] From the above, the advantages of the device 4 and method according to the present invention are clear.
[0140] Calculating route P2 provides the driver with a valid alternative to the typical shortest route based on certain criteria of driving pleasure.
[0141] Calculating route P2 takes into account the advantages of route P2 in terms of distance and travel time, thus achieving a good compromise between enjoyment and efficiency.
[0142] In addition, unit 8 will only suggest route P2 to the driver if the difference between P2 and the shortest route is small, for example because even the shortest route requires stops to increase the fuel level of vehicle 1.
[0143] Finally, it is clear that modifications and variations can be made to the device 4 and the method according to the invention without departing from the scope of protection defined by the claims.
Claims
1. A computer-implemented method for calculating the driving path (P2) that an automobile (1) should follow, a. A step of obtaining information corresponding to the departure point and destination of the vehicle, b. A step of determining a road map (6) that includes the starting point (S) and the arrival point (Y) of the travel route (P2) associated with the departure point and the destination, respectively, c. A step of calculating the travel route (P2) on the road map (6) from the starting point (S) to the destination point (Y) by solving a route planning problem, which includes optimizing an objective function according to at least one first scalar parameter defined by a fan index, and optionally, d. The step of providing the calculated travel route (P2), The aforementioned fan index is - A first quantity that is positively correlated with the lateral acceleration of the automobile (1) expected along the aforementioned travel path (P2), - A second quantity that is positively correlated with the forward acceleration of the automobile (1) expected along the aforementioned travel path (P2), - A third quantity that is positively correlated with gradient fluctuations, expected along the aforementioned travel path (P2), - A fourth quantity that is negatively correlated with the deceleration of the automobile (1) expected along the aforementioned travel path (P2), - A fifth quantity that is negatively correlated with passing through urban areas or central areas, as expected along the aforementioned travel route (P2), The level of enjoyment of the driving path (P2) increases with at least one or any combination of the following: A method wherein, if the fun index indicates a deviation from the target level of the level of enjoyment, or a decrease in the level of enjoyment, the objective function deviates from the optimal value of the objective function, and the optimal value of the objective function corresponds to the solution of the path planning problem.
2. The method according to claim 1, wherein the objective function has one or more optimization variables, each value of which is suitable for determining the travel path (P2) and corresponds to at least one value of the fan index in the objective function, and as a result the path planning problem is solved by the travel path (P2) and the respective optimal values of the optimization variables, which correspond to at least one optimal value of the fan index associated with the travel path (P2) and the objective function.
3. e. The method according to claim 1 or 2, comprising the step of calculating the shortest route (P1) on the road map (6) from the starting point (S) to the destination point (Y) as the solution to minimizing a cost function, wherein the cost parameter is different from the fan index, the cost parameter being different from the fan index.
4. f. The method according to claim 3, further comprising the step of determining the current fuel level of the automobile (1).
5. g. Further includes the step of estimating whether the current fuel level is sufficient to travel the shortest route (P1), The method according to claim 4, wherein step c. or step d. is performed when step g. has a negative result.
6. h. A step of estimating whether the current remaining fuel is sufficient to travel the route (P2), i. If step g. has a negative result, the step further includes determining whether there is at least one point on the route (P2) that has a distance less than a predetermined threshold to a refueling station on the road map (6) for refueling, The method according to claim 4, wherein step d is performed if step i has a positive result.
7. j. A step of assigning the first value of the cost parameter to the travel path (P2), k. The method according to claim 3, further comprising the step of assigning a second value of the cost parameter to the shortest path (P1).
8. l. The step of determining whether the first value satisfies a predetermined relationship with the second value, The method according to claim 7, wherein step d is performed if step l has a positive result.
9. The method according to claim 7, wherein the optimization of the objective function is constrained by the fact that the first value satisfies a predetermined relationship with the second value.
10. The method according to claim 1, wherein the objective function further depends on a second parameter indicating the expected time, distance, or amount of fuel used along the travel route (P2), and as a result, if the second parameter indicates an increase in the expected time, distance, or amount of fuel used along the travel route (P2), the objective function deviates from the optimal value of the objective function.
11. The method according to claim 2, wherein the optimal value of the fan index is associated with each of a plurality of fan categories corresponding to each value or range of the fan index, and as a result, the travel path (P2) is stored in relation to each of the categories, and as a result, information items relating to the stored travel path and each of the categories can be provided.
12. m. A step of obtaining driver feedback regarding the driving route (P2) concerning the level of enjoyment of the driving route (P2), n. Based on the feedback obtained, the step of reassociating the driving path (P2) with the reassociated category among the fan categories, o. The method according to claim 11, comprising the step of storing the travel path (P2) in association with the reassociated category.
13. The method according to claim 1, wherein the objective function deviates more significantly from the optimal value of the objective function when the fan index is outside the target range compared to when the fan index takes any value within the target range.
14. A device (4) for calculating a travel route, comprising means (8) for carrying out the method described in claim 1.
15. A navigation system for an automobile (1), comprising the device (4) described in claim 14.