Scanning system and method for autonomous driving

Abstract

Method implemented in a computer (105) of a vehicle (101), the method comprising: - Identifying an operational task which is a decision to actuate a throttle valve or a brake to adjust a speed of the vehicle (101) or to actuate a steering system to adjust a steering angle of the vehicle (101); - Acquiring an initial set of data (115) that is assigned to the operational task, wherein the data (115) of the acquired initial set of data (115) are validated by the computer (105) by the computer (105) determining whether the data (115) are within a predefined parameter range limited by threshold values; - Determine whether additional data (115) will be taken into account in connection with the decision; then, after collecting the first set of data (115) and in response to determining whether the additional data (115) will be taken into account, - Selecting a second set of data (115) that is assigned to a second task different from the operational task, wherein the second set of data (115) differs from the first set of data (115) and is useful for making the decision; - Acquisition of the second set of data (115), wherein the data (115) of the acquired second set of data (115) are validated by the computer (105) by the computer (105) determining whether the data (115) are within a predefined parameter range limited by threshold values; - Determine the adjustment of the vehicle's speed (101) or steering angle (101) according to the first set of data (115) and the second set of data (115); and - Controlling the speed of the vehicle (101) depending on the determination by actuating the throttle valve or the brake, or the steering angle of the vehicle (101) by actuating the steering.

Description

[0001] This application claims priority to U.S. Provisional Patent Application Serial No. 61 / 943,590, entitled "AUTONOMOUS DRIVING SENSING SYSTEM AND METHOD," filed February 24, 2014, the contents of which are hereby incorporated by reference in their entirety.

[0002] A vehicle, particularly a vehicle operating autonomously or semi-autonomously, may obtain data regarding environmental conditions through a variety of mechanisms, e.g., sensors or the like, incorporated within the vehicle. Sensor data may provide information regarding environmental conditions, roadsides or lanes on a road, etc., and may be used to develop an appropriate speed for a vehicle, an appropriate path for a vehicle, etc. However, existing vehicle sensor data suffers from limitations regarding the information that can be determined therefrom. For example, a vehicle computer may not be able to utilize data from a variety of sensors regarding a variety of phenomena in a variety of formats, etc.

[0003] DE 10 2014 201 965 A1, DE 10 2013 207 231 A1, DE 10 2010 035 235 A1 and DE 10 2010 004 057 A1 disclose generic methods for operating a vehicle.

[0004] The objective, technical problem to be solved can be seen as eliminating or at least mitigating the disadvantages of the prior art. This problem is solved according to the invention by the subject matter of the independent patent claims. Fig. 1 is a block diagram of an exemplary autonomous sensing system of a vehicle. Fig. 2 is a block diagram of a car lane with sensor markings. Fig. 3 is a schematic diagram of an exemplary process for an autonomous sensing system of a vehicle in an autonomous mode. Fig. 4 is a schematic representation of an example process for determining data for use in implementing an autonomous action in a vehicle.

[0005] Fig. 1 is a block diagram of an exemplary autonomous vehicle system 100 including a vehicle 101 provided with one or more sensor data collectors 110 that collect sensed data 115 relating to an environment in close proximity to the vehicle 101, such as a roadway 155 (in Fig. 2) and areas in close proximity thereto, one or more target objects 160, etc. A computing device 105 in the vehicle 101 generally receives the sensed data 115 and further includes an autonomous driving module 106, e.g., as a set of instructions stored in a memory of the computing device 105 and executable by a processor thereof, whereby some or all of the operations of the vehicle 101 may be performed autonomously or semi-autonomously, i.e., without human control and / or with limited human intervention.

[0006] The collected data 115 includes data regarding a roadway 155 and / or the surrounding environment including one or more targets 160, which data 115 can be used by the computer 105 of the vehicle 101 to make determinations regarding operations of the vehicle 101, including autonomous operations of the vehicle 101. Because the collected data 115 pertains to a variety of different aspects of the environment of a vehicle 101 and / or targets 160 and can include data 115 in a variety of formats for a variety of different sensor data collectors 110, the computer 105 can be further configured to use various mechanisms for integrating or combining various data 115 to make determinations regarding one or more actions within the vehicle 101.

[0007] For example, computer 105 may be programmed to perform autonomous tasks that fall into one or more categories including strategic, tactical, and operational tasks. A strategic task is defined herein as making a decision regarding route planning for vehicle 101, e.g., determining an optimal route to a destination or waypoint, rerouting vehicle 101 due to traffic or road conditions, etc. A tactical task is defined herein as determining a speed of vehicle 101 and / or steering while traveling a route determined for vehicle 101. Accordingly, examples of tactical tasks include performing lane changes, braking, accelerating, maintaining a predetermined distance from one or more surrounding vehicles, etc.An operational task includes controlling the operation of various components of a vehicle 101 to support the implementation of tactical tasks, e.g., determining appropriate lateral or longitudinal steering of a vehicle 101, applying a throttle to increase power to a drivetrain, applying brakes, applying a steering to change a steering angle, etc. Various sensed data 115 may be used to support these different task categories. Advantageously, data typically associated with one task category may be used to support another task category. For example, global positioning system (GPS) data 115 may be available to support the strategic task of route planning for a vehicle 101.However, GPS data 115 could also be used to support tactical tasks, such as determining and adjusting the speed of a vehicle 101.

[0008] A vehicle 101 includes a vehicle computer 105, which generally includes a processor and memory, wherein the memory comprises one or more forms of computer-readable media and stores instructions executable by the processor to perform various operations, including those disclosed herein. For example, the computer 105 generally includes and is capable of executing instructions for selecting an autonomous operating mode of the vehicle 101, adjusting an autonomous operating mode of the vehicle 101, changing an autonomous operating mode of the vehicle 101, etc.

[0009] Furthermore, the computer 105 may include more than one computing device, e.g., controllers or the like, included in the vehicle 101 for monitoring and / or controlling various vehicle components, e.g., an engine control unit (ECU), a transmission control unit (TCU), etc., or may be communicatively coupled thereto, e.g., via a communication bus of the vehicle 101, as described below. The computer 105 is generally configured for communications on a network within the vehicle 101, such as a CAN (Controller Area Network) bus or the like. The computer 105 may also have a connection to an onboard diagnostics (OBD-II) port. Via the CAN bus, the OBD-II, and / or other wired or wireless mechanisms, the computer 105 may send messages to various devices in a vehicle and / or receive messages from the various devices, e.g., controllers, actuators, sensors, etc., including the data collectors 110. Alternatively or additionally, in cases where the computer 105 actually includes multiple devices, the CAN bus or the like may be used for communications between devices, which are depicted in this disclosure as the computer 105. Furthermore, as mentioned below, various controllers and the like, e.g., an ECU, TCU, etc., may provide data 115 to the computer 105 via a network of the vehicle 101, e.g., a CAN bus or the like.

[0010] Additionally, computer 105 may be configured to communicate with one or more remote computers 125 via network 120, which, as described below, may include various wired and / or wireless network technologies, e.g., cellular, Bluetooth, wired and / or wireless packet networks, etc. Further, computer 105, e.g., as part of module 106, generally includes instructions for receiving data, e.g., from one or more data collectors 110 and / or a human-machine interface (HMI), such as an interactive voice response (IVR) system, a graphical user interface (GUI) with a touch screen, or the like.

[0011] As previously mentioned, an autonomous driving module 106 is generally embodied in instructions stored and executed by computer 105. Using data received by computer 105, e.g., collected data 115 from data collectors 110, server 125, etc., module 106 may make various determinations and / or control various components and / or operations of vehicle 101 without a driver to operate vehicle 101. For example, module 106 may be used to control operating behavior, such as speed, acceleration, deceleration, steering, etc., as well as the tactical behavior of the vehicle 101, such as a distance between vehicles and / or a time duration between vehicles, a minimum distance between vehicles when changing lanes, a minimum left turn perpendicular to the direction of travel, a time to arrive at a certain location, a minimum arrival time to an intersection (without a signal) to cross the intersection, etc. In addition, the module 106 can make strategic determinations based on data 115, e.g., a route of the vehicle 101, waypoints on a route, etc.

[0012] The data collectors 110 may include a variety of devices. As previously mentioned, for example, various controllers in a vehicle may function as data collectors 110 to provide sensed data 115, e.g., sensed data 115 related to vehicle speed, acceleration, etc., via the CAN bus. Furthermore, sensors or the like, a GPS (Global Positioning System) device, etc., may be included in a vehicle and configured as data collectors 110 to provide data directly to the computer 105, e.g., via a wired or wireless connection. The data collectors 110 may also include sensors or the like, e.g., medium-range and long-range sensors, for detecting and possibly also obtaining information from targets 160, as described in more detail below, as well as other conditions external to the vehicle 101.For example, sensor data collectors 110 could include mechanisms such as radios, radar, lidar, sound meters, cameras, or other image capture devices that could be used to detect targets 160 and / or obtain other sensed data 115 relevant to the autonomous operation of the vehicle 101, e.g., measuring a distance between the vehicle 101 and other vehicles or objects to detect other vehicles or objects, and / or detect road conditions such as curves, potholes, dips, bumps, grade changes, etc. As yet another example, GPS data 115 could be combined with data from high-resolution 2D and / or 3D digital maps and / or baseline data known as "electronic horizon" data, such data being stored, for example, in a memory of the computer 105.Based on data 115 relating to dead reckoning in a known manner and / or any other known simultaneous localization and mapping (SLAM) and / or localization calculation, possibly using GPS data 115, digital map data 115 may be used as relevant data for computer 105 for use in determining a route for vehicle 101 or assisting a route planner, as well as in other decision-making processes for tactical driving decisions.

[0013] A memory of the computer 105 generally stores the sensed data 115. The sensed data 115 may include a variety of data collected in a vehicle 101 by the data collectors 110, including the data 115 obtained from one or more targets 160. Examples of the sensed data 115 are given above and below, e.g., with respect to the targets 160, and furthermore, the data 115 may also include data calculated therefrom in the computer 105. In general, the sensed data 115 may include any data that can be collected by a sensing device 110 and / or calculated from such data, e.g., values ​​of raw data 115 of the sensors 110, e.g., values ​​of radar and lidar raw data 115, derived data values, e.g., B. a distance of an object 160, which is calculated from radar raw data 115, measurement data values, which e.g.provided by an engine control or other control and / or monitoring system in the vehicle 101. In general, various types of raw data 115 may be collected, e.g., image data 115, data 115 related to reflected light or reflected sound, data 115 indicating an amount of ambient light, a temperature, a speed, an acceleration, a yaw motion, etc.

[0014] Accordingly, the collected data 115 could generally include a variety of data 115 related to operations and / or performance of the vehicle 101, as well as data particularly related to the movement of the vehicle 101. For example, in addition to the data 115 obtained from a target 160, as discussed below, the collected data 115 could include data related to speed, acceleration, braking, lane changes, and / or lane usage (e.g., on certain roads and / or types of roads, such as interstate highways) of a vehicle 101, average distances from other vehicles at respective speeds or speed ranges, and / or data 115 related to the operation of the vehicle 101.

[0015] Additionally, the captured data 115 could be provided by the remote server 125 and / or one or more other vehicles 101, e.g., using vehicle-to-vehicle communications. Various technologies are known for vehicle-to-vehicle communications, including hardware, communication protocols, etc. For example, messages could be sent and received vehicle-to-vehicle according to dedicated short-range communications (DSRC) or the like. DSRCs are known to operate at relatively low power over a short or medium range in spectrum specially allocated by the U.S. government in the 5.9 GHz band. In any event, information in a vehicle-to-vehicle message could include captured data 115, such as a position (e.g., according to geocoordinates such as latitude and longitude), speed, acceleration, deceleration, etc., of a transmitting vehicle 101.Furthermore, a transmitting vehicle 101 could provide other data 115 such as a position, speed, etc. of one or more targets 160.

[0016] Network 120 represents one or more mechanisms by which a vehicle computer 105 can communicate with a remote server 125 and / or a user device 150. Accordingly, network 120 may be one or more of various wired or wireless communication mechanisms, including any desired combination of wired (e.g., cable and fiber) and / or wireless (e.g., cellular, wireless, satellite, microwave, and radio frequency) communication mechanisms, and any desired network topology (or topologies if multiple communication mechanisms are used). Example communication networks include wireless communication networks (e.g., using Bluetooth, IEEE 802.11, etc.), local area networks (LANs), and / or wide area networks (WANs), including the Internet, that provide data communication services.

[0017] Server 125 may be one or more computer servers, each generally including at least one processor and at least one memory, where the memory stores instructions executable by the processor and includes instructions for performing various steps and processes described herein. Server 125 may include or be communicatively coupled to a data store 130 for storing sensed data 115 received from one or more vehicles 101.

[0018] Additionally or alternatively, the server may provide data 115 for use by a vehicle computer 105, e.g., in a module 106. In general, a combination of data 115 from various sources, e.g., the data store 130, via the server 125, other vehicles 101, and / or the data collectors 110 in a vehicle 101, may be synthesized and / or combined to provide the basis for an alert, message, and / or autonomous operation. For example, a first vehicle 101 may detect an object 160 in a roadway 155. The object 160 may be classified as a potential obstacle, but then, as, e.g., the first vehicle 101 approaches the object 160, it may be determined that it has a size, shape, and / or position that does not pose a hazard that impedes the further travel of the first vehicle 101.The first vehicle 101 could then provide data 115 regarding the object 160 to the server 125, which could store this data 115 in the data storage 130. The data 115 regarding the object 160 could then be provided to one or more second vehicles 101 in an area in close proximity to the object 160, whereby the second vehicles 101 could benefit from the data 115 indicating that the object 160 does not, in fact, pose a threat to the second vehicles 101. Accordingly, a vehicle 101 can use its own operating history and / or the history recorded by other vehicles 101 to make determinations regarding autonomous operations.

[0019] User device 150 may be any of a variety of computing devices having a processor and memory, as well as communication capabilities. For example, user device 150 may be a portable computer, a tablet computer, a smartphone, etc., having wireless communication capabilities using IEEE 802.11, Bluetooth, and / or cellular communication protocols. Furthermore, user device 150 may also use these communication capabilities to communicate with a vehicle computer 105 via network 120. User device 150 could communicate with computer 105 or the other mechanisms of vehicle 101, such as a network within vehicle 101, a known protocol such as Bluetooth, etc. Accordingly, user device 150 may be used to perform certain operations attributed herein to data collector 110, e.g.,Voice recognition features, cameras, global positioning system (GPS) features, etc., could be used in a user device 150 to provide data 115 to the computer 105. Furthermore, a user device 150 could be used to provide a human-machine interface (HMI) to the computer 105.

[0020] As in Fig. 2, one or more targets 160 may be in close proximity to a roadway 155. A target or object 160 may be a variety of objects, such as a vehicle 101, a rock, a traffic sign, a bump, a barrier, a lane marking, a street light, a traffic signal, an intersection, etc. In this context, the meaning of "in close proximity" includes riding on, standing on, embedded or fixed in, or arranged on or above a surface of a roadway 155 or a surface near a roadway 155, e.g., a traffic sign or the like on a pole a short distance therefrom, e.g., on, near, or adjacent to a shoulder of the roadway 155, etc. Further, a target 160 could be in close proximity to a roadway 155 by being above the roadway 155, e.g.,suspended on a side or underside of a bridge, on a structure designed to suspend traffic signs above the roadway, etc. In general, in close proximity to a roadway 155 for a target 160 means that the target 160 is located such that the target 160 can be detected by one or more data collectors 110 in a vehicle 101 traveling on the roadway 155 with respect to which the target 160 is in close proximity.

[0021] Fig. 3 is a schematic representation of an exemplary process for an autonomous sensing system of a vehicle 101 in an autonomous mode.

[0022] The process 300 begins at a block 305 in which a vehicle 101 performs autonomous driving operations. That is, the vehicle 101 is operated partially or fully autonomously, i.e., in a manner partially or fully controlled by the autonomous driving module 106, which may be configured to operate the vehicle 101 according to collected data 115. For example, all operations of the vehicle 101, e.g., steering, braking, speed, etc., could be controlled by the module 106 in the computer 105. It is also possible for the vehicle 101 to be operated in a partially or semi-autonomous, i.e., partially manual, manner at block 305, wherein some operations, e.g., braking, could be manually controlled by a driver, while other operations, including, for example, steering, could be controlled by the computer 105. Similarly, the module 106 could control when the vehicle 101 performs a lane change.Furthermore, it is possible that the process 300 could be initiated at a point after the start of driving operations of the vehicle 101, e.g., upon manual triggering by a vehicle occupant through a user interface of the computer 105.

[0023] Next, at block 310, data collectors 110 collect acquired data 115. For example, camera data collectors 110 may collect image data 115, an engine control unit may provide RPM data 115, a speed sensor 110 may provide speed data 115, as well as other types of data, e.g., radar, lidar, acoustic, etc. data 115.

[0024] Next, at block 315, the acquired data 115, which was acquired, for example, within a predetermined period of time, is synchronized in time. That is, one or more data elements of the acquired data 115 may be assigned a same timestamp or the like, indicating that each data element of the data 115 marked in this way represents a same time with respect to the sampled data 115. Further, acquired data 115 may be associated with a location, e.g., geocoordinates in latitude and longitude or the like may be determined by a data collector 110 in the vehicle 101 using the global positioning system (GPS) or the like, and it may be synchronized or associated with other acquired data 115 according to similar locations associated with the acquired data 115.

[0025] Next, the acquired data 115, which has been temporally synchronized, i.e., associated with a specific time or time duration, as previously described with respect to block 315, is then spatially synchronized at block 320. For example, a vehicle 101 may include global positioning system (GPS) data 115, and may additionally determine location information, e.g., a position, speed, acceleration and / or deceleration, etc., of one or more targets 160, possibly including other vehicles 101. For example, the position of a target 160 may be determined using image data 115, radar data 115, lidar data 115, etc. As previously mentioned, a vehicle 101 may further receive data 115 in one or more vehicle-to-vehicle communications from one or more second vehicles 101 and / or from a remote server 125 via the network 120.Such data 115 received from external sources may also be synchronized in time and space and with the data 115 collected in a vehicle 101, as described with respect to blocks 315, 320.

[0026] Next, at block 325, the acquired data 115 may be synchronized with a set of map data. For example, the computer 105 may store in memory map information, each of which is correlated with geocoordinates in an area surrounding a vehicle, and which indicates, for example, streets, landmarks, characteristics of roads such as speed limits, lane counts, directions of travel, existing construction zones, etc. At block 325, this map data is associated with the data 115 having spatial coordinates at or near coordinates indicated by the map data.

[0027] Next, at block 330, the computer 105 performs a data validation step. For example, various data 115 may be examined to determine that the data 115 is within an acceptable range. For example, speed data 115 above a predetermined threshold set at or above expected speeds of a vehicle 101 may be considered invalid.

[0028] Next, at block 335, the computer 105 may determine that at least one strategic, tactical, or operational action is appropriate, such as selecting or changing a route of a vehicle 101, reducing a speed to comply with a speed limit, changing lanes, slowing to an appropriate speed for potential flood conditions, changing a distance between the vehicle 101 and another vehicle, etc. However, in some cases, the computer 105 may determine that no action is required. For example, a target 160 may indicate a change in a speed limit or an unavailability of a particular lane on a roadway if a vehicle 101 travels in an available lane according to the new speed limit, etc. In any event, the computer 105 implements each action determined at block 335.For example, a speed, a distance from other vehicles, a travel lane, etc., may be adjusted, as previously described. As part of determining whether an action is appropriate and / or determining an action to implement, the computer 105 selects a data item or data items of the data 115 to support the action. In some cases, the computer 105 may select a data item of data 115 typically used for a first decision-making category, e.g., strategic determinations, for a second decision-making category, e.g., tactical determinations. A process 400 for determining data 115 for use in determining an autonomous action of the vehicle 101 is described below with reference to FIG. Fig. 4 described.

[0029] At block 340, which follows block 335, the computer 105 determines whether the process 300 should continue. For example, the process 300 may end when autonomous driving operations end and a driver resumes manual control, when the vehicle 101 is shut down, etc. In any event, the process 300 ends after block 340 if the process 300 should not continue. Otherwise, the process 300 proceeds to block 305.

[0030] Fig.4 is a schematic representation of an exemplary process 400 for determining data 115 for use in implementing an autonomous action in a vehicle 101. The process 400 begins at block 405, wherein a computer 105 of a vehicle 101, e.g., according to instructions in an autonomous module 106, identifies a decision to be made regarding a potential autonomous action in the vehicle 101. As previously mentioned, the computer 105 could, for example, identify various strategic, tactical, and / or operational decisions, such as a need to determine a route for the vehicle 101, determine whether the vehicle 101 should change lanes, determine whether a speed of the vehicle 101 should be adjusted, etc.

[0031] Next, at block 410, the computer 105 identifies data 115 for the decision or determination identified at block 405. For example, the computer 105 determines whether data 115 received as relevant to the decision or determination identified at block 410 is appropriate for making the decision or determination, and / or whether data 115 typically provided for a different decision category could be used to supplement data 115 received as relevant to the decision or determination. For example, to determine whether to change lanes, the computer 105 may typically use data 115 from radar and / or lidar sensors 110. However, GPS data 115 could also be used, for example,by providing information usable for upcoming roadway features, such as a narrowing of a roadway, an upcoming construction site, the presence of an entrance or exit ramp that could affect traffic patterns, etc. Furthermore, in addition to determining whether a particular type of data 115 could be used, the computer 105 could also limit the use of particular types of data 115 according to a driving context.

[0032] For example, the use of GPS data 115 to support a tactical operation could be limited to situations in which a vehicle 101 is traveling on a roadway classified as a highway, e.g., a four-lane highway, or the like.

[0033] In summary, at block 410, computer 105 determines whether to include additional data 115 related to a second category in addition to data 115 related to one category of a decision. If no usable data is found in a second category, then process 400 ends after block 410. Otherwise, process 400 proceeds to block 415.

[0034] At block 415, the computer 105 synchronizes and validates the data 115, such as previously described.

[0035] After block 415, the computer 105 implements an action using the identified data 115 at block 420, as previously described. After block 415, the process 400 ends.

[0036] Computing devices, such as those discussed herein, generally each include instructions executable by one or more computing devices, such as those identified above, for performing blocks or steps of the previously described processes. For example, the previously discussed process blocks are implemented as computer-executable instructions.

[0037] Computer-executable instructions may be compiled from or interpreted from computer programs created using a variety of programming languages ​​and / or technologies, including, without limitation, Java™, C, C++, Visual Basic, Java Script, Perl, HTML, etc., either alone or in combination. In general, a processor (e.g., a microprocessor) receives instructions from, e.g., memory, a computer-readable medium, etc., and executes those instructions to thereby perform one or more processes, including one or more of the processes described herein. Such instructions and other data may be stored and transmitted using a variety of computer-readable media. A file in a computing device is generally a collection of data stored in a computer-readable medium, such as a storage medium, random access memory, etc.

[0038] A computer-readable medium includes any medium involved in providing data (e.g., instructions) that can be read by a computer. Such a medium can take many forms, including, but not limited to, non-volatile media, volatile media, and so on. Non-volatile media includes, for example, optical or magnetic disks and other persistent storage. Volatile storage includes dynamic random-access memory (DRAM), which is typically main memory.Common forms of computer-readable media include, for example, a floppy disk, a film disk, a hard disk, a magnetic tape, any other magnetic medium, a CD-ROM, a DVD, any other optical medium, punched cards, punched tape, any other physical medium with hole patterns, a RAM, a PROM, an EPROM, a FLASH-EEPROM, any other memory chip or any other memory cartridge, or any other medium from which a computer can read.

[0039] In the drawings, the same reference numerals indicate the same elements. Furthermore, some or all of these elements could be interchanged. With respect to the media, processes, systems, methods, etc. described herein, it is to be understood that although the steps of these processes, etc., have been described as occurring in a certain ordered sequence, these processes could also be performed such that the described steps are performed in a different order than the order described herein. It is further to be understood that certain steps could be performed concurrently, that other steps could be added, or that certain steps described herein could be omitted.In other words, the descriptions of processes herein are intended to illustrate particular embodiments and are in no way to be construed as limiting the claimed invention.

[0040] Accordingly, it is to be understood that the foregoing description is intended to be illustrative and not restrictive. Upon reading the foregoing description, many other embodiments and applications besides the examples provided will become apparent to those skilled in the art. The scope of the invention should not be determined with reference to the foregoing description, but should instead be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled. It is anticipated that future developments will occur in the arts discussed herein, and it is intended that the disclosed systems and methods be incorporated into such future embodiments. In summary, it is to be understood that the invention is susceptible of modification and variation and is limited only by the following claims.

[0041] All terms used in the claims are intended to be given their common meanings readily understood by those skilled in the art, unless expressly stated otherwise herein. In particular, the use of singular terms such as "a," "a / an," "the," "this," etc., is intended to refer to one or more of the specified elements, unless a claim expressly states otherwise.

Claims

[1] A method implemented in a computer (105) of a vehicle (101), the method comprising: - identifying an operational task which is a decision to operate a throttle or a brake to adjust a speed of the vehicle (101) or to operate a steering to adjust a steering angle of the vehicle (101); - Acquiring a first set of data (115) associated with the operational task, wherein the data (115) of the acquired first set of data (115) are validated by the computer (105) by the computer (105) determining whether the data (115) lie within a predetermined parameter range limited by threshold values; - determining whether additional data (115) are taken into account in connection with the decision; then, after detecting the first set of data (115) and in response to determining whether the additional data (115) are taken into account, - selecting a second set of data (115) associated with a second task different from the operational task, the second set of data (115) different from the first set of data (115) and useful for making the decision; - Acquiring the second set of data (115), wherein the data (115) of the acquired second set of data (115) are validated by the computer (105) by the computer (105) determining whether the data (115) lie within a predetermined parameter range limited by threshold values; - determining the adjustment of the speed of the vehicle (101) or the steering angle of the vehicle (101) according to the first set of data (115) and the second set of data (115); and - controlling the speed of the vehicle (101) in dependence on the determination by actuating the throttle or the brake, or the steering angle of the vehicle (101) by actuating the steering. [2] A system comprising a computer (105) adapted to be installed in a vehicle (101), the computer (105) comprising a processor and a memory (130), the memory (130) storing instructions executable by the processor such that the computer (105) is programmed to: - identifying an operational task which is a decision to operate a throttle or a brake to adjust a speed of the vehicle (101) or to operate a steering to adjust a steering angle of the vehicle (101); - determining whether additional data (115) are to be taken into account in connection with the decision; - Acquiring a first set of data (115) associated with the operational task, wherein the data (115) of the acquired first set of data (115) are validated by the computer (105) by the computer (105) determining whether the data (115) lie within a predetermined parameter range limited by threshold values; then, after acquiring the first set of data (115) and in response to determining whether the additional data (115) are taken into account, - selecting a second set of data (115) associated with a second task different from the operational task, the second set of data (115) different from the first set of data (115) and useful for making the decision; - Acquiring the second set of data (115), wherein the data (115) of the acquired first set of data (115) are validated by the computer (105) by the computer (105) determining whether the data (115) lie within a predetermined parameter range limited by threshold values; - determining the adjustment of the speed of the vehicle (101) or the steering angle of the vehicle (101) according to the first set of data (115) and the second set of data (115); and - controlling the speed of the vehicle (101) in dependence on the determination by actuating the throttle or the brake, or the steering angle of the vehicle (101) by actuating the steering. [3] The method of claim 1 or the system of claim 2, wherein the second set of data (115) includes GPS data. [4] The method of claim 1 or the system of claim 2, wherein the first set of data (115) is derived from data (115) acquired by sensors (110) in the vehicle (101). [5] The method of claim 1 or the system of claim 2, wherein the second task relates to a roadway (155), an object (160) in close proximity to the vehicle (101), or a second vehicle (101). [6] The method of claim 1 or the system of claim 2, wherein the second task is a determination of a vehicle route or a determination of a lane change of the vehicle (101). [7] The method of claim 1 or the system of claim 2, wherein the first set of data (115) or the second set of data (115) includes data (115) from a sensor in the vehicle (101) and a remote server (125), respectively. [8] The method of claim 1 or the system of claim 2, further comprising synchronizing the data (115) using a timestamp and / or geocoordinates. [9] The method of claim 1 or the system of claim 2, further comprising, prior to acquiring the first set of data (115), - determining that the vehicle (101) is traveling on a roadway (155) classified such that the use of the first set of data (115) is permitted. [10] The method of claim 1 or the system of claim 2, wherein the second set of data (115) is derived from sensors (110) in one or more second vehicles (101).

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