CROSS REFERENCE

The present application claims the benefit under 35 U.S.C. § 119 of German Patent Application No. DE 102018222783.7 filed on Dec. 21, 2018, which is expressly incorporated herein by reference in its entirety.

FIELD

The present invention relates to a method for controlling an automated or autonomous locomotive device, as well as to an evaluation unit of a locomotive device.

BACKGROUND INFORMATION

With the continuous evolution of highly automated and autonomous vehicles, the relevance of trajectory planning is also growing. In this context, the requirements for trajectory planning may be varied. They extend from the planning of a comfortable trajectory to the lane change on the highway, up to the safety-related avoidance maneuver in urban and rural traffic. Thus, high demands, such as the adherence to particular comfort or safety criteria or the prediction of critical situations, are often placed on the trajectory planning as a function of the request. In addition, it must be possible to compute the trajectories in real time, since they must followed during current vehicle operation. In order to satisfy this requirement, polynomial-based approaches are discussed in the relevant literature. Polynomial-based design approaches do possess real-time capability, but have the disadvantage that constraints may not be taken explicitly into account in a simple manner. In addition, the asymmetric driving behavior of a person may only be simulated with difficulty, by polynomials of low order over a relatively long time horizon. For example, a human driver initiates a lane change more dynamically than he/she ends it. Model predictive control (MPC) constitutes an option for integrating constraints and asymmetric behavior in the trajectory planning. In this connection, however, since optimization problems must be solved continuously in view of constraints, MPC-based planning requires very high computing power, in order to be real-time capable. However, given the standard capacity utilization of control units, which are used in today's vehicles, such high computing power may not be ensured.

SUMMARY

According to a first aspect, the present invention relates to a method for controlling an automated or autonomous locomotive (locomotion, transport) device. In the spirit of the present invention, automobiles, in particular, passenger cars and/or cargo trucks and/or motorcycles and/or airplanes and/or ships come into consideration. A locomotive device operable in an “autonomous” and/or “automated” manner may presently be understood as at least a locomotive device, which is able, for example, using an evaluation unit, to plan a trajectory independently and additionally move independently on this trajectory. In a first step, the method of the present invention includes ascertaining a deviation, e.g., of a path, which requires a return to a predefined trajectory. Such ascertainment of the deviation may be carried out, in particular, by the locomotive device, for example, using an evaluation unit, for example, a CPU and/or a microcontroller or an electronic control unit. Further dynamic system variables currently available, such as the speed and/or the acceleration, which may be ascertained by a conventional locomotive-device sensor system, may also be taken into account for the ascertainment of the deviation. A “predefined trajectory” may be understood as a global trajectory, which runs, for example, on a reference line or reference curve of a Frenet coordinate system. The global trajectory may be planned, for example, at or prior to the start of the trip and does not consider any eventualities, which, due to different routing or due to obstacles, would signify a necessity of deviating from this trajectory. A deviation and/or necessity of a return to this predefined trajectory may occur, for example, on the basis of sensory data of the vehicle, which come, for example, from an ultrasonic sensor and/or a camera and/or a lidar sensor and/or a radar sensor and/or an accelerometer and/or a speedometer. A deviation may be justified by an obstacle, which means that the locomotive device must deviate from the predefined trajectory. The obstacle may be detected or ascertained sensorially. In addition, because of the ascertained deviation, in particular, a roadway corridor is formed, which results due to obstacles and roadway boundaries. In response to the ascertainment of a deviation, a jerk in the form of an input variable is automatically computed as a function of the deviation. For example, a deviation from the predefined reference trajectory, e.g., a distance, and/or a current acceleration and/or a current velocity of the locomotive device may be utilized for calculating the jerk. Starting from the input variable, an unconstrained correcting variable is automatically computed for returning to the predefined trajectory. Presently, a “correcting variable” is understood, in the control-engineering sense, as an output variable of a utilized control element (for example, an evaluation unit);

A targeted intervention in the controlled member of the system, that is, of the locomotive device, is able to take place via the output variable and/or correcting variable, on the basis of the input variable (the jerk). However, the unconstrained correcting variable does not act directly upon the system. On the contrary, further manipulation steps take place, which are explained below. The unconstrained correcting variable for returning to the predefined trajectory is calculated as a function of a weighted sum that includes a weighted summand of the input variable and a weighted summand of a state and/or weighted summands of further states of the return path. In this connection, the summand of the input variable may also have a weighting of zero. In addition, further states may be considered. In particular, each state receives a weighted summand, in order to compute the unconstrained correcting variable. The unconstrained correcting variable is limited by the saturation cascade. After thrice integrating an output value obtained in this manner, with respect to time, a state may be obtained, which acts directly upon the locomotive device. For the next instant, this operation is repeated, starting from the current state, etc. For example, the velocity, the jerk, the acceleration and/or the position of the locomotive device come into consideration as a “state.” In this connection, the state is calculated, in particular, as a function of the deviation from the predefined trajectory. For example, the state may be ascertained sensorially. In particular, for a specific lateral or longitudinal or vertical motion of the locomotive device, assuming that the enumerated motions are decoupled from each other, in the case of the deviation, the state is ascertained for the respective acceleration, velocity and position of the vehicle and transformed to a state space. For example, the planning of the unconstrained correcting variable may be regarded as a state return path, which means that starting from an initial condition, the locomotive device may be stabilized asymptotically so as to approach the predefined reference trajectory. For example, a return matrix may be calculated for this with the aid of pole selection and/or a linear quadratic regulator (LQR) method. Based on the use of the weighted sum and its simple arithmetic processing, a required computing speed or power may be reduced by a factor of 10 to 100 in comparison with predictive modeling approaches. In a subsequent step, a constrained correcting variable with regard to the jerk is computed automatically. In this connection, the unconstrained correcting variable is manipulated via a cascade of a plurality of stages. For this, a saturation function regarding a dynamic system state is provided for each stage of the cascade. The saturation function constitutes a constraint, which is undertaken within the stage of the cascade with regard to a dynamic system state. A dynamic system state may contain, for example, a jerk, which, according to the physical definition, includes the third derivative of the distance with respect to time. In this connection, a deviation from a predefined reference trajectory may be ascertained, in particular, one used for the lateral coordinate. In simplified terms, it may be assumed, in particular, that the lateral and the longitudinal motion of the locomotive device are decoupled; at least a constant longitudinal movement being assumed in the determination of the return trajectory for the lateral movement. In the case of a Frenet coordinate system, the time characteristic of the distance of the predefined trajectory, as well as of the length of the curve of the predefined trajectory, may be used in order to compute, in each instance, a jerk from this. In particular, the characteristic curve of the jerk may be used, in this case, as a correcting variable. Mathematical models, which result from these assumptions, may be transformed, in particular, in a state space. In response to that, the constrained correcting variable is integrated in a time-dependent manner, e.g., integrated three times with respect to time, in order to obtain the constrained return trajectory, that is, which contains the constraint data from the cascade with regard to dynamic system states (e.g., jerk and/or acceleration). In a final step, the locomotive device is automatically steered towards the predefined trajectory by way of a constrained return trajectory. This may be accomplished, e.g., with the aid of a controller. Thus, using the method of the present invention, a jerk is limited to the effect that a comfortable trajectory plan may be carried out. For example, the steps discussed above may be ascertained and carried out by an evaluation unit, for example, a CPU and/or a microcontroller and/or an electronic control unit. Thus, in the preceding, trajectory planning may be provided, which, in comparison with classic planning approaches based on polynomials of higher order, may also be used for asymmetric driving behavior (by taking into account the jerk as an input and output variable) and may take constraints into account.

Preferred further refinements of the present invention are described herein.

According to one advantageous further refinement of the present invention, the dynamic system state includes a jerk and/or a velocity and/or an acceleration and/or a position of the locomotive device. The dynamic system state may relate to, in particular, a longitudinal and/or lateral and/or a vertical trajectory component of the locomotive device. Consequently, all of the relevant, dynamic system states, which are necessary for trajectory planning, may be used within the method of the present invention in a computationally efficient manner.

According to a further advantageous refinement of the method according to the present invention, the constrained return trajectory includes a longitudinal and/or a lateral and/or a vertical component. A vertical trajectory component may then be relevant, in particular, if the locomotive device is an airplane. If the method of the present invention is carried out for a lateral trajectory component, then, in particular, a constant longitudinal velocity is always assumed in spite of the simplified decoupling assumption of the two trajectory components.

According to one advantageous further refinement of the method of the present invention, the cascade may include two to four stages. The cascade ideally includes four stages. Therefore, all of the constraint data (e.g., jerk, acceleration, position and velocity) necessary for the planning of the constrained return trajectory may be considered without requiring a high computing capacity.

According to a further advantageous refinement of the method of the present invention, the saturation function may be ascertained as a function of predefined constraints. In this manner, variant-rich initial conditions for the generation of saturation functions may be set specifically to the need, in order to model asymmetric human driving behavior as realistically as possible. For example, a predefined constraint regarding the position of the locomotive device may include coordinates of a roadside for calculating a limiting corridor. Additionally, or as an alternative, a maximum jerk and/or a maximum acceleration may be predefined as a function of test studies. For example, a maximum jerk and/or a maximum acceleration may be defined, in each instance, as a maximum value, the exceedance of which causes unease in a representative group of test subjects. A current state of the locomotive device (with regard to acceleration and/or position and/or velocity) may be estimated, for example, as a function of corresponding sensorial data of the locomotive device.

According to a further advantageous refinement of the method according to the present invention, the automatic determination of the constraint does not vary with time. In this manner, computing power may be conserved while neglecting the time component.

According to one advantageous embodiment of the method of the present invention, a later step, that is, a manipulation of the correcting variable in a later stage of the cascade, is preferred over an earlier step, that is, a manipulation of the correcting variable in an earlier step of the cascade. In other words, this means that the unconstrained correcting variable passes through individual saturation functions, which may be associated with the constraints of the individual states. If different constraints are competing, that is, there is no physically meaningful solution for all of the constraints, then the constraint, that is, the manipulation, using the saturation function that occurs later in the cascade, is dominant, since this may overwrite the adaptation of the preceding saturation steps. This is also called “prioritization.” Due to this, one or more given constraints having a lower priority, such as comfort, may be deliberately violated, it the problem may no longer be solved in view of all of the constraints. This makes the trajectory planning of the present invention, that is, the method of the present invention, highly robust for solving a trajectory planning problem, while at the same time, only a fraction of the computing power of an MPC method is needed.

According to a further advantageous refinement of the method according to the present invention, this includes a step of sensorial detection of an obstacle by the locomotive device. For example, the obstacle may be detected sensorially with the aid of a lidar sensor and/or a camera sensor and/or a radar sensor and/or an ultrasonic sensor. In addition, an obstacle may also be detected on the part of the vehicle, based on received data, for example, real-time traffic information data.

According to a further advantageous refinement of the method according to the present invention, this includes the step of calculating a return matrix, on the basis of which the return trajectory is automatically ascertained with regard to the dynamic system state. In particular, the return matrix may be understood to describe the dynamics of how rapidly the return trajectory is lead back to the predefined reference. For example, the return matrix may be computed prior to the start of the trip, that is, offline. Therefore, the present trajectory planning has real-time capability. Since the return matrices for the method of the present invention do not have to be computed during the trip, this method is sufficiently discrete and efficient with regard to computing power.

The following aspect of the present invention accordingly includes the advantageous embodiments and further refinements having the above-mentioned features, as well as the general advantages of the method of the present invention and the respective technical effects associated with them. In order to prevent repetition, a new listing is therefore omitted.

According to a second aspect, the present invention relates to an evaluation unit of a locomotive device; the evaluation unit being configured to implement a method according to the first aspect of the present invention. In particular, the evaluation unit may be rigidly installed in onboard electronics of the locomotive device. According to the present invention, it is, in particular, not necessary to plan a trajectory, using an external server. In particular, the method of the present invention is so computationally efficient, that it may easily be implemented in an evaluation unit of a locomotive device.

