DE102018123896A1 - Method for operating an at least partially automated vehicle - Google Patents

Abstract

In a method for operating an at least partially automated vehicle, at least one object in the surroundings of the vehicle is recognized from environmental sensor data and an environmental model of the vehicle is created, an object position and at least one object movement feature are determined for the recognized object and transferred to the environmental model by means of an optimization method, a trajectory calculated for the vehicle from the environmental model and controlled the vehicle according to the calculated trajectory.

Description

The invention relates to a method for operating an at least partially automated vehicle and, in particular, to ascertaining a trajectory to be driven by the vehicle without colliding with other objects in the surroundings of the vehicle. Objects that are in the vicinity of the vehicle are also referred to as obstacles or obstacle vehicles in the context of the following description. The term obstacle covers all road users who are generally mobile, e.g. also pedestrians currently standing at traffic lights. The vehicle for which the trajectory to be driven is determined is also referred to here as a first-person vehicle. The terms environment model and environment model are used synonymously.

The tasks of perception, planning and control play an important role in the architecture of modern automated vehicles. Comfortable and safe driving requires knowledge of the surrounding environment. Based on the knowledge of the vehicle's surroundings, i.e. the current driving situation, a trajectory must be planned along which the vehicle can drive automatically. Known trajectory planning methods can have the disadvantages that they lack a guarantee of a complete solution in real-time applications and increase inappropriately with increasing complexity of the computing time requirement.

The object of the invention is to overcome the disadvantages of the prior art.

According to the inventive method for operating an at least partially automated vehicle, at least one object in the surroundings of the vehicle is recognized from environment sensor data and an environment model of the vehicle is created. In this case, an object position and at least one object movement feature are determined for the recognized object and transferred to the environmental model. Using an optimization method, a trajectory for the vehicle is calculated from the environmental model and the vehicle is controlled in accordance with the calculated trajectory. The vehicle can be controlled for braking, accelerating and steering.

In particular, the trajectory is calculated based on information that is perceived by sensor data. The information that is perceived by sensor data is transmitted to a planning module. This is achieved by transforming the information that is perceived by sensor data into an environment model that contains merged information about the road geometry and obstacles, such as pedestrians or other vehicles.

The environment sensor data are provided by an environment sensor or by fusion of the data of several environment sensors, such as a camera, a radar, a lidar and / or an ultrasound sensor system, and processed by an object detection module that detects objects, their position relative to the vehicle and object movement characteristics such as speed and Direction of movement determined. In particular, the environment model is a potential field, preferably a cost potential field, in which low costs are specified for passable areas and higher costs for areas that are not to be driven on. In particular, each determined object is transformed into the environment model by processing the at least one object movement feature according to a predefined rule set. In particular, a target position of the vehicle is specified by another module, such as a navigation module, and transformed into the environment model. In doing so and for the optimization, the "Spline-based motion planning for automated driving," by C. Götte, M. Keller, T. Nattermann, C. Haß, K.-H. Glander, T. Bertram in Proceedings of the 20th IFAC World Congress, 2017, pp. 9444-9449, known methods used.

The calculation of the trajectory takes place in an egotrajectory calculation module, which is preferably implemented on a vehicle computer. The vehicle computer comprises at least one processor, which in particular has a plurality of processor cores, memory and communication interfaces, such as Ethernet, CAN or Flexray.

The trajectory is preferably calculated for a predetermined prediction period, also called the prediction horizon. Even with a long prediction horizon, the method according to the invention requires only a small computing time for determining a trajectory and thereby captures all the necessary information with regard to the detected objects. In particular, for each time step of the prediction horizon, a changed position of the recognized object is predicted using the object movement feature and transformed accordingly into the environmental model.

In a preferred embodiment, the environment model comprises a static and a dynamic environment model component. In short, the following only speaks of the static environment model or the dynamic environment model when the static environment or environment model component or the dynamic environment or environment model component is mentioned. In particular, both the static and the dynamic environment model Potential fields, preferably cost potential fields, in which areas to be avoided are taken into account due to high costs and passable areas due to low costs. Detected objects and their object movement characteristics are preferably processed with a predetermined rule set in order to create the dynamic environment model. In particular, the objects are transformed into the environment model as an area of high costs.

In a preferred embodiment, the dynamic environment model is based on a physical interpretation of a required safety distance. In particular, movement characteristics, such as speed and trajectory, of the first-person vehicle and / or at least one object are predicted for the prediction period. Relative speed and safety margin are preferably predicted for the prediction period. Depending on the determined data, the predefined rule set is used to transform the necessary safety distance with respect to the detected object into the dynamic environment model. The transformation with the predetermined rule set preferably comprises at least one cost function with object data of an object and object movement features. In particular, the at least one cost function is parameterized by predicted distances between the ego vehicle and the object.

In a preferred embodiment, the static environment model component contains boundary conditions or values that are determined by static objects in the environment of the vehicle. Static objects are not to be understood as road users, but rather not moving obstacles, such as traffic islands or temporary construction sites, which narrow the road. In particular, information about static objects from the environment sensor system or a car 2-X Receive communication module and transformed into the static environment model using a predefined rule set. For example, coordinates are transmitted for a non-navigable area and the costs of the static environment model are increased at these coordinates in such a way that no trajectory that touches this area is determined in the optimization step.

In a preferred embodiment, delimitation of a lane is transformed into the static environment model by processing lane-related environment sensor data with a predetermined rule set. In particular, information relating to the lane marking is automatically transformed into the static environment model by means of a rule set. In particular, the rule set contains a parameter for each lane marking, by means of which a desired distance from the lane marking can be specified. Information relating to the lane markings is preferably transmitted by a lane detection system.

In a preferred embodiment, the object position and the at least one object movement feature are used to parameterize a predetermined rule set in order to create the environmental model. In particular, the predetermined rule set includes the calculation of distances between the ego vehicle and a recognized object.

In a preferred embodiment, the trajectory is determined by means of a combination of curve interpolation and numerical optimization, with optimization based in particular on a gradient. For example, an optimization method can be used in which a greatest slope is followed iteratively.

In a special embodiment, the trajectory is determined by means of spline-based interpolation.

In a special embodiment, the recognized object is classified as a potential obstacle in the vicinity of the vehicle and a trajectory of the obstacle is calculated from the at least one object movement feature and the position of the object. Typical object movement features are instantaneous speeds in the x direction and / or y direction of the vehicle. The at least one object movement feature is preferably predicted both for the first-person vehicle and for an obstacle vehicle for the entire prediction horizon. In particular, the obstacle and the trajectory of the obstacle are transferred to the environment model, in particular the dynamic component of the environment model.

In a special embodiment, a road geometry, a road marking and / or at least an obstacle, such as a pedestrian and / or another vehicle, an object speed and / or object movement direction are determined from the environment sensor data and transferred to the environment model.

The environment model is represented as a potential field, which is composed of a static and a dynamic part. The dynamic part provides an interpretable model based on physical considerations, taking into account vehicle kinematics. The novelty that distinguishes the developed approach from others lies in the fact that the environment potential field includes knowledge about the future development of the current situation through the use of trajectory planning and obstacle prediction, which at the same time safety distance to be observed. A fixed, oversized safety distance, without reference to the developing situation, will lead to unnecessarily high braking, whereas a predetermined, undersized safety distance leads to risky behavior. Due to its predictive character, the developed environment model significantly improves safety and comfort in complex traffic scenarios. The analysis shows that the developed environment model is fully suitable for highway scenarios. In addition, the environment model can be further developed by an advanced calculation of the braking distance so that it takes into account the road conditions, for example.

The trajectory of the vehicle and / or the obstacle can be calculated by a combination of curve interpolation and numerical optimization. This ensures efficient trajectory planning. The big advantage is the ability to find an optimal solution for different maneuvers even in a complex scenario.

The environment model can include a potential field with high potential for non-negotiable areas and low potential for negotiable areas. The environment model can be composed of a static and a dynamic potential field.

A static component of the environment model can be provided by polynomial road markings. In particular, the road marking is called a polynomial 3 , Order is transmitted to the trajectory planning module and evaluated at certain positions in order to determine distances between the ego vehicle and the road marking. The static environment model is preferably based on the determined distances. In order to combine both components of the environment model, the components can be transformed into a common potential field, so that a comprehensive environment model is formed.

In order to obtain a holistic description of the environment, both representations in particular are transformed into a potential field in such a way that they are combined to form a comprehensive environment model. This combines the environment model to carry out the task of trajectory planning for automated driving.

In particular, other objects, which may be obstacles when the trajectory is calculated, are approximated by a circular shape, as a result of which a predefined safety distance is maintained in order to take into account the spatial extent of the ego vehicle and the obstacle vehicle. The static environment is modeled using a distance to the respective road boundary.

In a preferred embodiment, an obstacle in the surroundings of the vehicle can be detected and a trajectory of the obstacle can be calculated. The obstacle and the calculated trajectory are transformed into the environment model, in particular into the dynamic component of the environment model.

By influencing the dynamic potential field, the information provided by the obstacle trajectory prediction is taken into account directly in the ego trajectory planning process. The term defined here is defined as defined with respect to the time within the prediction horizon of the trajectory planning algorithm, that is to say that the safety margins do not change over time during the planning phase.

A static component of the environment model or a static environment model can be provided by polynomial road markings. The dynamic component of the environment model or the dynamic environment model can be formed by an obstacle list. In order to combine both components of the environment model, the components can be transformed into a potential field in such a way that a comprehensive environment model is formed.

In order to obtain a holistic description of the environment, both representations can be transformed into a potential field in such a way that they are combined in a comprehensive environment model. This combined the environment model to enable the task of planning trajectories for automated driving.

• 1: Eine Darstellung des prädiktiven Charakters des Umfeldmodells, während das dynamische Potentialfeld mithilfe des Hindernistrajektorienprädiktionsansatzes aufgebaut wird;
• 2 ein statisches Potentialfeld, das die zwei unterschiedlichen Modelle für durchgezogene und gestrichelte Straßenmarkierungsarten zeigt, wobei zu Visualisierungszwecken Konturlinien auf die Straße projiziert sind;
• 3: eine Darstellung des dynamischen Potentialfelds, wobei ein potentielles Kollisionsrisiko gekennzeichnet ist;
• 4: das sich durch Superposition ergebende resultierende Umfeldpotentialfeld des statischen und dynamischen Potentialfelds;
• 5: eine Tabelle, die die Startbedingungen des Egofahrzeugs und der Hindernisfahrzeuge im analysierten Szenario wiedergibt;
• 6: die Momentansituation, dargestellt für jeden Zeitschritt, mit Bezug zum sich bewegenden Egofahrzeug, wobei zusätzlich zum Umfeldpotentialfeld die geplante Egotrajektorie dargestellt ist;
• 7: eine schematische Darstellung eines Assistenzsystems, das nach dem erfindungsgemäßen Verfahren arbeitet.

Further features, advantages and properties of the invention are explained on the basis of the description of preferred embodiments of the invention with reference to the figures, which show:

• 1 : A representation of the predictive character of the environment model, while the dynamic potential field is built using the obstacle trajectory prediction approach;
• 2 a static potential field which shows the two different models for solid and dashed road marking types, contour lines being projected onto the road for visualization purposes;
• 3 : a representation of the dynamic potential field, whereby a potential collision risk is identified;
• 4 : the resulting surrounding potential field of the static and dynamic potential field resulting from superposition;
• 5 : a table that shows the starting conditions of the ego vehicle and the obstacle vehicles in the analyzed scenario;
• 6 : the current situation, shown for each time step, with reference to the moving ego vehicle, whereby the planned ego trajectory is shown in addition to the environment potential field;
• 7 : A schematic representation of an assistance system that works according to the inventive method.

wobei $$\begin{array}{r}\hfill x_1=x_s.\\ \hfill f_k\left(x\right)=0.\\ \hfill h_k\left(x\right)\le 0.\end{array}$$

mit dem tatsächlichen Zustand x_s und der Gleichheitsrandbedingung f_k und den Ungleichheitsrandbedingungen h_k. Der Zielzustand x_g ist Teil der Optimierung. Es gibt z quadratische Funktionen y_i, die aus Objekten oder Zielen o_i zusammengesetzt sind, welche durch die Matrix Γ_i gewichtet werden $${\gamma }_i\left(B.{\mathrm{\Gamma }}_i\right)=o_i^T\left(B\right){\mathrm{\Gamma }}_i{o}_i\left(B\right).$$

As in 1 shown, the environment model adapts advantageously to the predicted future development of the current situation. For this purpose, a spline-based interpolation strategy is used in the planning step for the trajectory of the vehicle, that is, the ego trajectory. Trajectory planning is formulated for trajectory B as a non-linear program: $$\gamma *\left(B,{\mathrm{\Gamma }}_1\right)=\underset{B}{{\displaystyle \text{min}}}{\displaystyle \underset{i=1}{\overset{z}{\Sigma }}{\gamma }_i}\left(B,{\mathrm{\Gamma }}_1\right)$$

in which $$\begin{array}{r}\hfill x_1=x_s,\\ \hfill f_k\left(x\right)=\mathrm{0th}\\ \hfill H_k\left(x\right)\le \mathrm{0th}\end{array}$$

with the actual state x _s and the equality boundary condition f _k and the inequality boundary conditions h _k . The target state x _g is part of the optimization. There are z quadratic functions y _i , which are composed of objects or targets o _i , which are weighted by the matrix Γ _i $${\gamma }_i\left(B,{\mathrm{\Gamma }}_i\right)=O_i^T\left(B\right){\mathrm{\Gamma }}_i{O}_i\left(B\right),$$

The solution to the nonlinear problem formulated in equation 1 is given by the approximation of the hard boundary conditions. Therefore, the boundary conditions f _k are transformed into penalty terms by using the concept of soft boundary conditions. An efficient real-time capable solution is thereby achieved. Due to the use of a simplified vehicle dynamics description, equality constraints are not part of the optimization problem. Inequality constraints are replaced by $$\chi \left(H_k\right)=\text{Max}\left\{\left[\mathrm{0th}{H}_k\right]\right\},$$

Hence the total error vector e (B): B → ℝ ^{d err} $$\text{e}={\left[{\text{O}}_{\text{1}}.{\text{O}}_2....{\text{O}}_z.\chi \left(H_1\right).\chi \left(H_2\right).....\chi \left(H_{n-1}\right)\right]}^T,$$

mit dem Optimierungsvektor b der Dimension dopt und der GewichtsmatrixThe cost function can then be defined as follows $$\mathrm{\Phi }\left(b\mathrm{.\Omega }\right)=\text{e}{\left(\text{b}\right)}^{\text{T}}\mathrm{\Omega }\text{e}\left(\text{b}\right),$$

with the optimization vector b of the dimension dopt and the weight matrix

was zu einem nichtlinearen Kleinste-Quadrate-Problem führt, das iterativ mit dem Levenberg-Marquardt-Algorithmus gelöst wird. Nach jedem erfolgreichen Levenberg-Marquardt-Schritt wird die derzeitige Lösung aktualisiert durch $${\text{b}}^{+}=\text{b}+\Delta \text{b}\text{.}$$

Ω ∈ ℝ ^{d err} × ^{d err} , which contains the weights for the respective destinations in the sense of automated driving. The optimal solution for the transformed problem is given by $$\text{b}*=\text{bad}\underset{\text{b}}{{\displaystyle \text{min}}}\mathrm{\Phi }\left(\text{b},\mathrm{\Omega }\right).$$

which leads to a nonlinear least squares problem that is solved iteratively with the Levenberg-Marquardt algorithm. After each successful Levenberg-Marquardt step, the current solution is updated by $${\text{b}}^{+}=\text{b}+\Delta \text{b}\text{,}$$

The basic equation of the Levenberg-Marquardt algorithm is given by $$\left(\text{H}+{\mathrm{\lambda }}_{\text{LM}}\text{I}\right)\Delta \text{b}=\text{G}$$

With the damping factor λ _LM , the identity matrix I and the gradient $$\text{G}={\text{J}}^{\text{T}}\mathrm{\Omega }\text{e}\left(\text{b}\right),$$

The Jacobi matrix J ∈ ℝ ^{d err} × ^{d opt} used to approximate the Hesse matrix H ∈ ℝ ^{d opt} × ^{d opt} to calculate with $$\text{H}={\text{J}}^{\text{T}}\mathrm{\Omega }\text{J},$$

The requirement of a differentiable environment model representation is derived from equations (9) and (10). In the following, the earth and vehicle coordinate systems are identified by prefixed letters E and F. The prediction horizon is defined as T _p with constant time intervals t ∈ [t _k t _{k + 1} ]. With the States x _k = [F _{x k} , F _{y k} ] ^T and k = 1 ... n gives the ego vehicle trajectory as $$x_{eGO}={\left[x_1.t_1.x_2.t_2.....x_k.t_k.....x_n.T_p\right]}^T,$$

Before the above gradient-based trajectory planning is applied, the environment model is created as described below. The environment model is stored in a predetermined representation in a memory of a trajectory planning module or environment fusion module. The trajectory planning module or environment fusion module can be implemented on an electronic control unit or another computing unit on board the vehicle. The respective module is connected to other vehicle systems that provide sensor data information.

In a preferred embodiment, a potential field is chosen to represent the environment. This creates a differentiable environment model. Both the static and the dynamic environment model can be considered separately. However, they are calculated in a combined way to provide a holistic environment model. For the purpose of discussion and explanation, the properties of the developed static and dynamic environment model are illustrated using an example with three straight lanes and the ego and obstacle vehicle in the middle lane of the road (e.g. 2 ).

Static environment model

The static environment model can be viewed as an extended driving corridor that indicates the negotiable space, that is, the available lanes that allow the first-person vehicle to drive in the intended direction of travel. A maximum of three tracks are taken into account, so that, if available, the possible tracks are composed of the ego track and the right and left neighboring track.

This does not imply a limitation in performance for scenarios with more than three lanes, since there is still enough information to enable the first-person vehicle to perform complete basic maneuvers such as lane changing and lane keeping. Note that obstacles included in the dynamic environment model are also predicted regardless of the number of lanes detected. Information about the existing tracks is obtained by on-board camera sensors, the shape of each track being specified as a third-order polynomial per track marking. In order to generate the static potential field, the model type for each lane marking is selected depending on the type of lane marking, so that a distinction is made between lane marks that can be passed over and those that cannot be passed over. Appropriately, all lane marking types that the ego vehicle must not drive over are copied and marked as solid lane markings, whereas all lane marking types that can be driven over are marked as dashed lane markings.

zwischen den ^Fy-Koordinaten der Trajektorie und folgenden Spurmarkierungspolynomen I angenähert $$\Delta {\stackrel{⌣}{y}}_k^{\mathcal{l}=0}=+\left({}^{F}yk{-}^F{y}_k^{\mathcal{l}=1}\right),$$

$$\Delta {\stackrel{⌣}{y}}_k^{\mathcal{l}=1}=+\left({}^{F}yk{-}^F{y}_k^{\mathcal{l}=1}\right),$$

$$\Delta {\stackrel{⌣}{y}}_k^{\mathcal{l}=2}=-\left({}^{F}yk{-}^F{y}_k^{\mathcal{l}=2}\right),$$

$$\Delta {\stackrel{⌣}{y}}_k^{\mathcal{l}=3}=-\left({}^{F}yk{-}^F{y}_k^{\mathcal{l}=3}\right),$$

Lane markings are counted from left to right in the ego vehicle coordinate system F, starting with index I = 0 for the left lane marking of the left lane, index I = 1 for the left and index I = 2 for the right lane marking, and index I = 3 for the right Lane marking of the neighboring right lane. The polynomial is evaluated for F _Xk of each trajectory point and the distance to the road _boundary is determined by the distance $\Delta {\stackrel{⌣}{y}}_k^{\mathcal{l}}$

approximated between the ^F y coordinates of the trajectory and the following lane marking polynomials I. $$\Delta {\stackrel{⌣}{y}}_k^{\mathcal{l}=0}=+\left({}^{F}yk{-}^F{y}_k^{\mathcal{l}=1}\right).$$

$$\Delta {\stackrel{⌣}{y}}_k^{\mathcal{l}=1}=+\left({}^{F}yk{-}^F{y}_k^{\mathcal{l}=1}\right).$$

$$\Delta {\stackrel{⌣}{y}}_k^{\mathcal{l}=2}=-\left({}^{F}yk{-}^F{y}_k^{\mathcal{l}=2}\right).$$

$$\Delta {\stackrel{⌣}{y}}_k^{\mathcal{l}=3}=-\left({}^{F}yk{-}^F{y}_k^{\mathcal{l}=3}\right).$$

und mit einem gewünschten Abstand zur Spurmarkierung Λ_l führt dies zu $$h_{l,k}=\Delta y_k^{\mathcal{l}}+{\mathrm{\Lambda }}_{\mathcal{l}}.$$

Depending on the lane marking type $$\Delta y_k^{\mathcal{l}}=\{\begin{array}{c}\Delta y_k^{\mathcal{l}}\text{for a solid lane marking}\\ -\left|\Delta y_k^{\mathcal{l}}\right|\text{for a dashed lane marking}\end{array}$$

and with a desired distance to the lane marking Λ _l this leads to $$H_{l.k}=\Delta y_k^{\mathcal{l}}+{\mathrm{\Lambda }}_{\mathcal{l}},$$

The static potential field with the costs Φ _S is generated from the superposition of all χ (h _ℓ ) using equation (3), equation (4) and equation (5). Here h _{l is} the vectorized form of h _{l, k} . The resulting potential field for the static environment is in 2 shown.

Dynamic environment model

$$\Delta y_k^{\mathcal{l}}{=}^F{y}_k{-}^{F}y\mathcal{l}.$$

In order to take dynamic obstacles into account, the obstacles are _predicted for the trajectory prediction using a lane change detection and the predicted time until the lane change t ttlc. In the following the obstacle sizes are marked with ϱ. For ϱ = 1 ... ρ obstacles, the lateral and longitudinal distance between the ego and obstacle vehicle trajectories is calculated separately $$\Delta x_k^{\mathcal{l}}{=}^F{x}_k{-}^F{x}_k^{\mathcal{l}}.$$

$$\Delta y_k^{\mathcal{l}}{=}^F{y}_k{-}^{F}y\mathcal{l},$$

mit dem Parameter a für die maximale Bremsverzögerung. Allerdings ist bezüglich Gleichung 17 jeder andere gewünschte Abstand möglich. Die Relativgeschwindigkeit Δv^ϱ wird in Längsrichtung wie folgt berechnet $$\Delta v_{x,k}^{\mathcal{l}}{=}^F{x}_{x,k}{-}^{F}v{\mathcal{l}}_{x,k}^l$$

und in Lateralrichtung $$\Delta v_{y,k}^{\mathcal{l}}{=}^F{x}_{y,k}{-}^F{v}_{y,k}^{\mathcal{l}}.$$

For safety reasons too, the necessary distance to be met is given by the approximation of the braking distance $$\Delta b^{\mathcal{l}}=\mathrm{<figure-callout\; id="2"\; label="\Delta \; v\; l"\; filenames="DE102018123896A1\_0038.png"\; state="\{\{state\}\}">\Delta \; </figure-callout>}{v}^{\mathcal{l}}{\mathcal{}2}^{}/\left(2a\right)$$

with parameter a for the maximum braking deceleration. However, any other desired distance is possible with respect to equation 17. The relative speed Δv ^ϱ is calculated in the longitudinal direction as follows $$\Delta v_{x.k}^{\mathcal{l}}{=}^F{x}_{x.k}{-}^{F}v{\mathcal{l}}_{x.k}^l$$

and in the lateral direction $$\Delta v_{y.k}^{\mathcal{l}}{=}^F{x}_{y.k}{-}^F{v}_{y.k}^{\mathcal{l}},$$

und ein minimaler Lateralabstand ${\stackrel{⌣}{\Lambda }}_{lat}^{\mathcal{l}}$

zwischen dem Ego- und Hindernisfahrzeug, unter Berücksichtigung der räumlichen Ausdehnung der Fahrzeuge, jederzeit erfüllt ist. Die gemessene Richtung F_λϱ des jeweiligen Hindernisfahrzeugs wird verwendet, um den vorbestimmten minimalen gewünschten Sicherheitsabstand entsprechend der momentanen Situation zu drehen. Dies ergibt $${\mathrm{\Lambda }}_{lon}^{\mathcal{l}}=\text{max}\left\{\left|{\stackrel{⌣}{\Lambda }}_{lon}^{\mathcal{l}}\text{ cos}\left({}^F{\lambda }^{\mathcal{l}}\right)\right|,\left|{\stackrel{⌣}{\Lambda }}_{lat}^{\mathcal{l}}\text{ sin}\left({}^F{\lambda }^{\mathcal{l}}\right)\right|\right\}.$$

$${\mathrm{\Lambda }}_{lat}^{\mathcal{l}}=\text{max}\left\{\left|{\stackrel{⌣}{\Lambda }}_{lat}^{\mathcal{l}}\text{ cos}\left({}^F{\lambda }^{\mathcal{l}}\right)\right|,\left|{\stackrel{⌣}{\Lambda }}_{lon}^{\mathcal{l}}\text{ sin}\left({}^F{\lambda }^{\mathcal{l}}\right)\right|\right\}.$$

In the case of low relative speeds, a minimal longitudinal distance should apply ${\stackrel{⌣}{\Lambda }}_{lOn}^{\mathcal{l}}$

and a minimal lateral distance ${\stackrel{⌣}{\Lambda }}_{lat}^{\mathcal{l}}$

between the ego and obstacle vehicle, taking into account the spatial extent of the vehicles, is fulfilled at all times. The measured direction F _λ ϱ of the respective obstacle vehicle is used to turn the predetermined minimum desired safety distance in accordance with the current situation. This results in $${\mathrm{\Lambda }}_{lOn}^{\mathcal{l}}=\text{Max}\left\{\left|{\stackrel{⌣}{\Lambda }}_{lOn}^{\mathcal{l}}\text{cos}\left({}^F{\lambda }^{\mathcal{l}}\right)\right|.\left|{\stackrel{⌣}{\Lambda }}_{lat}^{\mathcal{l}}\text{sin}\left({}^F{\lambda }^{\mathcal{l}}\right)\right|\right\},$$

$${\mathrm{\Lambda }}_{lat}^{\mathcal{l}}=\text{Max}\left\{\left|{\stackrel{⌣}{\Lambda }}_{lat}^{\mathcal{l}}\text{cos}\left({}^F{\lambda }^{\mathcal{l}}\right)\right|.\left|{\stackrel{⌣}{\Lambda }}_{lOn}^{\mathcal{l}}\text{sin}\left({}^F{\lambda }^{\mathcal{l}}\right)\right|\right\},$$

$${\sigma }_{lat}^{\mathcal{l}}=\{\begin{array}{cc}{\mathrm{\Lambda }}_{lat}^{\mathcal{l}}& \text{sgn}\left(\Delta r_k^{\mathcal{l}}\Delta v_{x,k}^{\mathcal{l}}\right)>0\\ \text{max}\left\{\Delta b_{lat}^{\mathcal{l}},{\mathrm{\Lambda }}_{lat}^{\mathcal{l}}\right\}& \text{otherwise}\end{array},$$

The safety distance σ ^ϱ with respect to a respective obstacle ϱ is calculated using equation (17). For the direction-dependent component of the dynamic environment model, the algebraic sign is taken into account for the longitudinal and lateral direction. $${\sigma }_{lOn}^{\mathcal{l}}=\{\begin{array}{cc}{\mathrm{\Lambda }}_{lOn}^{\mathcal{l}}& \text{sgn}\left(\Delta r_k^{\mathcal{l}}\Delta v_{x.k}^{\mathcal{l}}\right)>0\\ \text{Max}\left\{\Delta b_{lOn}^{\mathcal{l}}.{\mathrm{\Lambda }}_{lOn}^{\mathcal{l}}\right\}& \text{otherwise}\end{array}.$$

$${\sigma }_{lat}^{\mathcal{l}}=\{\begin{array}{cc}{\mathrm{\Lambda }}_{lat}^{\mathcal{l}}& \text{sgn}\left(\Delta r_k^{\mathcal{l}}\Delta v_{x.k}^{\mathcal{l}}\right)>0\\ \text{Max}\left\{\Delta b_{lat}^{\mathcal{l}}.{\mathrm{\Lambda }}_{lat}^{\mathcal{l}}\right\}& \text{otherwise}\end{array}.$$

zwischen beiden Fahrzeugen erfüllt sein. Eine Gauss-Funktion wird verwendet, um das dynamische Potentialfeld zu modellieren, indem der Momentanabstand und der gewünschte Sicherheitsabstand berücksichtigt werden. Dadurch wird das Erfordernis eines differenzierbaren Umfeldmodells inhärent erfüllt $$d_{lon}^{\mathcal{l}}=e^{-\mathrm{0,5}{\left(\frac{\Delta x^{\mathcal{l}}}{{\sigma }_{lon}^{\mathcal{l}}}\right)}^2},$$

$$d_{lat}^{\mathcal{l}}=e^{-\mathrm{0,5}{\left(\frac{\Delta x^{\mathcal{l}}}{{\sigma }_{lat}^{\mathcal{l}}}\right)}^2},$$

In the event that the ego and the obstacle vehicle move away from each other, only the minimum distance is required ${\mathrm{\Lambda }}_{lOn/lat}^{\mathcal{l}}$

be met between the two vehicles. A Gaussian function is used to model the dynamic potential field by taking the instantaneous distance and the desired safety distance into account. As a result, the requirement of a differentiable environment model is inherently met $$d_{lOn}^{\mathcal{l}}=e^{-0.5{\left(\frac{\Delta x^{\mathcal{l}}}{{\sigma }_{lOn}^{\mathcal{l}}}\right)}^2}.$$

$$d_{lat}^{\mathcal{l}}=e^{-0.5{\left(\frac{\Delta x^{\mathcal{l}}}{{\sigma }_{lat}^{\mathcal{l}}}\right)}^2}.$$

Based on the properties of the Gaussian function, the combination of longitudinal and lateral potential costs $$H_{\mathcal{l}.k}=d_{lOn}^{\mathcal{l}}{d}_{lat}^{\mathcal{l}},$$

The costs Φ _D , which represent the dynamic potential field, are vectorized with h _{ϱ, k} for each λ ^{(h ρ )} generated analog to the static potential field. The characteristic of the dynamic potential field in relation to a difference in orientation is given in 3 shown. The safety distance is adjusted based on the relative speed in the longitudinal and lateral directions.

As in 3 (a) and (b) shown, the first-person vehicle runs at a speed of 120 km / h. In 3 (a) the obstacle vehicle has a speed of 85 km / h with a heading angle of F _{λ ρ = 1} = 0 °. In 3 (b) the obstacle vehicle has 85 km / h with a heading angle of F _{λ ρ = 1} = -13 °. The dynamic potential field is formed for each predicted point in time in order to take into account the predicted relative speeds at that time. This makes it possible to take into account the varying safety distances depending on the situation.

Environment potential field

entsprechend gewählt werden. Aufgrund seiner Charakteristiken kann das entwickelte Umfeldmodell als prädiktives Umfeldpotentialfeld Φ_DS beschrieben werden.The resulting environment model results from the superposition of the static and dynamic potential fields. The static potential field is divided into negotiable and non-negotiable space based on the street geometry, whereas the dynamic potential field represents a potential risk of collision with dynamic obstacles. In 4 the combination of the static potential field (cf. 2 ) and the dynamic potential field with the obstacle vehicle, which is aligned with the first-person vehicle (cf. 3 (a) shown. Since both the static and the dynamic potential fields both influence the resulting trajectory, a compromise must be found that balances the implementation of lane keeping and lane change maneuvers. This can be achieved by using the parameters for the trajectory planning approach (e.g. the weight matrix Ω) and the parameters of the environment model (e.g. the desired distance to the respective lane marking Λ _I and the minimum safety distance ${\stackrel{⌣}{\Lambda }}_{lOn/lat}^{\mathcal{l}})$

be chosen accordingly. Due to its characteristics, the developed environment model can be described as a predictive environment potential field Φ _DS .

In the in the 1 to 6 illustrated embodiment, sensor data are provided by camera and radar sensors. The first-person vehicle is equipped with radar sensors on the front, rear and sides as well as a front-facing camera. This provides lane marking information and merged information about obstacle vehicles. A reference position is generated by the trajectory planning approach and Receding Horizon Control (RHC) is also applied. The results are shown for straight lanes, but the developed environment model is also valid for curved road scenarios.

In order to understand the characteristics of the environment model in cooperation with the optimization-based trajectory planning approach in the examples of 1 to 6 shows a section of the motorway with three lanes 3.75 m wide and two obstacle vehicles. The initial conditions at time t = 0 s are in 5 (Table 1) shown. During the simulation, the vehicle speeds can change due to the implemented individual driving behavior of the road users. 6 shows the results achieved by the presented approach in steps of 1.5 s starting with t = 0.6 s. At this time, the first-person vehicle and the obstacle vehicle are 1 in the far right lane. The ego vehicle moves faster than the obstacle vehicle 1 and the dynamic potential field is shaped according to the relative speed of about 35 km / h. In the further course, obstacle vehicle overtakes 1 the slower obstacle vehicle 2 , With regard to the first-person vehicle, two dynamic potential fields overlap. The safety distance results from the relative speed in such a way that for the obstacle vehicle 1 a smaller safety distance is valid than for the obstacle vehicle 2 , Like the obstacle vehicle 1 the ego vehicle also makes a lane change to the left around the obstacle vehicle 2 to overtake.

The first-person vehicle, which is supported by the predictive potential field, can immediately trigger an optimal follow-up maneuver. By making another lane change to the left, the first-person vehicle overtakes the obstacle vehicle 1 keeping the desired speed. During the maneuver, the shape of the dynamic potential field is mainly oriented in the longitudinal direction due to only slight deviations in orientation. The static potential field is mainly visible on the roadsides. Nonetheless, it is apparent that the static field enables precise tracking by taking track dimensions into account using the dashed lane marking model applied. This in 6 The result shown clearly shows the dependency of the environment model on sound sensor data. Lane markings are only detected by the camera sensor, which could lead to limited lane marking detection due to occlusion or a sensor field of view that is too narrow. Within the environment model, the field of view of a respective lane marking is taken into account in such a way that no costs are generated if the field of vision is exceeded. In future work, difficulties arising from small areas of vision can be solved by using map data.

In 6 it must be determined that the ego trajectory that intersects the potential field does not indicate a collision, since the surrounding potential field is only shown for the current time step. However, based on the predicted situation, the developing potential field is directly included in the trajectory planning.

In 7 A driver assistance system is shown schematically, which works according to the method according to the invention. The Ego Trajectory Planning Module (ETP) 200 receives driving target specifications and environment sensor data via an interface 150 , Such driving target specifications are, for example, a target position, which is provided by a navigation module 140 provided. As a environment sensor data from a lane detection module 100 Road markings as polynomials 3 , Grades transmitted to the ego trajectory planning module. Other systems, such as one, can also be connected to the ego trajectory planning module 200 Transfer data through predetermined rule sets into the environment model 220 be transformed. In the examples described above, at least lane markings are made by the lane detection module 100 to the ETP 200 transmitted as well as object positions and their object movement characteristics, such as instantaneous speed and direction. The ETP 200 has a predefined set of rules, which, as further described in detail, specifies with which calculations from the transmitted road markings the static environment model 222 is to be determined. The ETP 200 processes the rule set described in the section Static environment model and thus creates the static environment model 222 , In the same way, as in the section Dynamic Environment Model, the detected objects and their object movement characteristics become the dynamic environment model 224 determined. Finally, the ETP determines 200 through superposition of the environment models 222 and 224 an overall environment model 220 , which reflects the overall driving situation. The environment models 222 and 224 and the overall environment model 220 are calculated for the entire prediction horizon. With the help of the optimization method described above, a trajectory is created 250 determined for the entire prediction horizon.

The trajectory determined 250 is connected to a driving control module 300 transmitted, which controls the vehicle according to the specified trajectory. The environment models 222 and 224 and are continuously updated after the completion of the trajectory calculation with the currently available objects and object movement characteristics and the optimization is repeated, so that a new trajectory is within the prediction horizon of the first trajectory planning, in particular after less than 20%, preferably less than 10% of the prediction horizon 250 to the driving control module 300 is transmitted.

An advantage of one or more embodiments is that a general representation of the environment is obtained without including the desired behavior in the environment model. Another advantage of one or more versions is that the safety distance between the vehicle for which the trajectory is planned (ego vehicle or ego) and other vehicles that can represent obstacles (age) is calculated in accordance with the predicted situation as a function of relative speed and relative orientation becomes. The environment model thus adapts to the predicted future development of the current situation.

LIST OF REFERENCE NUMBERS

100

Lane detection module

110

A spacer module

120

Lane Change Assistance Module

130

Ride comfort module

140

navigation module

150

interface

200

Egotrajektorienplanungsmodul

220

Total environmental model

222

static environment model

224

dynamic environment model

250

trajectory

300

Driving control module

Claims (10)

Method for operating an at least partially automated vehicle, in which: a) at least one object in the surroundings of the vehicle is identified from environmental sensor data and an environmental model of the vehicle is created, an object position and at least one object movement feature being ascertained for the identified object and transferred to the environmental model, b) a trajectory for the vehicle is calculated from the environmental model using an optimization method, and c) the vehicle is driven according to the calculated trajectory. Procedure according to Claim 1 , in which the environment model comprises a static and a dynamic environment model component. Procedure according to Claim 2 , in which the dynamic environment model component is based on a physical interpretation of a required safety distance. Procedure according to Claim 2 or 3 , in which the static environment model component contains boundary conditions which are determined by static objects in the environment of the vehicle. Method according to one of the preceding claims, in which the object position and the at least one object movement feature are used to parameterize a predetermined rule set in order to create the environmental model. Procedure according to Claim 5 , wherein the predetermined set of rules includes calculating distances between the ego vehicle and a detected object. Method according to one of the preceding claims, in which the trajectory is determined by means of a combination of curve interpolation and numerical optimization, optimization in particular being gradient-based. Method according to one of the preceding claims, in which the trajectory is determined by means of spline-based interpolation. Method according to one of the preceding claims, in which the object is a potential obstacle in the vicinity of the vehicle and a trajectory of the obstacle is calculated from the at least one object movement feature. Method according to one of the preceding claims, in which a road geometry, a road marking and / or at least an obstacle, such as a pedestrian and / or another vehicle, an object speed and / or object movement direction are determined from the environment sensor data and transmitted to the environmental model.

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