Method, computer program, and device for adjusting the operating parameters of a means of transport, as well as means of transport

Abstract

Method for adjusting operating parameters of a means of transport (40) in response to the detection (10) of a malfunction of a system (41, 42) of the means of transport (40), comprising the steps: - Detection (10) of a malfunction of a system (41, 42) of the means of transport (40); - Acquisition (11) of environmental data (UD) relating to a driving situation of the means of transport (40); - Acquisition (12) of status data (ZD) of the means of transport (40); and - Determine (13) operating parameters to be used (BP) O ) using an optimization algorithm based on the environment data (UD), the state data (ZD) and initial operating parameters (BP) I ), whereby a hazard potential is determined from the environmental data (UD), the condition data (ZD) and the operating parameters (BPI), and from a degradation of the operating parameters (BP) I ) a comfort potential is determined.

Description

[0001] The present invention relates to a method, a computer program with instructions, and a device for adjusting the operating parameters of a means of transportation. The invention further relates to a means of transportation in which a method or a device according to the invention is used.

[0002] Automated driving, also known as autonomous driving or controlled driving, is the movement of vehicles, mobile robots, and driverless transport systems that are largely autonomous. There are different levels of automated driving. In Europe, various transport ministries, such as the Federal Highway Research Institute (BASt) in Germany, have defined the following levels of automation: • Level 0: “Driver only”, the driver drives, steers, accelerates, brakes, etc. • Level 1: Certain assistance systems help with vehicle operation, including a speed control system such as ACC (Automatic Cruise Control). • Level 2: Partial automation. Here, automatic parking, lane keeping, general longitudinal control, acceleration, deceleration, etc. are taken over by the assistance systems, including collision avoidance. • Level 3: High automation. The driver does not need to constantly monitor the system. The vehicle independently performs functions such as activating the turn signals, changing lanes, and maintaining lane position. The driver can attend to other tasks but must take control within a specified warning time if required. • Level 4: Full automation. The system permanently takes over driving the vehicle. If the system is no longer able to handle the tasks, the driver may be prompted to take over. • Level 5: No driver required. Apart from setting the target and starting the system, no human intervention is required.

[0003] In fully automated (Level 4) and autonomous (Level 5) vehicles, the driver is relieved of driving duties and becomes a passenger. In the event of a critical failure in the primary braking system, the human fallback option is therefore eliminated. The driver cannot decelerate the vehicle solely by applying pressure to the brake pedal. After a critical failure in the primary braking system, the journey of the fully automated or autonomous vehicle should not be interrupted, but rather continued. The vehicle enables safe continuation of the journey using a secondary braking system. However, the longer this continuation of the journey lasts, the higher the probability of a potential secondary failure. If the secondary braking system also fails, the vehicle's ability to decelerate can be ensured by a tertiary braking system, at least to bring the vehicle to a standstill.The vehicle's braking functions must therefore be ensured by means of redundant systems.

[0004] Therefore, to ensure the operational braking function of a Level 4 / 5 vehicle, three brake control systems must be provided, each capable of independently decelerating the vehicle. A secondary or tertiary brake system cannot be expected to be as effective as the primary brake system.

[0005] A similar problem is to be expected due to the increasing demands placed on modern battery-electric vehicles. These require not only new battery technologies but also new braking systems. Brake-by-wire systems will be increasingly used in this area in the future. However, with this technology, the elimination of the mechanical link between the brake pedal and the brake booster means there is no longer a fallback system that ensures deceleration through the driver's foot force alone in the event of a system failure. Since driving should be able to continue after potential failures in the brake-by-wire system, the vehicle's deceleration capability must be guaranteed. One possibility here is the vehicle's integrated electric motor operating as a generator. Because the performance of this deceleration system and its vehicle dynamics control are limited, the functional requirements for the fallback braking system must be reduced.

[0006] One way to counteract the aforementioned performance losses is to adopt a defensive operating strategy for the vehicle during a degraded journey, in which individual operating parameters are adapted.

[0007] In this context, DE 10 2020 202 477 A1 describes a safety system for an electrically powered motor vehicle, comprising a first braking system, a second braking system (which includes an electric machine designed to propel the vehicle), and a third braking system. The safety system is switchable from normal operation, in which the vehicle can be braked using the first braking system, to fault operation, in which the vehicle can be braked using the second braking system. Furthermore, a method for degrading the drive system according to the current performance of the braking function, which is primarily determined by the vehicle's condition and the prevailing environmental conditions, is described.

[0008] This adaptation of the operating strategy, described above, is implemented at the system level, the track guidance level, and the navigation level. A reduction in functional requirements is accompanied by an increase in safety for vehicle occupants and other road users. An adaptation at the system level might, for example, involve preconditioning the battery of an electric vehicle to avoid high charge levels during regenerative deceleration. An example of an adaptation at the track guidance level is reducing the vehicle speed. An adaptation at the navigation level might, for example, involve avoiding downhill sections of track. Reducing speed and adjusting other parameters creates a conflict of objectives between minimizing the potential for danger on the one hand and maximizing occupant comfort on the other.due to a longer travel time caused by the reduced driving speed.

[0009] Against this background, WO 2017 / 220169 A1 describes a dynamically triggered, automated motor vehicle system, in particular a motor vehicle system based on expert systems or machine learning, which relies on the real-time acquisition of vehicle data by means of distributed data transmission devices on autonomously or semi-autonomously driving motor vehicles.

[0010] WO 2021 / 089567 A1 describes a system for controlling a vehicle. The system comprises a cloud server that performs a three-dimensional simulation of actual driving conditions for vehicles within a predefined area, based on real-time information collected from the controlled vehicle and information from surrounding vehicles. Furthermore, the cloud server generates control information for decisions during the driving simulation of the controlled vehicle, based on the three-dimensional simulation of the driving conditions. This control information is then sent to an onboard automated driving system of the controlled vehicle.

[0011] DE 10 2018 131 470 A1 describes a driver assistance system for a motor vehicle. The driver assistance system comprises environmental sensors, a control unit, and an evaluation module. The control unit is configured to determine an assistance function control signal based on environmental data acquired by the environmental sensors. This signal is functionally related to at least one assistance system parameter and the acquired environmental data in a predetermined way. The control unit is also configured to control the vehicle using the assistance function control signal to provide an assistance function. The evaluation module is configured to determine a success value for the assistance function control signal based on reference information and, if the success value falls below a success threshold, to trigger an optimization of the at least one assistance system parameter.

[0012] DE 10 2019 208 735 B4 describes a method for operating a vehicle's driver assistance system, in which sensor data from the vehicle's environment are successively recorded and verified. The verified sensor data are analyzed using a neural network. Based on the analyzed sensor data, control data for the semi-automated or fully automated control of the vehicle are generated.

[0013] DE 10 2016 007 563 A1 describes a method for trajectory planning. In this method, a vehicle driven by a driver follows an actual trajectory. Using recorded sensor data and the vehicle's current driving state, a planned trajectory is generated and compared with the actual trajectory. The comparison result is then evaluated.

[0014] One object of the invention is to provide improved solutions for adjusting the operating parameters of a means of transportation.

[0015] This problem is solved by a method having the features of claim 1, by a computer program with instructions according to claim 11, by a device having the features of claim 12, and by a means of propulsion according to claim 13. Preferred embodiments of the invention are the subject of the dependent claims.

[0016] According to a first aspect of the invention, a method for adjusting operating parameters of a means of transport in response to the detection of a malfunction of a system of the means of transport comprises the steps: - Detecting a malfunction in a system of the means of transport; - Recording environmental data regarding a driving situation of the means of transport; - Recording status data of the means of transport; and - Determining operating parameters to be used by means of an optimization algorithm based on the environmental data, the state data and initial operating parameters, whereby a hazard potential is determined from the environmental data, the state data and the operating parameters and a comfort potential is determined from a degradation of the operating parameters.

[0017] According to a further aspect of the invention, a computer program comprises instructions which, when executed by a computer, cause the computer to perform the following steps for adjusting operating parameters of a means of transportation in response to the detection of a malfunction of a system of the means of transportation: - Detecting a malfunction in a system of the means of transport; - Recording environmental data regarding a driving situation of the means of transport; - Recording status data of the means of transport; and - Determining operating parameters to be used by means of an optimization algorithm based on the environmental data, the state data and initial operating parameters, whereby a hazard potential is determined from the environmental data, the state data and the operating parameters and a comfort potential is determined from a degradation of the operating parameters.

[0018] The term "computer" is to be understood broadly. In particular, it also includes control units, embedded systems, and other processor-based data processing devices.

[0019] The computer program can, for example, be made available for electronic retrieval or be stored on a computer-readable storage medium.

[0020] According to a further aspect of the invention, a device for adjusting operating parameters of a means of transport in response to the detection of a malfunction of a system of the means of transport comprises: - a monitoring module for detecting a malfunction of a system of the means of transport; - a data acquisition module for capturing environmental data relating to a driving situation of the means of transport and for capturing status data of the means of transport; and - a computing module for determining operating parameters to be used by means of an optimization algorithm based on the environmental data, the state data and initial operating parameters, whereby a hazard potential is determined from the environmental data, the state data and the operating parameters and a comfort potential is determined from a degradation of the operating parameters.

[0021] In the solution according to the invention, finding suitable values ​​for the operating parameters of the means of transport is considered a multi-criteria optimization problem. Using an optimization algorithm, a set of minimum and maximum parameter values ​​is identified at the time of an upcoming action within a given scenario, ensuring the safest and most comfortable execution of the action. Care is taken to ensure that the criticality of the scenario is not increased. From this set of solutions, a suitable parameter combination is then selected.

[0022] According to the invention, the operating parameters are adjusted in response to the detection of a malfunction in a system of the means of transport. Since the control systems of a means of transport are designed for the regular operation of the installed systems, the use of the solution according to the invention is often not necessary during regular operation. However, it is particularly advantageous for determining the most suitable operating parameters in the event of a fault.

[0023] According to the invention, a hazard potential is determined from the environmental data, the condition data, and the operating parameters. By simulating the current operating situation, taking into account the current traffic situation and other environmental data, a hazard potential based on the current design of the operating parameters can be derived as a first criterion. This hazard potential can, for example, be assessed using established safety metrics such as FuSi (Functional Safety) or ASIL (Automotive Safety Integrity Level).

[0024] According to the invention, a comfort potential is determined from a degradation of the operating parameters. Comfort, as a second criterion, can be extracted directly from the set of current operating parameters. For example, it can be assumed that the minimal degradation of the operating parameters leads to optimal comfort.

[0025] According to one aspect of the invention, the malfunctioning system is a braking system or a steering system. Faults in these systems typically have a significant impact on the operating strategy, as only a degraded driving mode is possible. In this case, the solution according to the invention can achieve a significant increase in comfort for the occupants of the vehicle.

[0026] According to one aspect of the invention, the environmental data describes a traffic situation or environmental parameters. The traffic situation has a significant influence on the actions that are possible or necessary in a given scenario. Environmental parameters can also be relevant. It is therefore useful to include them in the evaluation of the target criteria.

[0027] According to one aspect of the invention, the environmental parameters include a coefficient of friction, an ambient temperature, or information about an incline or decline. Since these environmental parameters have a significant influence on, for example, braking behavior, it is advantageous if they are taken into account by the optimization algorithm.

[0028] According to one aspect of the invention, the state data includes speed, distances, steering angles, or reaction times. This state data makes it possible, for example, to determine operating parameters that are suitable for avoiding a collision or at least minimizing the severity of an accident.

[0029] According to one aspect of the invention, the optimization algorithm is an evolutionary algorithm. Evolutionary algorithms are based on the evolutionary theory of living organisms. They apply individual factors of natural evolution to optimization. Evolutionary algorithms rely on an interplay between modification and the selection of better individuals and are particularly well-suited for solving complex problems. Of course, other algorithms can also be used.

[0030] According to one aspect of the invention, the method is executed in real time within the means of transport. Since increasing computing power is expected, particularly in Level 4 / 5 vehicles, a method according to the invention can be carried out online in the vehicle in real time. In this way, the required operating parameters are available very quickly.

[0031] According to one aspect of the invention, the optimization algorithm is executed outside the vehicle. The selected operating parameters are then transmitted wirelessly to the vehicle. Particularly with Level 4 / 5 vehicles, increasing connectivity is to be expected. Therefore, an alternative approach is to learn the operating parameters offline and transmit them to the vehicle via a wireless connection. In this way, the solution according to the invention can also be implemented for vehicles without sufficient computing power.

[0032] According to one aspect of the invention, operating parameters to be used in response to a situation detected locally in the means of transport are retrieved from a database outside the means of transport. Another way of implementing the solution according to the invention is to detect the current situation, e.g., by means of an AI-supported tool, and to access existing data records. These may, for example, be located in a backend of a manufacturer of the means of transport.

[0033] A solution according to the invention is particularly advantageous when used in a means of transport. The means of transport can be, for example, a motor vehicle, such as a passenger car or a commercial vehicle. The use of the solution according to the invention has the advantage that, even in the event of system failures, the means of transport is able to continue driving in a reduced capacity, maximizing passenger comfort.

[0034] Further features of the present invention will become apparent from the following description and the attached claims in conjunction with the figures. Fig. Figure 1 schematically shows a procedure for adjusting the operating parameters of a means of transport; Fig. Figure 2 shows a first embodiment of a device for adjusting the operating parameters of a means of transport; Fig. Figure 3 shows a second embodiment of a device for adjusting the operating parameters of a means of transport; Fig. Figure 4 schematically represents a means of transport in which a solution according to the invention is implemented; Fig. Figure 5 schematically shows a scenario in which a solution according to the invention can be applied; Fig. Figure 6 schematically shows a cycle of an evolutionary algorithm; Fig. Figure 7 shows a starting population of 80 individuals; Fig. 8 shows certain criteria for the individuals of the starting population; Fig. Figure 9 shows the criteria determined for individuals of the 70th generation; and Fig. Figure 10 schematically shows another scenario in which a solution according to the invention can be applied.

[0035] To better understand the principles of the present invention, embodiments of the invention are explained in more detail below with reference to the figures. It is understood that the invention is not limited to these embodiments and that the described features can also be combined or modified without leaving the scope of protection of the invention as defined in the appended claims.

[0036] Fig. Figure 1 schematically shows a procedure for adjusting the operating parameters of a means of transport. In a first step, it is detected that a malfunction of a system of the means of transport exists, in particular a braking system or a steering system. To determine the operating parameters, environmental data relating to a driving situation of the means of transport are recorded. This environmental data can describe a traffic situation or environmental parameters, e.g., a coefficient of friction, an ambient temperature, an incline, or a decline. In addition, state data of the means of transport are recorded. This state data can include, for example, speed, distances, steering angles, or reaction times. The operating parameters to be used are then determined by means of an optimization algorithm based on the environmental data, the state data, and initial operating parameters. A hazard potential is determined from the environmental data and the state data.Furthermore, a comfort potential is determined from a degradation of the operating parameters. The optimization algorithm can, for example, be an evolutionary algorithm. In a first embodiment, the described procedure is executed in real time in the means of transport. In a second embodiment, the optimization algorithm is executed outside the means of transport. The 13 operating parameters to be used are then transmitted wirelessly to the means of transport. In a third embodiment, the operating parameters to be used are retrieved from a database outside the means of transport in response to a situation detected locally in the means of transport.

[0037] Fig. Figure 2 shows a simplified schematic representation of a first embodiment of a device 20 for adjusting the operating parameters of a means of transportation. The device 20 has an input 21 through which data D from sensors 43, 44 of the means of transportation can be received. A monitoring module 22 is configured to detect the presence of a malfunction in a system of the means of transportation, in particular a braking system or a steering system. A data acquisition module 23 is configured to acquire environmental data UD relating to a driving situation of the means of transportation and state data ZD of the means of transportation contained in the received data D. The environmental data UD can describe a traffic situation or environmental parameters, e.g., a coefficient of friction, an ambient temperature, an incline, or a decline. The state data ZD can include, for example, speed, distances, steering angles, or reaction times.A computing module 24 is set up, operating parameters to be used are BP. O using an optimization algorithm based on the environmental data UD, the state data ZD and initial operating parameters BP I to determine this. This is done using the environmental data UD, the condition data ZD, and the operating parameters BP. I A hazard potential is determined. Furthermore, a degradation of the operating parameters BP is observed. I A comfort potential is determined. The optimization algorithm could, for example, be an evolutionary algorithm. The operating parameters BP to be used are determined in this way. OThe outputs 27 of the device 20 can be sent to a control system 45 of the means of transport. Naturally, the device 20 can also be part of such a control system 45. In a first embodiment, the device 20 executes the described process steps in real time within the means of transport. In a second embodiment, the optimization algorithm is executed outside the means of transport. The operating parameters BP to be used are determined O are then transmitted wirelessly to the means of transport. In a third embodiment, the operating parameters BP to be used are determined in response to a situation detected locally in the means of transport. O retrieved from a database outside the means of transport.

[0038] The monitoring module 22, the data acquisition module 23, and the computing module 24 can be controlled by a control module 25. Settings of the monitoring module 22, the data acquisition module 23, the computing module 24, or the control module 25 can be changed via a user interface 28. The data generated in the device 20 can be stored in a memory 26 as needed, for example, for later evaluation or for use by the components of the device 20. The monitoring module 22, the data acquisition module 23, the computing module 24, and the control module 25 can be implemented as dedicated hardware, for example, as integrated circuits. Of course, they can also be partially or completely combined or implemented as software running on a suitable processor, such as a GPU or a CPU.Input 21 and output 27 can be implemented as separate interfaces or as a combined bidirectional interface.

[0039] Fig. Figure 3 shows a simplified schematic representation of a second embodiment of a device for adjusting the operating parameters of a means of transportation. The device 30 comprises a processor 32 and a memory 31. For example, the device 30 is a computer or a control unit. Instructions are stored in the memory 31 which, when executed by the processor 32, cause the device 30 to perform the steps according to one of the described methods. The instructions stored in the memory 31 thus embody a program executable by the processor 32, which implements the method according to the invention. The device 30 has an input 33 for receiving information, for example, environmental data and status data. Data generated by the processor 32 is provided via an output 34. Furthermore, it can be stored in the memory 31.Input 33 and output 34 can be combined into a bidirectional interface.

[0040] The processor 32 can comprise one or more processor units, such as microprocessors, digital signal processors, or combinations thereof.

[0041] The memory elements 26, 31 of the described embodiments can have both volatile and non-volatile memory areas and can include a wide variety of storage devices and storage media, for example hard disks, optical storage media or semiconductor memory.

[0042] A preferred embodiment of a solution according to the invention will be described below with reference to Fig. 4 to Fig. 10 will be described using a specific application example.

[0043] Fig. Figure 4 schematically represents a means of transport 40 in which a solution according to the invention is implemented. In this example, the means of transport 40 is a motor vehicle. The motor vehicle has a braking system 41, a steering system 42, and a control system 45. An environmental sensor system 43 is configured to provide environmental data relating to the driving situation of the motor vehicle. The environmental sensor system 43 can, for example, include cameras, radar sensors, lidar sensors, ultrasonic sensors, or climate sensors. A condition sensor system 44 is configured to provide condition data of the motor vehicle. The control system 45 has a device 20 according to the invention for adjusting operating parameters, which can alternatively be designed as an independent component or integrated into another component of the motor vehicle.The device 20 accesses data from the environmental sensors 43 and the condition sensors 44 to adjust the operating parameters. A data transmission unit 46 allows connections to be established to a backend 50, for example, to exchange data or to receive operating parameters stored in a database 51 of the backend 50. A memory 47 is provided for storing data. Data exchange between the various components of the vehicle takes place via a network 48.

[0044] Fig. Figure 5 schematically illustrates a scenario in which a solution according to the invention can be applied. The ego vehicle 60 approaches a target vehicle 61. The target vehicle decelerates to a standstill, i.e., a full deceleration occurs. The starting point is the situation that both the primary and the secondary brake control systems of the ego vehicle 60 malfunction. The vehicle deceleration must therefore be ensured by a further redundant system, i.e., a tertiary brake system. This could, for example, be a combination of an electric motor and a parking brake. It must now be ensured that the functional requirements for the tertiary brake system are reduced. This is achieved by a defensive operating strategy. The necessary adjustment of the operating strategy is carried out at the system level, the path guidance level, and the navigation level. At the system level, for example,The performance of the electric motor can be improved. At the navigation level, the route with the lowest demands, manageable by the tertiary braking system, can be selected. At the path control level, operating parameters can be adapted, e.g., regarding speed, steering angle, etc.

[0045] Parameters can be assigned to each scenario. These include environmental parameters, such as coefficient of friction, temperature, gradient, incline, etc. These determine whether the scenario falls within the intended operational design domain (ODD). Secondly, there are vehicle parameters, such as speed, distances, steering angle, and reaction times, etc. These are relevant to the dynamic driving tasks (DDT). Relevant parameters in the depicted scenario are the speed of the target vehicle 61 or its deceleration profile, the ego speed, and the initial distance to the target vehicle 61.

[0046] The aim is now to identify, for the given scenario, the limit values ​​of the operating parameters that the ego vehicle 60 must possess at the time of braking in order to, on the one hand, avoid a collision or minimize the severity of an accident, and on the other hand, prevent so-called "pass-through," i.e., other vehicles merging into the area between the ego vehicle 60 and the target vehicle 61. The second criterion implies that the distance and relative speed should be low.

[0047] Fig. Figure 6 schematically illustrates a cycle of an evolutionary algorithm as described in K. Weicker: "Evolutionary Algorithms," Springer Vieweg. Evolutionary algorithms are based on the evolutionary theory of living organisms. Individual factors of natural evolution are applied to optimization. Evolutionary algorithms rely on an interplay between modification and the selection of better individuals. The cycle begins with an initialization (A1) of the population, followed by an evaluation (A2). If a check (A3) reveals that a termination criterion has been met, the population is output (A4). Otherwise, a mating selection (A5) takes place, followed by a recombination (A6) in which new solution vectors are generated from existing solution vectors. In a subsequent mutation (A7), these new solution vectors are modified.The modified solution vectors are subjected to an evaluation A8 and an environmental selection A9, before the achievement of the termination criterion is checked again A3.

[0048] The different representation of solution candidates for any given optimization problem leads to the delimitation of the search space Ω. This describes the phenotype. The genotype G In contrast, it represents the solution candidate in an individual. The evaluation function (7) is defined on the phenotype. The operators recombination (3) and mutation (2) are defined on the genotype. A decoding function dec:G→Ω This enables the evaluation of the individual present in the genotype by mapping it into the search space.

[0049] An individual is defined as a tuple (AG,AS,AF). This consists of the genotype AG∈G, optional additional information AS∈Z and the goodness of fit AF = f (dec(A. G)) ∈ ℝ. Furthermore, the mutation operator is defined as a mapping Mutξ:G×Z→G×Z and the recombination operator analogously with r ≥ 2 parents and s ≥ 1 children also as a mapping Rekξ:(G×Z)r→(G×Z)s.

[0050] Here, ξ ∈ Ξ represents a state of the random number generator. Ξ is the set of possible states.

[0051] Recombination, following an evolutionary algorithm, involves combining individual components of the parent individuals' genotypes and then transferring them to the offspring. This generates variants of existing solution candidates. To ensure compliance with boundary conditions, specially designed mutation and recombination operators can be used, preventing the generation of invalid individuals.

[0052] The selection operator takes a population as input and selects s ​​individuals from r individuals. The selection operator is based on index selection, since only individuals are selected and no new ones are created. The selection operator is defined on a population P = (A(1), ...,A(r)) as a mapping Selξ:(G×Z×ℝ)r→(G×Z×ℝ)s〈A(i)〉1≤i≤r↦〈A(ISξ(c1,...,cr)k)〉1≤k≤swith A(i)=(ai,bi,ci).

[0053] Index selection is defined by: ISξ:ℝr→{1,...,r}s.

[0054] A basic evolutionary algorithm is shown in Algorithm 1.

[0055] Determining the operating parameters can be viewed as an optimization problem (Ω, F, ≻), with the comparison relation ≻∈ {<, >}, with the search space Ω ∈ ℝ n and Ω={(x1...xn): c1,Min≤x1≤c1,Max∧...∧cn,Min≤xn≤cn,Max}.

[0056] The candidate solution x ∈ Ω contains relevant operating parameters of the vehicle. The restriction of the operating parameters formulated as a constraint in (6) leads to a delimitation of the search space. It is not necessarily required for every decision variable x i A solution candidate x ∈ Ω must be subject to a restriction. Furthermore, the restrictions can be more than just lower and upper bounds. They can also include, for example, the requirement of integers, etc.

[0057] The evaluation function F: ℝ n → ℝ k is defined for x ∈ Ω by: F(x)=(Criterion1 ...Criterionk).

[0058] For the in Fig. The five scenarios presented can use the collision speed v as a target criterion. Kollision , the relative velocity v Ziel - v Ego and the distance d ZielThe collision between the ego vehicle and the target vehicle is considered. The collision speed should be minimized, as it influences the severity of the accident. This can be determined, for example, through simulation using known methods. A small distance should, in particular, prevent another vehicle from cutting in front of the ego vehicle, for example, after an overtaking maneuver.

[0059] Fig. Figure 7 shows an example starting population of 80 individuals. The individuals are randomly and uniformly distributed over the search space Ω. Fig. Figure 8 shows the criteria of the evaluation function F(x) determined for the individuals of the initial population. The individuals are mapped to the solution space using the evaluation function (7). Fig. Figure 9 shows the criteria of the evaluation function F(x) determined for individuals of the 70th generation. The dashed area highlights those members of the Pareto-optimal set that avoid a collision. Under the considered scenario, the implemented evolutionary algorithm results in a broad distribution of individuals along the Pareto front. This provides the decision-maker with a wide range of possible solutions. Optimal combinations of operating parameters can thus be chosen to prevent a collision. The members above the highlighted area are of interest for inferring the potential severity of a collision if one becomes unavoidable. Based on this, countermeasures can be initiated, such as tightening the belts.

[0060] The Pareto front can also be used for other applications. Fig.Figure 10 schematically illustrates another scenario in which a solution according to the invention can be applied. This scenario involves a target vehicle 61' merging in front of the ego vehicle 60. Based on the solution according to the invention, a deceleration point can be determined when the target vehicle 61' merges in front of the ego vehicle 60. In this case as well, a collision should be avoided as soon as the target vehicle 61' is relevant for full deceleration. Another application is evasive maneuvers, for which the steering angle, speed, and evasive maneuver point can be determined. Reference symbol list 10. Detecting a malfunction 11. Collecting environmental data regarding a driving situation 12. Recording status data of the means of transport 13 Determining operating parameters using an optimization algorithm 20 Device Entrance 21 22 Monitoring module 23 Data Acquisition Module 24 Calculation module 25 Control module 26 storage 27 Exit 28 User interface 30 Device 31 storage 32 processor 33 Entrance 34 Exit 40 means of transport 41 Braking system 42 Steering system 43 Environmental sensors 44 Condition sensors 45 Control system 46 Data transmission unit 47 storage 49 Network 50 Backend 51 database 60 Ego Vehicle 61, 61' Target vehicle BP I Initial operating parameters BP O Operating parameters to be used D data UD environmental data ZD status data A1 Initialization A2 rating A3 inspection A4 edition A5 mating selection A6 Recombination A7 mutation A8 rating A9 To perform environmental selection of a system (41, 42) of the means of transport (40).

Claims

[1] Method for adjusting operating parameters of a means of transport (40) in response to the detection (10) of a malfunction of a system (41, 42) of the means of transport (40), comprising the steps: - Detection (10) of a malfunction of a system (41, 42) of the means of transport (40); - Acquisition (11) of environmental data (UD) relating to a driving situation of the means of transport (40); - Acquisition (12) of status data (ZD) of the means of transport (40); and - Determine (13) operating parameters to be used (BP) O ) using an optimization algorithm based on the environment data (UD), the state data (ZD) and initial operating parameters (BP) I ), whereby a hazard potential is determined from the environmental data (UD), the condition data (ZD) and the operating parameters (BPI), and from a degradation of the operating parameters (BP) I ) a comfort potential is determined. [2] Method according to claim 1, wherein the optimization algorithm identifies a set of minimum or maximum parameter values ​​at the time of an upcoming action within a current scenario that ensure the safest and most comfortable execution of the action, from which a situationally appropriate parameter combination is then selected. [3] Method according to claim 1 or 2, wherein the system with a malfunction is a braking system (41) or a steering system (42). [4] Method according to any of the preceding claims, wherein the environmental data (UD) describe a traffic situation or environmental parameters. [5] Method according to claim 4, wherein the environmental parameters include a coefficient of friction, an ambient temperature or information about a slope or gradient. [6] Method according to any of the preceding claims, wherein the state data (ZD) comprise speed, distances, steering angles or reaction times. [7] Method according to any of the preceding claims, wherein the optimization algorithm is an evolutionary algorithm. [8] Method according to any one of claims 1 to 7, wherein the method is carried out in real time in the means of transport (40). [9] Method according to any one of claims 1 to 8, wherein the optimization algorithm is performed outside the means of transport (40) and the specific operating parameters (BP) to be used (13) O ) are transmitted wirelessly to the means of transport (40). [10] Method according to any one of claims 1 to 8, wherein operating parameters (BP) to be used in response to a situation detected locally in the means of transport (40) O ) from a database (51) outside the means of transport (40). [11] Computer program with instructions which, when executed by a computer, cause the computer to perform the steps of a method according to one of claims 1 to 10 for adjusting operating parameters of a means of transport (40) in response to the detection (10) of a malfunction of a system (41, 42) of the means of transport (40). [12] Device (20) for adjusting operating parameters of a means of transport (40) in response to the detection (10) of a malfunction of a system (41, 42) of the means of transport (40), comprising: - a monitoring module (22) for detecting (10) a malfunction of a system (41, 42) of the means of transport (40); - a data acquisition module (23) for acquiring (11) environmental data (UD) relating to a driving situation of the means of transport (40) and for acquiring (12) state data (ZD) of the means of transport (40); and - a computing module (24) for determining (13) operating parameters to be used (BP O ) using an optimization algorithm based on the environment data (UD), the state data (ZD) and initial operating parameters (BP) I ), wherein the computing module (24) is set up to determine a hazard potential from the environmental data (UD), the condition data (ZD) and the operating parameters (BPI) and from a degradation of the operating parameters (BP) I ) to determine a comfort potential. [13] Means of transport (40), wherein the means of transport (40) has or is equipped to perform a method according to any one of claims 1 to 10 for adjusting operating parameters of the means of transport (40) in response to the detection (10) of a malfunction of a system (41, 42) of the means of transport (40).

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