GATEWAYS

The present invention relates to a method for controlling an ego vehicle in an environment of a plurality of gateways at least in part automatically, wherein a computing system of the ego vehicle is used to receive sensor data from at least one environmental sensor system and to determine a position of a first vehicle in the environment based on the sensor data and determine a first path connecting a position of the ego vehicle to the position of the first vehicle. The invention further relates to an electronic vehicle guidance system and to a computer program product.

In the context of autonomous driving or partially autonomous driving, path planning is usually performed based on lane markings on the road.

In certain situations, however, there may be no or not sufficient lane markings on the road to allow for a reliable path planning. This may, for example, be the case in the environment of toll gates or in other locations where a relatively small number of driving lanes is continuously transferred to a plurality of targets, such as gateways or toll gates, for example.

The website https://safecarnews.com/staging/hitachi-demonstrates-vehicle -with-11- function-autonomous-driving-ecu/ (retrieved on November 26, 2019) describes a traffic jam assist corresponding to a low speed adaptive cruise control function for driving on a highway. It has the ability to keep track of a vehicle ahead and makes it possible to drive autonomously around a highway toll gate, where there are no highway lines.

One drawback of this approach is that only a single path for the ego vehicle is available and it is strictly connected to the vehicle driving ahead of ego vehicle. Therefore, safety and driving comfort may be limited in case the path of the vehicle driving ahead is not optimal with respect to safety or comfort. Furthermore, the risk of road congestion may be reduced only to a very limited extent, since potentially available targets are not taken into account.

It is therefore an object of the present invention to provide an improved concept for controlling an ego vehicle in an environment of a plurality of targets at least in part automatically, which reduces the risk for road congestion and/or increases safety and/or driving comfort for the ego vehicle or its users.

According to the improved concept, this object is achieved by the respective subject matter of the independent claims. The subject matter of the dependent claims relate to further implementations and preferred embodiments.

The improved concept is based on the idea to determine, in addition to a first path leading from a position of the ego vehicle to a position of a first vehicle in the environment, for each target a respective second path connecting the position of the ego vehicle to a position of the respective target. The first path and the second paths are filtered according to predefined rules in order to select a path for the ego vehicle.

According to the improved concept, a method for controlling an ego vehicle in an environment of a plurality of targets at least in part automatically is provided. A computing system of the ego vehicle is used to receive sensor data from at least one environmental sensor system and to determine a position of a first vehicle in the environment based on the sensor data. The computing system is used to determine a first path connecting a position of the ego vehicle to the position of the first vehicle. The computing system is used to determine a respective second path for each target of the plurality of targets connecting the position of the ego vehicle to a position of the respective target. The computing system is used to evaluate the first path and each of the second paths according to a predefined set of selection rules. The computing system is used to select the first path or one of the second paths based on a result of the evaluation and to control the ego vehicle at least in part automatically to follow the selected path.

The environment of the targets can also be considered as an environment of the ego vehicle and/or of the first vehicle or further vehicles. The first vehicle is, in particular, located between the plurality of targets and the ego vehicle, in particular between each of the plurality of targets and the ego vehicle.

The targets may, for example, be understood as respective target positions or target regions for the ego vehicle to be driven to. For example, the targets may correspond to respective gateways, in particular toll gates, for example on a highway.

In other implementations, the targets may for example correspond to parking positions. In particular, at least a part of the environment, in particular a part of the environment located between the plurality of targets and the ego vehicle, has no lane markings or has only a reduced set of lane markings. In other words, the number of lane markings may be less than for road portions bordering said part of the environment.

The at least one environmental sensor system may comprise a first environmental sensor system of the ego vehicle and/or a second environmental sensor system of the first vehicle or of a second vehicle in the environment and/or a third environmental sensor system of an infrastructure device in the environment.

An environmental sensor system can be understood as a sensor system which is able to generate data or signals, which depict, represent or image the environment. In particular, an environmental sensor system may comprise one or more cameras, one or more lidar systems, one or more radar systems and/or one or more ultrasonic sensor systems.

Here and in the following, a position can be understood as a relative position with respect to the position of the ego vehicle, if not stated otherwise. The positions may for example dynamically change due to a possible motion of the ego vehicle and/or of the first or the second vehicle. In this case, the computing system is used to adapt the respective path or other quantities based on the positions, in particular repeatedly or continuously. The computing system may receive the positions of the targets from the targets themselves or from an infrastructure device in the environment, such as a radio transmitter device. In particular, the computing system may receive the positions of the targets via a communication interface of the ego vehicle, for example of the computing system, which may be implemented as a vehicle-to-infrastructure-, V2I-, interface.

That the ego vehicle is controlled at least in part automatically can be understood such that the ego vehicle is controlled fully automatically or fully autonomously, and, in particular, without a manual intervention or control by a driver or user of the ego vehicle being necessary. The vehicle conducts required steering maneuvers, braking maneuvers and/or acceleration maneuvers and so forth automatically. In particular, the vehicle may be implemented as a fully automated or fully autonomous vehicle according to level 5 of the SAE J3016 classification. Here and in the following, SAE J3016 refers to the respective standard dated June 2018.

The ego vehicle being controlled at least in part automatically can also be understood such that the vehicle is controlled by an advanced driver assistance system, ADAS, in particular of the computing system, assisting a driver for partially automatic or partially autonomous driving. In particular, the ego vehicle may be implemented as a partially automatic or partly autonomous vehicle according to one of levels 1 to 4 of the SAE J3016 classification.

Apart from the selected path, one or more further paths of the first path and the second paths may be selected by the computing system based on the result of the evaluation.

The computing system may then control the ego vehicle at least in part automatically to follow one of the selected paths.

By means of the improved concept, the ego vehicle can be controlled in environments without relying on any lane markings on the road, partly or fully automatically. Therein, the control is not restricted to a single path following a further vehicle driving ahead. Therefore, the level of comfort for the user of the ego vehicle as well as traffic safety may be improved. Furthermore, since the computing system chooses from several paths, the traffic may be distributed more evenly amongst the available targets, which leads to a reduced risk for road congestion.

According to several implementations of the method according to the improved concept, the first path is selected based on the result of the evaluation, only if the first vehicle drives at a longitudinal velocity, which is smaller than a predefined maximum velocity.

According to several implementations, the computing system is used to determine respective positions of at least one further first vehicle in the environment based on the sensor data and to determine for each of the further first vehicles a respective further first path connecting the position of the ego vehicle to the respective position of the respective further first vehicle. The evaluation includes an evaluation of the first path and the further first paths as well as the second paths according to the predefined set of selection rules. The first path, one of the further first paths or one of the second paths is selected by the computing system based on the result of the evaluation. According to several implementations, the predefined set of selection rules comprises at least one safety-related rule and/or at least one driving comfort-related rule.

According to several implementations, a first selection rule of the predefined set of selection rules, in particular of the at least one comfort-related rules, concerns a maximum lateral offset of a respective path to be evaluated.

In other words, the respective path is selected based on the result of the evaluation, only if a total lateral offset or total lateral amplitude of movement of the ego vehicle when following the respective path, is smaller than a predefined maximum offset.

According to several implementations, a second selection rule of the predefined set of selection rules, in particular the at least one safety-related rule, concerns a minimum distance of the ego vehicle from the first vehicle.

In other words, the respective path is selected based on the result of the evaluation, only if a minimum distance between the ego vehicle and the first vehicle can be ensured in case the ego vehicle would be controlled to follow the respective path.

According to several implementations, a third selection rule of the predefined set of selection rules, in particular of the at least safety-related rule, concerns a maximum velocity of the ego vehicle.

In other words, the respective path is selected based on the result of the evaluation, only if a predefined maximum longitudinal velocity of the ego vehicle may be observed in case the ego vehicle is controlled to follow the respective path.

According to several implementations, a fourth selection rule of the predefined set of selection rules, in particular of the at least one safety-related rule, concerns a collision risk of the ego vehicle with an object in the environment.

In particular, the respective path is selected based on the result of the evaluation, only if the respective path does not collide with the object or with a potential or estimated trajectory of the object. The object may for example be a further vehicle or a stationary object in the environment. According to several implementations, a fifth selection rule of the predefined set of selection rules, in particular of the at least one safety-related selection rule, concerns a driving behavior of the first vehicle or a driver of the first vehicle.

In particular, the respective path is selected based on the result of the evaluation, only if an observed trajectory of the first vehicle does not contain a predefined critical maneuver, such as a strong braking maneuver, a strong acceleration or a zigzag driving maneuver.

In order to check whether the respective selection rule is fulfilled, the computing system may, in particular evaluate the sensor data.

According to several implementations, the computing system is used to receive a first set of the sensor data from a first environmental sensor system of the ego vehicle.

In particular, the computing system is used to determine the first path based on the first set of the sensor data.

The more sources for the sensor data are used to determine the path for the ego vehicle, the more possible paths may result and/or the more reliable will the evaluation be. Therefore, road congestion risk is decreased and safety or driving comfort can be increased.

According to several implementations, the first environmental sensor system of the ego vehicle is used to generate the first set of the sensor data.

According to several implementations, the computing system is used to receive a second set of the sensor data from a second environmental sensor system of the first vehicle or of a second vehicle in the environment.

In particular, the computing system is used to determine the first path based on the second set of the sensor data.

In particular, the second set of the sensor data may be received by the computing system from the first or the second vehicle, respectively, via a communication interface, in particular a vehicle-to-vehicle-, V2V-, interface of the ego vehicle. According to several implementations, the computing system is used to receive a third set of the sensor data from a third environmental sensor system of an infrastructure device in the environment.

In particular, the computing system may receive the third set of the sensor data from the infrastructure device via the V2l-interface of the ego vehicle.

The more sources for the sensor data are used to determine the path for the ego vehicle, the more possible paths may result and/or the more reliable will the evaluation be. Therefore, road congestion risk is decreased and safety or driving comfort can be increased.

According to several implementations, the computing system is used to receive availability information concerning an evaluation of the plurality of targets, in particular of each of the plurality of targets or of some of the plurality of targets, via a communication interface of the ego vehicle, in particular via the V2l-interface. The computing system is used to perform the selection of the first path or one of the second paths depending on the availability information.

In particular, the availability information may concern respective information, whether the respective target, in particular gateway, is available or, if applicable, when it will be available.

In this way, it is avoided that the ego vehicle is controlled to follow a path leading the ego vehicle to an unavailable target. In consequence, the risk for road congestion is reduced and the level of safety and driving comfort may be increased.

According to several implementations, the computing system is used to determine the position of each target of the plurality of targets based on map data to determine the second paths.

The map data may include data stored as a digital map, in particular a high-definition digital map, also denoted as HD-map. The digital map can be considered as a database stored on a memory unit of the ego vehicle, in particular of the computing system. The HD-map may, for example, provide information with a resolution in the order of one or few centimeters. In particular, the position of the targets is stored in the map data, in particular in a defined map coordinate system.

The position of the ego vehicle in the map coordinate system may be determined by the computing system depending on the sensor data, GPS-data and/or odometry data.

According to several implementations, the computing system is used to determine a set of potential paths based on the result of the evaluation, to assign a respective priority to each of the potential paths and to select one potential path of the set of potential paths depending on the assigned priorities in order to select the first path or one of the second paths based on the result of the evaluation.

The potential paths correspond, in particular, to a subset of the first and the second paths or to all of the first and the second paths.

Assigning the priorities can be understood as an ordering of the potential path.

In particular, such implementations allow to select a path in a reproducible manner in case more than one path could be selected based on the result of the evaluation.

According to several implementations, the computing system is used to assign the respective priority according to a corresponding risk of road congestion.

In particular, the computing system is used to determine for each of the potential paths a value of one or more predefined parameters affecting the probability of road congestion and to calculate the respective risk of road congestion based on the determined values.

In particular, the respective priority of the corresponding path is the lower the corresponding risk of road congestion is.

In particular, the computing system is used to select the potential path having the lowest risk for road congestion or having the highest priority, respectively.

In this way, the risk for road congestion is decreased.

According to several implementations, the computing system is used to generate an information signal depending on a selected target of the plurality of targets, wherein the selected target corresponds to the selected path. The computing system is used to provide the information signal to the communication system or to another communication system of the ego vehicle, in particular to the V2V- and/or the V2l-interface.

The information signal can be considered as an announcement signal announcing to the infrastructure, the targets and/or further vehicles in the environment, which is the selected target. In this way, other road users may be informed accordingly and the risk of road congestion may be further decreased.

According to the improved concept, also an electronic vehicle guidance system for controlling an ego vehicle in an environment of a plurality of targets at least in part automatically is provided. The vehicle guidance system comprises a computing system, configured to receive sensor data from at least one environmental sensor system, to determine a position of the first vehicle in the environment based on the sensor data and to determine a first path connecting a position of the ego vehicle to the position of the first vehicle. The computing system is configured to determine for each target a respective second path connecting the position of the ego vehicle to a position of the respective target, to evaluate the first path and each of the second paths according to a predefined set of selection rules, to select the first path or one of the second paths based on a result of the evaluation and to control the ego vehicle at least in part automatically to follow the selected path.

An electronic vehicle guidance system may be understood as an electronic system, configured to guide a vehicle fully automatically or fully autonomously and, in particular, without a manual intervention or control by a driver or user of the vehicle being necessary. The vehicle conducts required steering maneuvers, braking maneuvers and/or acceleration maneuvers and so forth automatically. In particular, the electronic vehicle guidance system may implement a fully automatic or fully autonomous driving mode according to level 5 of the SAE J3016 classification. An electronic vehicle guidance system may also be implemented as an advanced driver assistance system, ADAS, assisting a driver for partially automatic or partially autonomous driving. In particular, the electronic vehicle guidance system may implement a partly automatic or partly autonomous driving mode according to levels 1 to 4 of the SAE J3016 classification.

According to several implementations of the electronic vehicle guidance system, the vehicle guidance system comprises a first environmental sensor system of the ego vehicle and the computing system is configured to receive a first set of the sensor data from the first environmental sensor system.

According to several implementations, the vehicle guidance system comprises at least one communication interface and the computing system is configured to receive a second set of the sensor data from a second environmental sensor system of the first vehicle or of a second vehicle in the environment via the at least one communication interface, in particular via a V2V-interface.

According to several implementations, the computing system is configured to receive a third set of the sensor data from a third environmental sensor system of an infrastructure device in the environment via the at least one communication interface, in particular via the V2l-interface.

According to several implementations, the vehicle guidance system comprises a storage medium storing map data.

According to several implementations, the computing system is configured to determine the position of each target of the plurality of targets based on the map data to determine the second paths.

Further implementations of the electronic vehicle guidance system according to the improved concept follow directly from the various implementations of the method according to the improved concept and vice versa. In particular, an electronic vehicle guidance system according to the improved concept may be configured or programmed to perform a method according to the improved concept or the vehicle guidance system performs a method according to the improved concept.

According to the improved concept, also a vehicle comprising an electronic vehicle guidance system according to the improved concept is provided.

According to the improved concept, a computer program comprising instructions is provided. When the instructions are executed by a computer system, in particular by an electronic vehicle guidance system according to the improved concept, for example by the computing system of the electronic vehicle guidance system, the instructions cause the computer system to carry out a method according to the improved concept. According to the improved concept, a computer readable storage medium storing a computer program according to the improved concept is provided.

The computer program as well as the computer readable storage medium can be understood as respective computer program products comprising the instructions.

Further features of the invention are apparent from the claims, the figures and the description of figures. The features and feature combinations mentioned above in the description as well as the features and feature combinations mentioned below in the description of figures and/or shown in the figures alone are usable not only in the respectively specified combination, but also in other combinations without departing from the scope of the invention. Thus, implementations are also to be considered as encompassed and disclosed by the invention, which are not explicitly shown in the figures and explained, but arise from and can be generated by separated feature combinations from the explained implementations. Implementations and feature combinations are also to be considered as disclosed, which do not have all of the features of an originally formulated independent claim. Moreover, implementations and feature combinations are to be considered as disclosed, in particular by the implementations set out above, which extend beyond or deviate from the feature combinations set out in the relations of the claims.

In the figures,

Fig. 1 shows a vehicle with an exemplary implementation of an electronic vehicle guidance system according to the improved concept and a situation according to an exemplary implementation of a method according to the improved concept;

Fig. 2 shows a further situation of the method of Fig. 1 ;

Fig. 3 shows a further situation of the method of Fig. 1 ;

Fig. 4 shows a further situation of the method of Fig. 1 ; Fig. 5 shows a further situation of the method of Fig. 1 ; Fig. 6 shows a further situation of the method of Fig. 1 ; and

Fig. 7 shows a flow diagram of a further exemplary implementation of a method according to the improved concept.

Fig. 1 shows a vehicle 1 with an exemplary implementation of an electronic vehicle guidance system 6 according to the improved concept. The vehicle guidance system 6 comprises a computing system 3, which may contain one or more computing units, for example, electronic control units, ECUs, of the ego vehicle 1 . The vehicle guidance system 6 further comprises at least one communication interface 5 including, for example, a V2V-interface and/or a V2l-interface.

The functionality of the electronic vehicle guidance system 6 is explained in more detail with respect to exemplary implementations of a method for controlling the ego vehicle 1 according to the improved concept.

Various situations of a method according to the improved concept are shown in Fig. 1 to Fig. 6. Fig. 7 shows a block diagram or flow diagram of the method.

According to Fig. 1 to Fig. 6, the ego vehicle 1 is located in an environment of a plurality of targets, which may for example be toll gates 2a, 2b, 2c, 2d, 2e, 2f. Flowever, the following explanations translate directly or analogously to other types of targets.

In particular, the ego vehicle 1 may be about to enter a region between the ego vehicle 1 and the toll gates 2a-2f, where no lane marking are present on the road 7 or only a limited set of lane markings is present, which is not sufficient to perform a path planning for autonomous driving of the ego vehicle 1 based on the lane markings.

In the environment of the toll gates 2a-2f, there may be further vehicles 1a, 1b, 1c, 1 d, 1 e, 1f.

In step S1 of the method, as shown in Fig. 7, the computing system 3 receives multiple sets of sensor data from the environmental sensor system 4 of the ego vehicle 1 and, for example, of further environmental sensor systems 4a, 4b of some or all of the further vehicles 1 a-1 f. The data from the further vehicles 1a, 1b may be received via the V2V- interface of the communication interface 5. Based on all available sensor data, the computing system 3 may for example generate a fused object list. The fused object list may, in particular, contain respective positions of some or all of the further vehicles 1 a- 1 f . In particular, the computing system 3 may determine or plan a plurality of first paths P1 a, P1 b, P1 c, P1 d, P1 e, which connect the present position of the ego vehicle 1 to the respective present positions of the further vehicles 1 a, 1 b, 1 d, 1 e, as the depicted in Fig. 2. The first paths P1 a-P1 e may also be denoted as multi object-based virtual driving lanes. In step S2, the computing system 3 determines the respective positions of each of the gates 2a-2f, for example based on the map data of an HD-map stored in the ego vehicle 1 , in particular in the computing system 3, and/or based on further information received from an infrastructure device 8, such as a radio transmitter device, in particular via the V2l-interface of the ego vehicle 1.

The computing system 3 determines a respective second path P2a, P2b, P2c, P2c, P2d, P2f connecting the present position of the ego vehicle 1 to respective positions of the gates 2a-2f, as can be seen in Fig. 3. The second paths may also be denoted as gate- based virtual lanes.

In step S3, the computing system 3 may optionally receive availability information concerning the respective availability of the gates 2a-2f, in particular via the V2l-interface, for example from the infrastructure device 8. Based on the availability information and, optionally, based on the map data, the computing system 3 may determine, which of the gates 2a-2f are available currently or shortly.

For example, individual gates may be closed, as indicated for the gate 2c in Fig. 4 or blocked by other vehicles, as indicated for the gates 2b and 2e in Fig. 4.

In step S4, the computing system 3 may evaluate the first paths P1a-P1e and the second paths P2a-P2f according to a predefined set of comfort related and/or safety related rules. In other words, the first and second paths P1a-P2e, P2a-P2f are filtered according to the predefined rules. Therein, the predefined rules may, for example, contain rules related to objects surrounding the ego vehicle 1 , in particular to potential collisions with these objects. The rules may also be related to a maximum lateral drift or offset of the ego vehicle 1 , a behavior of one the further vehicles 1 a-1 f or its respective driver, speed limits, safety distances and so forth.

The computing system applies a risk estimation algorithm to the first and second paths P1a-P1e, P2a-P2f depending on the predefined rules.

As a result of the evaluation, a subset of potential paths P1b, P2d, P2f may remain as indicated in Fig. 5.

In step S5, the computing system 3 may order the potential paths P1b, P2d, P2f, in particular according to their respective risk for increasing or generating road congestion.

Based on a result of the ordering, the computing system 3 selects one or more of the potential paths P1b, P2d, P2f having the lowest risk for road congestion.

In optional step S6, the computing system 3 may validate the selected path P2f, as indicated in Fig. 6. To validate the selected path P2f, the computing system 3 may use the map data and/or information related to road boundaries or other limiting conditions, to determine whether following the selected path is actually feasible.

In step S7, the computing system 3 controls the ego vehicle 1 along the selected path P2f, provided the validation, if applicable, of the selected path P2f was successful, to the corresponding selected gate 2f.

As described, the improved concept allows a fully or partly autonomous control of an ego vehicle in an environment even without requiring on lane markings.

In particular, the improved concept reduces the risk for road congestion and/or improves the level of safety or driving comfort.

In various implementations of the improved concept, an improved management of vehicle cross flow through the lanes at a toll station may be achieved. The ego vehicle may be guided in the respective area when there are no road markings and, for example, the overall lane width increases. The improved concept may also take into account unforeseen events that may occur as the ego vehicle crosses through or to the toll gate.

In consequence, the risk for road congestion is reduced.

In particular, according to the improved concept, increasing possibilities of wireless connectivity and data communication technologies implemented in the automotive context may be exploited. For example vehicle-to-vehicle, vehicle-to-device, vehicle-to-pedestrian, vehicle-to-home, vehicle-to-grid and/or vehicle-to-infrastructure communication may be used.

The ability to keep track of the selected path makes it possible to drive fully autonomously around a highway toll gate when there a no highway lines. In particular, once the toll gate is passed, a reversed process may be applied for lane merging to attend the road markings reoccurring. Then, the ego vehicle can continue its route normally.

Several safety and comfort rules may be considered to evaluate the paths. In particular, a maximum lateral drift or gate limitation may be considered. The ego vehicle may be bounded inside two or three lane widths and may not be allowed to travel across more. This may for example be reflected in the shape of the respective trajectories.

Also safety distance and adaption of speed limits may be employed to evaluate the paths. Also collision warning aspects may be included. Trajectories related to rear obstacles with respect to the ego vehicle with a higher relative speed may, for example, not be selected. Also activated turn lights of a further vehicle may be considered. By dropping trajectories with high driving risk, a dynamic driving behavior may be enabled.

In particular, by the improved concept, the best gate or best target may be selected effectively in terms of the lowest risk for road congestion.