Patent ID: 12252119

In the figures, corresponding elements have the same reference signs. The discussion of same reference signs in different figures is omitted where possible without adversely affecting comprehensibility.

DETAILED DESCRIPTION

The method according to the first aspect provides an advantageous solution to the aforementioned problem. The program according to a second aspect, the assistance system according to a third aspect, and the vehicle according to a fourth aspect provide further advantageous solutions to the problem. The dependent claims define further advantageous embodiments of the invention.

The method according to an embodiment comprises in the step of planning the trajectories,a step of planning the trajectories of the ego-agent for each combination of a behavior of the ego-agent with each of the behaviors of the at least one other agent.

According to an embodiment of the method, a risk value used in the step of planning the trajectories, corresponding to the planned trajectory with the highest similarity to the actual trajectory is a perceived risk value.

The method according to an embodiment comprises in the step of planning the trajectories, predicting the trajectories, by a path prediction module, based on a process for kinematic expansion.

According to an embodiment of the method, in the step of predicting the possible behaviors of the at least one other agent, the method comprises predicting the planned trajectories, by a prediction model of the assistance system, by approximating a perceived behavior of the at least one other agent based on the sensed environment.

The method according to an embodiment comprises in the step of planning the trajectories of the ego-agent, a step of planning, by a planning model of the assistance system, to determine for each possible behavior of the at least one other agent, an optimal plan for the ego-agent, wherein the optimal plan is optimized with respect to at least one of the criteria of a safe plan, an efficient plan and a comfortable plan, and planning the trajectories of the ego-agent based on the determined optimal plan.

According to an embodiment of the method, the assistance system is a road traffic driving assistance system that includes at least one of a lane change assistant, a cruise control system, an intersection assistant.

The method according to one embodiment further comprises determining a perceived risk value based on the perceived situation, and determining a risk perception error of the ego-agent based on a difference between the perceived behavior of the at least one other agent and an actual behavior of the at least one other agent.

According to an embodiment of the method, the method further comprises determining whether to generate a warning signal configured to warn on the determined risk perception error and outputting the generated warning signal to the assistance system.

The method according to an embodiment determines to generate and output the warning signal in case of a difference between the perceived risk and the actual risk exceeding a threshold.

According to an embodiment of the method, the method outputs the warning signal including an indication of the risk perception error to an operator of the ego-agent.

The method according to an embodiment has the risk perception error including at least one of a trajectory prediction error, a dynamics estimation error, and an entity perception error.

The program according to the second aspect, the assistance system according to a third aspect, and the vehicle according to a fourth aspect provide corresponding advantageous solutions as discussed with regard to the embodiments of the method according to the first aspect.

The vehicle may be at least one of a road vehicle, a maritime vehicle, an air vehicle, a space vehicle, an autonomously operating vehicle, and a micromobility vehicle.

The term micromobility vehicle refers to a range of lightweight vehicles of small dimensions and operating velocities typically of 25 km per hour or less. Micromobility vehicles may be driven by their users personally and include bicycles, e-bikes, electric scooters, electric skateboards, and electric pedal-assisted bicycles (pedelecs). The definition of micromobility vehicles (micromobility devices) may include devices with a gross vehicle weight below 500 kg and maximum velocities below or including 45 km/h, and exclude devices with internal combustion engines.

The assistance system according to one aspect may be included in a portable device including a human-machine interface and carried by a user. The assistance system is configured to assist the user by outputting a perceivable signal via the human-machine interface to the user based on the generated control signal.

FIG.1shows a simplified flowchart of a method according to an embodiment.

The computer-implemented method is tailored for use in an assistance system for determining a perceived situation perceived by an ego-agent1. The ego-agent1may in particular be a road vehicle moving in a traffic environment, in which at least one, regularly a plurality of other agents is present, in particular moving. The other agents may include other traffic participants, e.g. pedestrians, cyclists, cars, trucks, buses, and tramways.

The assistance system may assist a human operator (driver) of the ego-agent1(ego-vehicle), e.g. by recommending actions or issuing alerts and warning signals determined to be suitable in the traffic scenario in the environment of the ego-agent1.

Alternatively or additionally, the assistance system is configured to at least partially autonomously operate the ego-agent1, e.g. by executing actions determined to be suitable in the traffic scenario in the environment of the ego-agent1. The actions may include performing lateral or longitudinal control of movement of the ego-agent1in the environment. In particular, the actions may include accelerating or decelerating the ego-agent1, maintaining a current velocity of the ego-agent1, amending a steering angle of the ego-agent1using at least one actuator, e.g., driving means such as a motor, brakes, or a steering assembly of the ego-agent1.

The method comprises a step S1of sensing the environment of the ego-agent1. In the sensed environment, at least one other agent2is present. The ego agent1comprises at least one sensor, which acquires sensor data from the environment of the ego-agent1and generates sensor data for further processing in processing equipment including at least one processor arranged at least one of locally of the ego-agent1, or remotely in server installations.

The subsequent steps S2to S7are essentially processing steps performed by the processor and performed based on the sensor data provided by the at least one sensor in the sensor signal.

The method proceeds with step S2that includes predicting possible behaviors of the at least one other agent2based on the sensed environment, in particular based on the sensor signal acquired from the at least one sensor.

In step S3, the method proceeds with planning trajectories of the ego-agent1for each possible behavior of the at least one other agent2to generate a set of planned trajectories. Each planned trajectory included in the set of planned trajectories corresponds to a behavior option for the ego-agent1and a predicted behavior of the at least one other agent2.

In step S4, the method determines an actual trajectory of the ego-agent1. The method determines the actual trajectory of the ego-agent1based on the sensor signal acquired from the at least one sensor via the sensor signal.

The at least one processor may perform steps S2, S3on the one hand and step S4on the other hand either sequentially, or at least partially in parallel.

In step S5succeeding to step S3and step S4, method proceeds with a step of comparing the determined actual trajectory of the ego-agent1with each of the planned trajectories of the ego-agent1.

In step S6, the method determines a planned trajectory from the set of planned trajectories with a largest similarity to the actual trajectory of the ego-agent1. The planned trajectory with the largest similarity to the actual trajectory of the ego-agent1may be determined as the particular planned trajectory included in the set of trajectories that runs closest in the environment in time and space with the actual trajectory.

The method may include calculating a similarity measure for the actual trajectory and each of the planned trajectories and determine the planned trajectory with the largest similarity as the planned trajectory, which corresponds to the highest calculated similarity measure.

Subsequently, the method proceeds to step S7and determines a perceived situation as perceived by the ego-agent1in the current situation in the environment based on a particular predicted possible behavior of the predicted possible behaviors corresponding to the planned trajectory with the largest similarity.

In step S8, the assistance system executes an action based on a generated and output control signal, or a sequence of actions in the assistance system based on the determined perceived situation as perceived by the ego-agent1.

The action triggered by the control signal may include generating a warning and providing the warning to the operator of the ego-agent1via a human-machine interface of the assistance system in order to make him aware of a wrong perception of the situation in the environment of the ego-vehicle1.

A wrong perception of the driver can result from perception errors of the ego-agent1. A wrong perception of the ego-agent1, e.g. a human driver, can result from wrong assumed predictions. Wrong assumed predictions may include a wrong trajectory prediction, such as predicting that the at least one other agent2will make a lane change but that the other agent2actually intends to stay on its current lane.

A wrong perception of the ego-agent1can result from a wrong assumed dynamics estimation. Wrong assumed dynamics estimations may include, e.g., a wrong current position or a wrong current velocity estimation of the ego-agent1, in particular the driver of the ego-agent1, e.g., assuming the other agent2is driving faster than the other agent2is actually driving.

Perception errors may include not being aware of the presence of the other agent2in the environment of the ego-agent1, or not considering another agent2, although the other agent2is actually present in the environment of the ego-agent1.

FIG.2provides a first schematic functional overview further illustrating the method according to an embodiment.

The environment includes a section of a road with two lanes for traffic into a same direction. The traffic situation in the environment of the ego-agent1(ego-vehicle) includes one other agent2(other vehicle) that is driving on a right lane with a first velocity. The ego-agent1moves on the left lane with a second velocity larger than the first velocity on a trajectory3(ego trajectory). The ego-agent1is approaching the other agent2from behind. Predicting a further evolvement of the current traffic situation provides different possible evolvements of the traffic situation.

In a first predicted evolvement of the current traffic situation, the other vehicle2changes to the left lane, e.g. due to another vehicle not shown cruising with a low velocity on the right lane ahead of the other vehicle2or intending to make a left turn at a next intersection.FIG.2shows this possible evolvement of the traffic situation for the other agent2proceeding on a first trajectory4and cutting in ahead of the ego-agent1on the left lane.

In a second predicted evolvement of the current traffic situation, the other vehicle2may continue driving on the right lane, thereby proceeding on a second trajectory5.

Executing steps S2and S3of the method according to an embodiment determines alternative behaviors (behavior options) for the ego-agent1based on the sensed situation in the environment.FIG.2shows two planned behaviors for the ego-agent1as examples. The steps S2and S3may be performed using a cooperative behavior planning process, e.g. using a behavior-planning tool such as risk maps discussed with reference toFIGS.8and9.

A first behavior option6predicts the other agent2to proceed on the trajectory4and to cut in in front of the ego-agent1. The first behavior option6includes a trajectory3.1of the ego agent1with a reduced (first) velocity of the ego-agent1in order to avoid a collision between the ego-agent1and the other agent2changing to the lane of the ego-agent1directly in front of the ego-agent1.

A second behavior option7predicts the other agent2to proceed on the trajectory5and to continue driving on the right lane. The second behavior option7includes a trajectory3.2of the ego-agent with an increased (first) velocity of the ego-agent1in order to pass the other agent2as early as possible, for example with the intention to enable the other agent2changing to the lane of the ego-agent1after being passed by the ego-agent1.

The illustrated set of planned trajectories for the ego-agent1inFIG.2includes a first planned trajectory8corresponding to the first behavior option6and a second planned trajectory9corresponding to the second behavior option7.

The method determines in step S4an actual behavior of the ego-agent1. Determining an actual behavior of the ego-agent1includes determining an actual trajectory10of the ego-agent1.

The steps S5and S6of the method compares the actual trajectory10driven by the ego-vehicle1with each of the planned trajectories included in the set of planned trajectories. The comparison of the actual trajectory10and each of the planned trajectories8,9may include comparing predicted locations of the ego-vehicle1on the actual trajectory10and each of the planned trajectories8,9for the same point in time during a planning horizon.FIG.2illustrates a pointwise comparison by showing three locations of the ego-vehicle1for corresponding point in times in each of the trajectories8,9,10.

The illustrated example ofFIG.2shows a reasonable similarity between the actually driven trajectory10and the planned trajectory8. Thus, performing step S7, the method determines based on the determined similarity of the actual trajectory10and the planned trajectory8that the ego-agent1executes the first planned behavior6. The first planned behavior6represents the perception of the ego-agent1of the current situation and the presumed evolvement of the current traffic situation in the environment of the ego-agent.

Additionally, using a cooperative behavior planning process, e.g. the behavior-planning tool risk maps, comparing the actual trajectory10and the planned trajectory8enables to determine a perceived risk value describing a risk as perceived by a user in the current situation and the presumed evolvement of the current traffic situation in the environment from the point of view of the ego-agent1.

FIG.3provides a second schematic functional overview over the method according to an embodiment.

The environment and the traffic situation in the environment of the ego-agent bases on the environment and the situation ofFIG.1. The environment includes the section of the road with two lanes for traffic into the same direction. The traffic situation in the environment includes the ego-agent1moving on the left lane on the ego trajectory3, and one other agent2driving on the right lane. Predicting a further evolvement of the current traffic situation provides different possible evolvements of the traffic situation as discussed with reference toFIG.1.

Performing steps S1to S7of the method determines based on the determined similarity of the actual trajectory10of the ego-agent1and the planned trajectory8that the ego-agent1executes the first planned behavior6. The first planned behavior6represents the perception of the ego-agent1of the current situation and the presumed evolvement of the current traffic situation in the environment of the ego-agent. Based on the perceived situation as perceived by the ego-agent1, the method ofFIG.2performs a step S8for determining the perceived risk model11.

Furthermore,FIG.2shows the one other agent2driving on the right lane on a determined actually confirmed, e.g., observed trajectory corresponding to an actual behavior of the other agent. A prediction module of the assistance system may determine the actually confirmed trajectory and determine an actual behavior (other behavior) of the other vehicle2based thereon.

The embodiment of the method ofFIG.3depicts a step S9for determining whether a warning to the ego-agent1, in particular to an operator of the ego-agent1or a similar action is required. Step S9may compare the determined perceived risk model11with the determined actual risk model12.

In particular, in step S9, a perceived risk value for the situation may be computed based on the determined perceived risk model11. Step S9further includes computing an actual risk value for the situation based on the determined actual risk model13. Step S9compares the computed perceived risk value and the computed actual risk value. If the comparison provides the result that the perceived risk value and the actual risk value differ, the system may conclude that an action, e.g., a warning14of the operator of the ego-agent1may be necessary.

The system may determine that the action is required in case a difference between the perceived risk value and the actual risk value exceeds a predetermined threshold value.

The determined action may include the warning. Additionally, the action may include an information of the operator of a determined perception error.

Information on the determined perception error may include highlighting a determined source of the determined perception error.

FIG.4provides an overview over a vehicle representing an ego-agent1configured for applying the method according to an embodiment. The ego-agent1ofFIG.4corresponds in its features to the ego-vehicle as disclosed in US 2020/231149 A1.

In particular,FIG.4illustrates a road vehicle as one particular embodiment of the ego-agent1in a side view. The ego-vehicle is equipped with the system for assisting a person as driver in operating the ego-vehicle1. The assistance system may provide assistance by outputting information, e.g., warning signals or recommendations on actions, to the assisted persons in situations with respect to other agents, here other traffic participants, and in the form of autonomously or at least partially autonomously operating the ego-vehicle1.

The ego-agent1may be any type of road vehicle including, but not limited to, cars, trucks, motorcycles, busses, and reacts to other agents2as other traffic participants, including but not limited to, pedestrians, bicycles, motorcycles, and automobiles.

The ego-agent1shown inFIG.4includes a plurality of sensors, including a front RADAR sensor15, a rear RADAR sensor16and plural camera sensors17,18,19,20for sensing the environment around the ego-agent1. The plurality of sensors is mounted on a front surface of the ego-agent1, a rear surface of the ego-agent1, and the roof of the ego-agent1, respectively. The camera sensors17, . . . ,20preferably are positioned so that a 360° surveillance area around the ego-agent1is possible. Thus, the ego-agent1is capable to monitor the environment of the ego-agent1.

Alternatively or in addition, further sensor systems, e.g. a stereo camera system or a LIDAR sensor can be arranged on the ego-agent1.

The ego-agent1further comprises a position sensor21, e.g., a global navigation satellite system (GNSS) navigation unit, mounted on the ego-agent1that provides at least position data that includes a location of the ego-agent1. The position sensor21may further provide orientation data that includes a spatial orientation of the ego-agent1.

The driver assistance system of the ego-agent1further comprises at least one electronic control unit (ECU)24and a computer23. The computer23may include at least one processor, e.g. a plurality of processors, microcontrollers, signal processors and peripheral equipment for the processors including memory and bus systems. The computer23receives or acquires the signals from the front RADAR sensor15, the rear RADAR sensor16, the camera sensors17, . . . ,20, the position sensor21, and status data of the ego-agent1provided by the at least one ECU24. The status data may include data on a vehicle velocity, a steering angle, an engine torque, an engine rotational speed, a brake actuation, for the ego-agent1, which may be provided by the at least one ECU10.

FIG.5shows a schematic structural diagram illustrating functional elements of the computer9of a driver assistance system according to an embodiment. The functional description of the computer9of the ego-agent1ofFIG.5corresponds in its features to the ego-vehicle as disclosed in US 2020/231149 A1.

An already existing computer23including at least one processor or signal-processor used for processing signals of an assistance system, e.g. an adaptive cruise control, of the ego-agent1, may be configured to implement the functional components described and discussed below. The depicted computer23comprises an image processing module25, an object classification module26, an object database27, a priority determination module28, a map database29, a planning module30, and a behavior determination module31.

Each of the modules is implemented in software that is running on the at least one processor or at least partially in dedicated hardware including electronic circuits.

The image processing module25receives the signals from the camera sensors17, . . . ,20and identifies relevant elements in the environment including but not restricted to a lane of the ego-vehicle1(ego lane), objects including other agents in the environment of the ego-vehicle1, a course of the road, and traffic signs in the environment of the ego-agent1.

The classification module26classifies the identified relevant elements and transmits a classification result to the planning module30, wherein at least the technically feasible maximum velocity and acceleration of another vehicle identified by the image-processing module25and assessed as relevant by the planning module30are determined based on the object database27.

The object database27stores a maximum velocity (speed) and an acceleration for each of a plurality of vehicle classes, e.g. trucks, cars, motorcycles, bicycles, pedestrians, and/or stores identification information (type, brand, model, etc.) of a plurality of existing vehicles in combination with corresponding maximum velocity and acceleration.

The priority determination module28individually determines a priority relationship between the ego-agent1and each other agent2(traffic participant) identified by the image-processing module25and involved in the current traffic situation under evaluation by the prediction module16. The traffic situations may be classified into at least two categories by the priority determination module28:

In a longitudinal case, the ego-agent1and the other agent2drive on the same path or lane and in a same direction, e.g., one vehicle follows the other one and a lateral case, in which, at the current point in time, the ego-agent1and the other agent2do not follow the same path, but the future paths intersect or merge within a prediction horizon of the assistance system. Thus, currently the moving directions of the ego-agent1and the respective other agents2are different. Exemplary scenarios in the road traffic environment could be road intersections, merging lanes, and more.

In a lateral case, the priority determination module28determines whether the ego-agent1has right of way over the other agent2, or the other agent2has the right of way over the ego-agent1based on the lane, a location of the other agent2, the course of the road, and/or the traffic signs identified by the image processing module25. Alternatively or in addition, the priority determination module28performs the determination based on a position signal of the position sensor21and map data from the map database29that includes information on applicable priority rules for the road network.

The planning module30calculates at least one hypothetical future trajectory for the ego-agent1based on the status data received from the ECU24, the information received from the image-processing module25, the signals received from the front RADAR sensor15and the rear RADAR sensor16, and, in case of the ego-agent1autonomously driving, information on the driving route that is defined by driving task. The calculated future trajectory indicates a sequence of future positions of the ego-agent1.

The planning module30selects a prediction model for the other traffic participant depending on the priority relationship determined by the priority determination module28. Further, the maximum velocity and acceleration may be determined by the classification module26, wherein, when the ego-vehicle1and the other agent2follow the same path, the selected prediction model defines a constant velocity over the prediction horizon. This means that for a prediction performed based on a current situation, the other agent2is assumed to move further with a constant velocity for a time interval corresponding to the prediction horizon.

On the other hand, when the trajectory of the ego-agent1and the trajectory of the other agent2intersect or merge, a prediction model that defines a delayed change of velocity is selected. In particular, a delayed decrease of velocity as the delayed change of velocity is set, if the ego-agent1has right of way over the other agent2. A delayed increased of velocity as the delayed change of velocity is defined in the prediction model that is selected if the other agent2has right of way over the ego-agent1.

In order to determine a suitable or best behavior for the ego-agent1, the planning module30may calculate a plurality of trajectories for the ego-agent1(ego-trajectories) and select the ego-trajectory, which results in the best behavior relevant score, as disclosed in U.S. Pat. No. 9,463,797 B2, or iteratively change at least one of the ego-trajectory velocity profile to optimize the behavior relevant score.

The planning module30outputs information on the finally determined ego-trajectory (velocity profile) to the behavior determination module31. The behavior determination module31determines a behavior of the ego-vehicle1based on the information provided by the planning module30, generates corresponding driving control signals for executing the determined behavior by controlling at least one of acceleration, deceleration, and steering of the ego-agent1, and outputs the generated control signals to the ECU24.

Alternatively or in addition, the behavior determination module31may generate and output at least one of warning signals or information recommending actions for a person operating the ego-agent1. The ego-agent1may include a human-machine interface with suitable output means including, for example, loudspeakers for an acoustic output and display screens or head-up-displays for a visual output to the operator.

The assistance system executes the described processes and steps repeatedly and parameters of the selected prediction model are adapted to changes in the environment and to behavior changes of the ego-agent1and other agent2.

FIG.6illustrates a complex exemplary traffic situation including an ego-agent1and plural other agents2, which is characteristic for an urban traffic environment.

The complex traffic situation depicted inFIG.6illustrates the resulting complexity of the objective of deciding on a proper maneuver sequence for the ego-agent1. The proper maneuver sequence or behavior the ego-agent1includes selecting out of a plurality of behavior candidates, which result in respective trajectories4,5of the ego-agent1.

The traffic situation includes various sources of risk, e.g., collision risks and curvature risks. The potential ego-trajectory4has to take both a collision risk with the other agent2in the lower right of the figure and a curvature risk due to the lane change from the lane the ego-vehicle1is currently driving on to the right lane into account. Moreover, there exist plural behavior alternatives for the other agent immediately front of the ego-agent1on the same lane. A plurality of behavior alternatives result in plural predicted trajectories3.1,3.2which each result in different potential outcomes and different risk models.

Furthermore, the depicted traffic situation covers a plurality of regulatory items, e.g. the right of way at an intersection of a two roads, which includes apparently no traffic lights or signs. Thus, the regulatory items including traffic lights, the road signs, and the applicable traffic rules all heavily influence the traffic situation to be assessed and resolved by the assistance system. This applies in particular to the dense urban traffic environment that typically includes a plurality of agents, a variety of regulatory items and a plurality of risk sources as relevant elements at the same time. Further challenges in finding a behavior for the ego-agent1exist due to the evolvement of the traffic situation, which is particularly rapid in the urban environment, contrary to the slower scene evolvement encountered on overland routes, for example. The evolvement of the traffic situation requires a constant update of situation-aware planning and benefits in particular from the advantages introduced by taking the perceived risk from the perspective of the ego-agent1into account.

FIG.7illustrates two further exemplary traffic situations including plural agents.

WhileFIG.6shows a traffic situation, which illustrates the objective of finding a behavior for the ego-agent1to cope with the scenario posed by a complex traffic situation, the traffic situations ofFIG.7show the challenge posed by ordering problems. The upper and the lower portion ofFIG.7show respectively a road traffic scenario, in which the predicted behaviors of the ego-agent1and the other agent2result in respective predicted trajectories3,4, which result in a risk model that has a significant collision risk in an area32in the environment. In both cases, judging the perceived risk by the ego-agent1may prove advantageous in order to resolve the current situation, in which traffic rules fail in unambiguously supporting a solution to the ordering problem inherent in the traffic situations.

The method for determining a perceived risk model is, in particular, suitable to support planning problems for finding suitable behavior alternatives for mid-term planning problems and long-term planning problems. Mid-term planning problems are characterized by gradual changes of speed and lateral positions, solving the issue how to proceed. Long-term planning problems concern deciding the different outcomes of the “you or me first?” problem, solving the issue what to do.

FIG.8illustrates a simplified processing flow for planning a complex behavior of the ego-agent1to cope with a complex situation in the environment.

The simplified processing flow illustrates in particular the processing for planning a behavior that uses a representation of the perceived situation in the environment of the ego-agent1input to a cooperative behavior-planning module35(CoRiMa agent). The cooperative behavior-planning module35performs behavior planning based on a representation of the perceived environment including the ego-agent1and the at least one other agent2. In particular, the cooperative behavior-planning module35is a computer-implemented system that generates appropriate behavior options for the ego-agent to address the perceived situation for further evaluation. The cooperative behavior-planning module35generates appropriate trajectories for a comprehensive environment representation. The cooperative behavior-planning module35provides generalizable concepts for an efficient analysis. The cooperative behavior-planning module35performs a selection from the behavior options and predicted trajectories.

For evaluating trajectories, the cooperative behavior-planning module35may rely on a risk-mapping module36(risk maps core). The risk-mapping module36assigns a value to trajectories input to the risk-mapping module36based on an evaluation of at least one of a trajectory risk, e.g. a collision risk, a utility of the trajectory, and a comfort of the trajectory when performed. The risk-mapping module36thereby provides an evaluation of a behavior corresponding to the evaluated trajectory. The proposed method of determining a perceived risk model using the behavior-planning module35and the risk-mapping module36may be integrated into an analytic, interpretable, generalizable and holistic approach of analyzing the behavior alternatives and predicted trajectories benefitting from proven advantages or a risk analysis based on risk maps over existing other evaluation methods for trajectories and for behavior planning.

The cooperative behavior-planning module35and the risk-mapping module36may form part of an implementation of the planning module30and the behavior determination module31of the computer23of the ego-agent1.

The cooperative behavior-planning module35determines a selected behavior and appropriate trajectory for the ego-agent1in the current situation. The behavior determination module31may generate corresponding driving control signals for executing the determined behavior by controlling at least one of acceleration, deceleration, and steering of the ego-agent1, and outputs the generated control signals to the ECU24of the ego-agent1in order to execute the determined behavior.

FIG.9illustrates an example of a plurality of behavior options for the ego-agent1in a behavior planning system based on an implementation of the cooperative behavior-planning module35and the risk-mapping module36according toFIG.8.

Perceiving the environment to generate the representation and predicting behavior alternatives for the current situation in the environment of the ego-vehicle1provides a plurality of behavior options37,38,39(ego-behavior options) for evaluation in the risk-mapping module36.

Each behavior option37,38,39corresponds to a trajectory of the ego-agent1, which is analyzed with respect to an associated risk including a plurality of specific risk types of which some are discussed in more detail.

Analyzing the collision risk of the ego-agent1with one other agent2of plural other agents2may include computing a collision probability of that is proportional to a product of two Gaussian distributions and a modelled severity of the collision between the ego-agent1and the other agent2involved in the collision.

Other types of risks taken into account may include regulatory risk, e.g. speed limits, or a static object lateral risk.

Further aspects of each behavior option37,38,39, which may be taken into account include at least one of a utility and the comfort of the behavior option37,38,39. The utility and the comfort of the behavior option37,38,39may be determined by assessing a covered ground by the ego-agent1while moving along the trajectory of the behavior option37,38,39. Alternatively or additionally, the utility and the comfort of the behavior option37,38,39may be computed based on an acceleration, which is exerted on the ego-agent1while moving along the trajectory of the behavior option37,38,39. Alternatively or additionally, the utility and the comfort of the behavior option37,38,39may be computed based on a jerk, which is exerted on the ego-agent1while moving along the trajectory of the behavior option37,38,39.

Based on the analysis, the risk-mapping module36assigns an objective value to each ego-behavior option37,38,39. The objective value may be determined based on a total accumulated score computed by a survival analysis.FIG.9illustrates three exemplary types of risks contributing to the total accumulated score, the curvature risk, the collision risk, and the regulatory risk.

The ego-behavior options37,38,39are optimized based on a cost function corresponding to the objective value of each ego-behavior option37,38,39.

The optimized ego-behavior options37,38,39are provided to a subsequent selecting step, which selects the most suitable optimized ego-behavior options37,38,39. The selection may be a conditional selection, e.g. the selected ego-behavior options37,38,39is selected under the prerequisite that the predetermined event in the perceived situation is determined to occur. In case of a the conditional selection a most suitable optimized ego-behavior options37,38,39may be selected and at least one fallback optimized ego-behavior options37,38,39that works in the perceived situation is also selected and executed in case that the predetermined event is determined to occur.

Finally the selected ego-behavior37,38,39is output for execution to the ECU24.

The described embodiments show how the method for an assistance system may be applied to an intersection scenario and a lane change scenario. However, the assistance system may apply the method to further driving situations in a road traffic environment.

Moreover, the method may prove an advantageous addition to an assistance system that supports control of vehicles with an improved capability to handle an obstructed sensor coverage, using a risk measure for a collision between a virtual traffic entity and the ego-agent1as discussed in U.S. Pat. No. 10,627,812 B2 in detail.

Alternatively, the method may prove an advantageous addition to an assistance system that supports control of other types of vehicles than road vehicles, e.g. spacecraft, vessels or planes.

Alternatively, the ego-agent may be a pedestrian carrying a portable device that includes a human-machine interface for communicating with the pedestrian. The portable device includes an embodiment of the assistance system and is adapted to assist the user by outputting a perceivable signal via the human-machine interface to the user based on the generated control signal. The perceivable signal may include at least one of a visual signal, an audible signal or a tactile signal, which, e.g., issue a warning to the pedestrian user in case a misinterpretation of a current situation in the environment is determined.

Determining a perceived risk from the perspective of the ego-agent1is also an advantageous addition to an assistance system that has a capability to autonomously, or at least partially autonomously operate an ego-agent1in the environment.

All steps which are performed by the various entities described in the present disclosure as well as the functionalities described to be performed by the various entities are intended to mean that the respective entity is adapted to or configured to perform the respective steps and functionalities. In the claims as well as in the description the word “comprising” does not exclude the presence of other elements or steps.

The indefinite article “a” or “an” does not exclude a plurality. A single element or other unit may fulfill the functions of several entities or items recited in the claims. The mere fact that different dependent claims recite certain measures and features of the converter circuit does not exclude that a combination of these measures and features cannot be combined in an advantageous implementation.